Claude Opus 5 is avail­able to­day. It’s a thought­ful and proac­tive model that comes close to the fron­tier in­tel­li­gence of Claude Fable 5 at half the price.

On cod­ing and knowl­edge work eval­u­a­tions like Frontier-Bench and GDPval-AA, Opus 5 is the new state-of-the-art, though it re­mains be­hind Mythos 5 on cy­ber­se­cu­rity tasks.

Opus 5 is de­signed to be used every day: it works more ef­fi­ciently than other mod­els. It’s the new de­fault model on Claude Max, and the strongest model on Claude Pro.

Performance and cost-ef­fec­tive­ness

Claude Opus 5 pro­vides greatly im­proved per­for­mance for the same cost as its pre­de­ces­sor, Opus 4.8. The charts in this sec­tion show how per­for­mance changes ac­cord­ing to the mod­el’s ef­fort set­ting, which cus­tomers can use to op­ti­mize for in­tel­li­gence or con­serve to­kens for faster and cheaper re­sults.

Opus 5 ex­cels on valu­able soft­ware en­gi­neer­ing tasks. For ex­am­ple, on Frontier-Bench v0.1, Opus 5 sur­passes all other mod­els, and more than dou­bles Opus 4.8’s per­for­mance at a lower cost per task. On CursorBench 3.2, at max ef­fort, the model per­forms within 0.5% of Fable 5’s peak score, but at half the cost per task; it also achieves greater per­for­mance at a given cost than all other mod­els on high, xhigh, and max ef­fort.

We see sim­i­lar re­sults on knowl­edge work and prob­lem-solv­ing tasks. For ex­am­ple:

On ARC-AGI 3, an eval­u­a­tion where the model has to solve novel prob­lems, Opus 5’s score is three times as high as the next-best model.

On Zapier AutomationBench, which mea­sures whether mod­els can com­plete busi­ness tasks from start to fin­ish, Opus 5’s pass rate is around 1.5× the next-best model for the same cost per task. Even at its low­est ef­fort set­ting, Opus 5 passes more tasks than any other model.

On OSWorld 2.0, a com­puter use bench­mark, Opus 5 out­per­forms every other model at any given cost, sur­pass­ing Fable 5’s best re­sult at just over a third of the cost.

It’s also our best and most cost-ef­fi­cient model on sev­eral re­lated eval­u­a­tions:

Opus 5 is a mean­ing­ful im­prove­ment over Opus 4.8 for sci­en­tific re­search. It shows bet­ter per­for­mance than Opus 4.8 on every one of our life sci­ences eval­u­a­tions, which cover top­ics in­clud­ing struc­tural bi­ol­ogy, or­ganic chem­istry, and bioin­for­mat­ics. Its im­prove­ments are most no­table on or­ganic chem­istry tasks, like in­fer­ring mol­e­c­u­lar struc­tures from spec­troscopy data (it scores 10.2 per­cent­age points higher than Opus 4.8 on our in­ter­nal bench­mark), and on pro­tein-re­lated tasks like pre­dict­ing how vari­a­tions in a pro­tein’s se­quence af­fect how it func­tions (here, it scores 7.7 per­cent­age points higher).

Finally, Opus 5 is ca­pa­ble of pro­duc­ing much stronger vi­sual out­puts:

Working with Claude Opus 5

Claude Opus 5 is much stronger at ver­i­fy­ing its work and it­er­at­ing care­fully un­til it suc­ceeds. In eval­u­a­tions and early-ac­cess test­ing, we and our users found many ex­am­ples of Opus 5’s agency and thor­ough­ness:

On one Frontier-Bench task, Opus 5 was given a draw­ing of a ma­chine part and asked to write code to re­build it as a 3D FreeCAD model. However, in this task, the model was in­ten­tion­ally given no way to di­rectly view the draw­ing. Opus 5 re­sponded by writ­ing its own com­puter vi­sion pipeline to pull the geom­e­try from the raw pix­els, then re­con­structed the full ma­chine part. It suc­ceeded in do­ing so re­peat­edly; no com­pet­ing model with the same setup could solve it af­ter five at­tempts.

Given a real bug in a pop­u­lar open-source pack­age man­ager, Opus 5 found the root cause and fixed an edge case that the com­mu­ni­ty’s patch had missed. A com­pet­ing model fixed only the sur­face symp­tom (not the un­der­ly­ing cause), then re­ported the bug re­solved.

An en­gi­neer at a trad­ing firm used Opus 5 to build a mar­ket data feed for a new ex­change in a sin­gle ses­sion. Previous mod­els could not com­plete this task at all, even given ex­ten­sive plans from the en­gi­neer. Finding no live feed to val­i­date against, Opus 5 even built its own test har­ness to check that its code parsed the ex­change’s data cor­rectly.

Below are fur­ther re­ports from our early-ac­cess cus­tomers on their ex­pe­ri­ence of work­ing with Opus 5:

On FrontierCode 1.1, Claude Opus 5 ap­proaches Fable-level per­for­mance at half the cost. Within Devin, it also shows par­tic­u­lar strength on dif­fi­cult de­bug­ging and root-cause analy­sis tasks.

On FrontierCode 1.1, Claude Opus 5 ap­proaches Fable-level per­for­mance at half the cost. Within Devin, it also shows par­tic­u­lar strength on dif­fi­cult de­bug­ging and root-cause analy­sis tasks.

Claude Opus 5 de­liv­ers near Fable 5 in­tel­li­gence at Opus speed and cost. On CursorBench it’s just un­der Fable 5 and has many of the same be­hav­iors. We are ex­cited to see how de­vel­op­ers use it in Cursor.

Claude Opus 5 de­liv­ers near Fable 5 in­tel­li­gence at Opus speed and cost. On CursorBench it’s just un­der Fable 5 and has many of the same be­hav­iors. We are ex­cited to see how de­vel­op­ers use it in Cursor.

Claude Opus 5 topped Zapier’s AutomationBench leader­board with­out spend­ing more to­kens than prior Claude mod­els. It took a raw ac­count-health work­book and ran a full churn-pre­ven­tion se­quence end to end: flag­ging at-risk ac­counts, alert­ing the right owner, and sum­ma­riz­ing for re­ten­tion ops. Previous mod­els did­n’t pass; Opus 5 hit 100%.

Claude Opus 5 topped Zapier’s AutomationBench leader­board with­out spend­ing more to­kens than prior Claude mod­els. It took a raw ac­count-health work­book and ran a full churn-pre­ven­tion se­quence end to end: flag­ging at-risk ac­counts, alert­ing the right owner, and sum­ma­riz­ing for re­ten­tion ops. Previous mod­els did­n’t pass; Opus 5 hit 100%.

On our ge­nomics analy­sis work, Claude Opus 5 be­haves more like a care­ful sci­en­tist than any model we’ve run. It reaches for the right sta­tis­ti­cal tests to rule out con­founders, cross-checks its own re­sults by in­de­pen­dent meth­ods, and stays on track through long multi-step analy­ses.

On our ge­nomics analy­sis work, Claude Opus 5 be­haves more like a care­ful sci­en­tist than any model we’ve run. It reaches for the right sta­tis­ti­cal tests to rule out con­founders, cross-checks its own re­sults by in­de­pen­dent meth­ods, and stays on track through long multi-step analy­ses.

Claude Opus 5 came out ahead of every model in its fam­ily on our in­ter­nal evals. It is­n’t just bet­ter on our hard­est agen­tic cod­ing tasks, up 22% over Opus 4.7, it’s stead­ier, with far less vari­ance run to run. For the mil­lions of builders on Lovable, that con­sis­tency is the whole game. Reliable re­sults, build af­ter build.

Claude Opus 5 came out ahead of every model in its fam­ily on our in­ter­nal evals. It is­n’t just bet­ter on our hard­est agen­tic cod­ing tasks, up 22% over Opus 4.7, it’s stead­ier, with far less vari­ance run to run. For the mil­lions of builders on Lovable, that con­sis­tency is the whole game. Reliable re­sults, build af­ter build.

Claude Opus 5 is the biggest leap in the Opus fam­ily since 4.5. On the same full-stack app builds, the front end shows it first: the best an­i­ma­tions, games, and 3D work we have seen from an Opus model.

Claude Opus 5 is the biggest leap in the Opus fam­ily since 4.5. On the same full-stack app builds, the front end shows it first: the best an­i­ma­tions, games, and 3D work we have seen from an Opus model.

We’re lov­ing Claude Opus 5. For the kind of open-ended an­a­lyt­i­cal work our agent han­dles, it’s a strict up­grade over Opus 4.8, and the gains are biggest ex­actly where it mat­ters: the harder, vaguer tasks. Responses are clearer and more con­cise, and we see im­proved ef­fi­ciency at higher ef­fort lev­els too.

We’re lov­ing Claude Opus 5. For the kind of open-ended an­a­lyt­i­cal work our agent han­dles, it’s a strict up­grade over Opus 4.8, and the gains are biggest ex­actly where it mat­ters: the harder, vaguer tasks. Responses are clearer and more con­cise, and we see im­proved ef­fi­ciency at higher ef­fort lev­els too.

Claude Opus 5 is a strik­ing im­prove­ment over Opus 4.8 for the fi­nan­cial re­search work­flows our an­a­lysts run every day. It stands out on nu­mer­i­cal rea­son­ing, table work, and sharper crit­i­cal think­ing where pre­ci­sion mat­ters.

Claude Opus 5 is a strik­ing im­prove­ment over Opus 4.8 for the fi­nan­cial re­search work­flows our an­a­lysts run every day. It stands out on nu­mer­i­cal rea­son­ing, table work, and sharper crit­i­cal think­ing where pre­ci­sion mat­ters.

Claude Opus 5 de­liv­ers the in­dus­try in­tel­li­gence and ac­cu­racy that is es­sen­tial for the analy­sis of spe­cial­ized en­ter­prise con­tent. Box found that Opus 5 out­per­forms Opus 4.8 by 8% and de­liv­ers no­table per­for­mance gains in the data analy­sis (11% im­prove­ment) and due dili­gence (17% im­prove­ment) work­flows that tech­nol­ogy, health­care, and pub­lic sec­tor or­ga­ni­za­tions rely on daily.

Claude Opus 5 de­liv­ers the in­dus­try in­tel­li­gence and ac­cu­racy that is es­sen­tial for the analy­sis of spe­cial­ized en­ter­prise con­tent. Box found that Opus 5 out­per­forms Opus 4.8 by 8% and de­liv­ers no­table per­for­mance gains in the data analy­sis (11% im­prove­ment) and due dili­gence (17% im­prove­ment) work­flows that tech­nol­ogy, health­care, and pub­lic sec­tor or­ga­ni­za­tions rely on daily.

Claude Opus 5 is a clear gen­er­a­tional step up from Opus 4.8. Over one week­end I gave it a chief-of-staff role over my dev en­vi­ron­ments: it built its own mon­i­tor, drove each box, and pulled me in only for the judg­ment calls.

Claude Opus 5 is a clear gen­er­a­tional step up from Opus 4.8. Over one week­end I gave it a chief-of-staff role over my dev en­vi­ron­ments: it built its own mon­i­tor, drove each box, and pulled me in only for the judg­ment calls.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant code­base, adapt­ing to feed­back through­out an agen­tic work­flow and ex­plain­ing its rea­son­ing more clearly than any model we’ve used. It han­dled work we would nor­mally have bro­ken into much smaller pieces.

Claude Opus 5 made large scale changes across our Fundamental Research Assistant code­base, adapt­ing to feed­back through­out an agen­tic work­flow and ex­plain­ing its rea­son­ing more clearly than any model we’ve used. It han­dled work we would nor­mally have bro­ken into much smaller pieces.

On some of our hard­est fi­nan­cial-mod­el­ing tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both ac­cu­racy and ef­fi­ciency. Its per­for­mance floor is ma­te­ri­ally higher, es­pe­cially on deep fi­nance do­main logic. Across ef­fort lev­els it av­er­aged 9 per­cent­age points higher ac­cu­racy with a third fewer turns and tool calls and 60% less time.

On some of our hard­est fi­nan­cial-mod­el­ing tasks, Claude Opus 5 is a clear step up from Opus 4.8 in both ac­cu­racy and ef­fi­ciency. Its per­for­mance floor is ma­te­ri­ally higher, es­pe­cially on deep fi­nance do­main logic. Across ef­fort lev­els it av­er­aged 9 per­cent­age points higher ac­cu­racy with a third fewer turns and tool calls and 60% less time.

Claude Opus 5 checks its own work the way a real fron­tend de­vel­oper would. On our bench­mark it opened its pages in a browser at desk­top and phone widths, caught a prod­uct hid­den be­low the mo­bile fold and an off-screen check­out but­ton, and fixed both be­fore hand­ing the work back.

Claude Opus 5 checks its own work the way a real fron­tend de­vel­oper would. On our bench­mark it opened its pages in a browser at desk­top and phone widths, caught a prod­uct hid­den be­low the mo­bile fold and an off-screen check­out but­ton, and fixed both be­fore hand­ing the work back.

Claude Opus 5 is a clear step up in per­for­mance on le­gal agent work com­pared to prior Opus mod­els, and we saw the biggest gains in prac­tice ar­eas like cor­po­rate gov­er­nance and ar­bi­tra­tion. We were also im­pressed with Opus 5’s abil­ity to main­tain qual­ity at lower rea­son­ing lev­els, achiev­ing sim­i­lar per­for­mance while gen­er­at­ing 26% fewer to­kens on av­er­age com­pared to Opus 4.8 at max rea­son­ing.

Claude Opus 5 is a clear step up in per­for­mance on le­gal agent work com­pared to prior Opus mod­els, and we saw the biggest gains in prac­tice ar­eas like cor­po­rate gov­er­nance and ar­bi­tra­tion. We were also im­pressed with Opus 5’s abil­ity to main­tain qual­ity at lower rea­son­ing lev­els, achiev­ing sim­i­lar per­for­mance while gen­er­at­ing 26% fewer to­kens on av­er­age com­pared to Opus 4.8 at max rea­son­ing.

Claude Opus 5’s biggest gains for us are on longer-hori­zon work: build­ing a full deck, then re­vis­ing it. Artifact qual­ity is what de­cides which model we ship, and this is the clear­est step up we’ve seen — bet­ter vi­sual un­der­stand­ing, cleaner for­mat­ting, fewer slide is­sues.

Claude Opus 5’s biggest gains for us are on longer-hori­zon work: build­ing a full deck, then re­vis­ing it. Artifact qual­ity is what de­cides which model we ship, and this is the clear­est step up we’ve seen — bet­ter vi­sual un­der­stand­ing, cleaner for­mat­ting, fewer slide is­sues.

Claude Opus 5’s judg­ment is what stands out. Handing off a PR, it does­n’t rush to pub­lish: it ver­i­fies the branches, checks the tem­plate, and thinks through test im­pli­ca­tions so the hand­off is clean. The older mod­els tended to jump ahead and get caught on our checks.

Claude Opus 5’s judg­ment is what stands out. Handing off a PR, it does­n’t rush to pub­lish: it ver­i­fies the branches, checks the tem­plate, and thinks through test im­pli­ca­tions so the hand­off is clean. The older mod­els tended to jump ahead and get caught on our checks.

During a rearchi­tect­ing ses­sion, Claude Opus 5 pushed back on a de­sign I pro­posed, and it did­n’t fold when I in­sisted. Instead, it ex­plained ex­actly what was valu­able in my idea, nar­rowed its ob­jec­tion to a sin­gle de­sign ques­tion, and pro­posed a com­pro­mise that kept the good part while fix­ing the flaw. That’s the kind of judg­ment that lets us trust it with less over­sight.

During a rearchi­tect­ing ses­sion, Claude Opus 5 pushed back on a de­sign I pro­posed, and it did­n’t fold when I in­sisted. Instead, it ex­plained ex­actly what was valu­able in my idea, nar­rowed its ob­jec­tion to a sin­gle de­sign ques­tion, and pro­posed a com­pro­mise that kept the good part while fix­ing the flaw. That’s the kind of judg­ment that lets us trust it with less over­sight.

On first-turn red­lines, Claude Opus 5 scored the high­est of any model we tested, nearly dou­ble Opus 4.8. Commenting is bet­ter too: on NDAs it gets to the red­line in less time and with fewer passes, with ac­cu­racy main­tained or bet­ter.

On first-turn red­lines, Claude Opus 5 scored the high­est of any model we tested, nearly dou­ble Opus 4.8. Commenting is bet­ter too: on NDAs it gets to the red­line in less time and with fewer passes, with ac­cu­racy main­tained or bet­ter.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger haz­ard spot­ter on sub­tle, code­base-spe­cific is­sues. We’re adopt­ing it for pro­duc­tion work­loads.

Claude Opus 5 writes clean, tight diffs with no dead code, and it’s the stronger haz­ard spot­ter on sub­tle, code­base-spe­cific is­sues. We’re adopt­ing it for pro­duc­tion work­loads.

We will def­i­nitely mi­grate a num­ber of use cases in Cosmos, our uni­fied agent plat­form. We’re look­ing for­ward to in­creas­ingly us­ing Claude Opus 5 for code re­view, and I am con­fi­dent in say­ing we would rather peo­ple be us­ing Opus 5 than Opus 4.8.

We will def­i­nitely mi­grate a num­ber of use cases in Cosmos, our uni­fied agent plat­form. We’re look­ing for­ward to in­creas­ingly us­ing Claude Opus 5 for code re­view, and I am con­fi­dent in say­ing we would rather peo­ple be us­ing Opus 5 than Opus 4.8.

What stands out about Claude Opus 5 is judg­ment. It thinks harder be­fore it writes a sin­gle line, catches its own log­i­cal faults dur­ing plan­ning rather than af­ter the fact, and rea­sons about why an an­swer is right, not just whether it works. It’s the clear­est jump in prob­lem-solv­ing we’ve seen from one Claude model to the next, and we’re look­ing for­ward to see­ing it adopted in JetBrains IDEs.

What stands out about Claude Opus 5 is judg­ment. It thinks harder be­fore it writes a sin­gle line, catches its own log­i­cal faults dur­ing plan­ning rather than af­ter the fact, and rea­sons about why an an­swer is right, not just whether it works. It’s the clear­est jump in prob­lem-solv­ing we’ve seen from one Claude model to the next, and we’re look­ing for­ward to see­ing it adopted in JetBrains IDEs.

Claude Opus 5 is the strongest Opus model we’ve tested on our trad­ing bench­mark, and it gets there us­ing roughly a sev­enth of the rea­son­ing to­kens and un­der half the la­tency of Opus 4.8. Better an­swers at a frac­tion of the com­pute.

Claude Opus 5 is the strongest Opus model we’ve tested on our trad­ing bench­mark, and it gets there us­ing roughly a sev­enth of the rea­son­ing to­kens and un­der half the la­tency of Opus 4.8. Better an­swers at a frac­tion of the com­pute.

Claude Opus 5 lets mon­i­tor­ing agents man­age parts of their own mem­ory in pro­duc­tion, mak­ing them more au­tonomous and re­li­able over longer hori­zons. The agent treats its con­text as a liv­ing doc­u­ment: af­ter flag­ging a po­ten­tial anom­aly in one of our ser­vices, it re-checked its own as­sump­tion against pro­duc­tion, found the sig­nal was be­nign, wrote the cor­rec­tion into its mem­ory, and re­tired its mon­i­tor­ing queries on its own.

Claude Opus 5 lets mon­i­tor­ing agents man­age parts of their own mem­ory in pro­duc­tion, mak­ing them more au­tonomous and re­li­able over longer hori­zons. The agent treats its con­text as a liv­ing doc­u­ment: af­ter flag­ging a po­ten­tial anom­aly in one of our ser­vices, it re-checked its own as­sump­tion against pro­duc­tion, found the sig­nal was be­nign, wrote the cor­rec­tion into its mem­ory, and re­tired its mon­i­tor­ing queries on its own.

Claude Opus 5 is a strong agen­tic cod­ing model built for long-run­ning, multi-step work. It deeply un­der­stands your code­base, holds the thread across com­plex tasks, and pins down re­quire­ments for fea­ture de­vel­op­ment and bug-fix­ing more ef­fec­tively than Opus 4.8. Developers can now build with Opus 5 in Kiro, ac­cess­ing its ad­vanced ca­pa­bil­i­ties to tackle am­bi­tious pro­jects.

Claude Opus 5 is a strong agen­tic cod­ing model built for long-run­ning, multi-step work. It deeply un­der­stands your code­base, holds the thread across com­plex tasks, and pins down re­quire­ments for fea­ture de­vel­op­ment and bug-fix­ing more ef­fec­tively than Opus 4.8. Developers can now build with Opus 5 in Kiro, ac­cess­ing its ad­vanced ca­pa­bil­i­ties to tackle am­bi­tious pro­jects.

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Alignment and safety

Alignment. During pre-de­ploy­ment test­ing, our au­to­mated be­hav­ioral au­dit found Opus 5 to be our most aligned model to date (as shown in the graph be­low). It ad­heres to Claude’s Constitution bet­ter than Opus 4.8, Sonnet 5, or Fable 5; ex­hibits the low­est rates of de­cep­tive be­hav­ior; and is the least sus­cep­ti­ble to be­ing tricked into mis­use. It’s also our safest model yet in terms of avoid­ing reck­less ac­tions that could have hard-to-re­verse side ef­fects.

Safety. Opus 5 does not ad­vance the fron­tier in risky, dual-use ca­pa­bil­i­ties. In rig­or­ous eval­u­a­tions con­ducted along­side pri­vate-sec­tor and gov­ern­ment part­ners, we found it re­mains be­hind Mythos 5 in both bi­ol­ogy re­search and of­fen­sive cy­ber­se­cu­rity. More in­for­ma­tion about these eval­u­a­tions can be found in our System Card.

As with its pre­de­ces­sor, Opus 4.8, we’ve in­ten­tion­ally avoided train­ing Opus 5 on cy­ber tasks. The model has nev­er­the­less im­proved sub­stan­tially on these tasks as a re­sult of be­com­ing more gen­er­ally ca­pa­ble, and it comes close to Mythos 5 at find­ing cy­ber­se­cu­rity vul­ner­a­bil­i­ties. However, it re­mains sub­stan­tially be­hind Mythos 5 on the ex­ploita­tion of those vul­ner­a­bil­i­ties—that is, in turn­ing vul­ner­a­bil­i­ties into ma­te­r­ial cy­ber threats.

This is il­lus­trated by Opus 5’s per­for­mance on OSS-Fuzz, an eval­u­a­tion we’ve de­vel­oped to as­sess how well mod­els can find and then ex­ploit vul­ner­a­bil­i­ties with­out ex­ten­sive hu­man guid­ance. Although Mythos 5 and Opus 5 iden­tify vul­ner­a­bil­i­ties with sim­i­lar suc­cess, Opus 5’s score on the de­vel­op­ment of ex­ploits is far be­hind that of Mythos 5.

Safeguards for Opus 5

Claude Opus 5’s safe­guards are de­signed to al­low ben­e­fi­cial uses of the model in both cy­ber­se­cu­rity and bi­ol­ogy. They are sim­i­lar to those we ap­plied to Opus 4.8, with the ex­cep­tion of some stronger guardrails on a nar­row range of cy­ber tasks.

Cybersecurity. Opus 5’s cy­ber clas­si­fiers are pro­por­tion­ally less re­stric­tive than those on Fable 5. They al­low Opus 5 to find vul­ner­a­bil­i­ties in source code, but block “binary-based” vul­ner­a­bil­ity scan­ning (a method more likely to be as­so­ci­ated with ma­li­cious ac­tors), pen­e­tra­tion test­ing, and ex­ploit gen­er­a­tion.

Based on our test­ing, we ex­pect the clas­si­fiers to in­ter­vene around 85% less of­ten than they do for Fable 5. In Claude.ai, Claude Code, and Claude Cowork, any flagged re­quests will fall back to Opus 4.8 by de­fault. Fallbacks to Opus 4.8 can also be en­abled on the API.

Our Cyber Verification Program (CVP) fa­cil­i­tates cy­ber­se­cu­rity work that would oth­er­wise be im­peded by the mod­el’s safe­guards. Enterprises and re­searchers who are al­ready part of the CVP have im­me­di­ate ac­cess to a ver­sion of Opus 5 with fewer se­cu­rity re­stric­tions.

Biology. Since Opus 5 has a sim­i­lar suite of safe­guards to Opus 4.8, it is now our most ca­pa­ble gen­er­ally avail­able model for sci­en­tific re­search. Nevertheless, the model still shows im­por­tant lim­i­ta­tions on long-run­ning, au­tonomous re­search tasks, which is where we ex­pect AI mod­els to pose the most sub­stan­tial bi­ol­ogy-re­lated risks. (Mythos 5 re­mains the stronger model for this type of bi­o­log­i­cal work.) As part of this launch, bi­ol­ogy-re­lated re­quests that are blocked on Fable 5 will now route to Opus 5 rather than Opus 4.8.

Getting started

Claude Opus 5 is avail­able to­day on all plat­forms, priced at $5 per mil­lion in­put to­kens and $25 per mil­lion out­put to­kens (the same as Opus 4.8). Developers can get started with claude-opus-5 on the Claude API.

It’s also of­fered in Fast mode, where it runs around 2.5 times the de­fault speed. As with Opus 4.8, Fast mode is avail­able at twice Opus 5’s base price on the Claude Platform and through us­age cred­its in Claude Code.

Alongside Opus 5, we’re re­leas­ing two up­dates in beta:

Mid-conversation tool changes on the Claude Platform. Within a con­ver­sa­tion, de­vel­op­ers can now change which tools Claude can use with­out in­val­i­dat­ing the prompt cache.

Automatic fall­backs on the API. Users can now choose to have re­quests that are flagged by our safety clas­si­fiers on Opus 5 (or Fable 5) au­to­mat­i­cally route to an­other model. With au­to­matic fall­backs on, API re­quests al­ways route to the best avail­able model by de­fault rather than be­ing blocked.

Consistent with prior Opus mod­els, Opus 5 does not have data re­ten­tion re­quire­ments for gen­eral ac­cess.

For more guid­ance on how to get the best out of Opus 5, see our prompt­ing guide.

Footnotes

Frontier-Bench v0.1, Effort plot: These re­sults are from an in­ter­nal run of Frontier-Bench v0.1, on the mini-SWE-agent har­ness and a GKE back­end, mean re­ward over 5 at­tempts per task. Opus 4.8 served as fall­back on safety-clas­si­fier re­fusals for Opus 5 and Fable 5.

Related con­tent

A re­search agenda for the Economic Futures Research Fund

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Ask Claude about the Anthropic Economic Index

We’re launch­ing the Anthropic Economic Index con­nec­tor for Claude, which lets any­one ex­plore real data about AI and work.

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Anthropic is do­nat­ing an­other $20 mil­lion to Public First Action

Anthropic is con­tribut­ing an ad­di­tional $20 mil­lion to Public First Action, bring­ing our to­tal sup­port to $40 mil­lion.

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Because I am known to write us­ing a foun­tain pen on pa­per, a num­ber of peo­ple have pointed me to this post and its un­der­ly­ing re­search. I won’t re­hash what is said in those sources, but the gist of it is that when you write things down by hand you’re re­cruit­ing more of your brain, which is a good thing.

I’m not an ex­pert on how the brain works, but I can say that, when writ­ing by hand, one is con­tin­u­ally solv­ing a se­ries of small prob­lems hav­ing to do with the spac­ing of words, how let­ters are con­nected, the cross­ing of the let­ter t (sometimes more than one in the same word) and the dot­ting of the let­ters i and j, and how to ac­com­plish all of those things through co­or­di­nated move­ments not just of the fin­gers but of the whole arm. All of that has to be in­te­grated in real time with what­ever is hap­pen­ing on a more ab­stract level in the brain’s pro­cess­ing of ideas and im­agery.

Concurrently I have been fol­low­ing dis­course on Reddit and other sources about how wide­spread use of AI has forced ed­u­ca­tors to re­turn to the long-aban­doned prac­tice of hav­ing their stu­dents take ex­ams in per­son by writ­ing things out long­hand in blue books. This has cre­ated new chal­lenges for stu­dents who never re­ally learned how to write by hand, and for teach­ers who can’t make sense of their stu­dents’ ter­ri­ble hand­writ­ing.

About twenty-five years ago I stopped com­pos­ing at the key­board and switched over to foun­tain pen on pa­per. Since then I have writ­ten many thou­sands of pages that way. The man­u­script of The Baroque Cycle was a stack of hand­writ­ten pages 42 inches high, which for a time was on dis­play at the Museum of Science Fiction in Seattle. With the ex­cep­tion of The Rise and Fall of D.O.D.O., which I co-wrote with Nicole Galland by email­ing Word files back and forth, every book I’ve writ­ten since then has been com­posed with foun­tain pen on pa­per.

Every so of­ten, when I’m sign­ing books at a book tour ap­pear­ance, some­one will come up to me and say some­thing like “you must have writer’s cramp!” or “is your hand sore yet?” I never have the time to pro­vide a full an­swer. If I did, how­ever, my an­swer would be that never, at any time dur­ing a quar­ter of a cen­tury dur­ing which I have spent a sub­stan­tial frac­tion of each work­ing day writ­ing by hand, have I ex­pe­ri­enced even the faintest traces of so-called “writer’s cramp” or any other such hob­gob­lins.

Yet I can re­mem­ber get­ting a sore hand when I was a kid writ­ing out as­sign­ments in school. Many peo­ple prob­a­bly re­mem­ber such ex­pe­ri­ences and as­sume, rea­son­ably enough, that it’s a nat­ural con­se­quence of writ­ing by hand for any length of time. This is not the case.

Here are some fairly sim­ple dos and don’ts for peo­ple who want to reap the ben­e­fits of writ­ing by hand.

It’s pretty ob­vi­ous that you’re go­ing to get tired faster if your mus­cles have to ex­ert more force. Writing with a pen­cil re­quires sig­nif­i­cantly more force than writ­ing with a good pen. Old-school ball­points with thick ink are no bet­ter. You can see vi­sual ev­i­dence of this if you flip over a sheet of pa­per on which you’ve been writ­ing with a pen­cil or an old ball­point. The pa­per will bear a vis­i­ble im­print where it was pressed down by the writ­ing in­stru­ment. Often that will con­tinue down into the stack of pa­per be­neath. That’s be­cause you had to push hard. This does­n’t hap­pen with a foun­tain pen. If the nib is work­ing prop­erly you need to ex­ert very lit­tle force. The nib is ba­si­cally skat­ing on the lit­tle lake of ink that it has just laid down.

Pains me to say it, but roller­ball gel pens are about as good as foun­tain pens on this front.

It might then seem rea­son­able to think that writ­ing with a sty­lus on an iPad or sim­i­lar would be best, since no force is needed and fric­tion is min­i­mized. I don’t think this is true. A small amount of fric­tion is ac­tu­ally de­sir­able. You don’t want the tip of the writ­ing in­stru­ment to skid out of con­trol. Your brain and your lit­tle hand mus­cles are re­ly­ing on a lit­tle bit of fric­tion. Since I’m writ­ing this dur­ing the World Cup, I’ll make a soc­cer anal­ogy. Soccer play­ers have spent many hours drib­bling balls across play­ing fields, and they’ve in­ter­nal­ized the physics—they know about how far the ball is go­ing to travel when they kick it a cer­tain way, and how of­ten they need to give it an­other kick to keep it mov­ing. If you put them on a gi­ant, fric­tion­less air hockey table, all of that knowl­edge would be­come use­less. Every touch on the ball would send it out of con­trol. Dribbling the ball down the field would be­come more tir­ing be­cause they’d have to be mak­ing con­tin­ual ef­forts to con­trol the bal­l’s move­ment. Relying on a lit­tle bit of fric­tion re­duces the amount of men­tal and phys­i­cal ef­fort.

The com­bi­na­tion of foun­tain pens and pa­per em­bod­ies a bal­ance that has been worked out over a long span of time by peo­ple who write a lot. This phe­nom­e­non is called “tooth” by afi­ciona­dos. Removing fric­tion by us­ing a hard sty­lus on glass will ac­tu­ally make the process more tir­ing.

Too much fric­tion, and too lit­tle fric­tion, are both more tir­ing than just a lit­tle bit of fric­tion, and that’s the bal­ance that is re­flected in the foun­tain pen/​pa­per tech­nol­ogy.

Rresults vary when you use var­i­ous pens on var­i­ous kinds of pa­per. Generally I get the worst re­sults on cheap printer pa­per, be­cause it wicks ink out of the nib too fast, and so cre­ates fat, blurry lines. Often I have the same prob­lem with yel­low le­gal pads. But al­most any pa­per in a blank note­book, or higher-grade printer pa­per with at least 25% cot­ton con­tent, works fine. I’ve learned over time that some of my foun­tain pens work bet­ter with cer­tain kinds of pa­per than oth­ers, so I match them up with­out hav­ing to think about it too hard.

Here’s a 300 dpi scan of tests I did with three dif­fer­ent pens on var­i­ous types of pa­per. You might have to zoom in to see much dif­fer­ence.

The pen on the left is a Jorg Hysek with a wide nib, and you can see that the cheap printer pa­per soaked up a lot of ink and left a thicker, fuzzier line. The le­gal pad was­n’t much bet­ter. Everything else ba­si­cally worked. The 100% cot­ton pa­per is from a box I pur­chased a long time ago - it was mar­keted for print­ing re­sumes, back in the days when peo­ple printed re­sumes. It is the tooth­iest of all these pa­pers and felt no­tice­ably scratch­ier. I guess it goes with­out say­ing that fancy Italian pa­per is the best, but the comp book and mole­sk­ine work per­fectly well with just about any pen.

(For those scor­ing at home, the mid­dle pen is a Diplomat Aero and the one on the right is a Monteverde Invincia)

If the pa­per is thin, writ­ing on one side can bleed through to the other, so the re­sults can be slightly harder to read if you write on both sides. Which leads me to:

The ecosys­tem is­n’t go­ing to col­lapse if you use more pa­per. It’s cheap. Focus on what’s im­por­tant here: your brain and your time. Write on one side. Trying to cram more words into a sheet will take you out of your nat­ural and com­fort­able writ­ing style and make you tired. Just buy a shit­load of pa­per or note­books or what­ever it is you want to use, and use it.

There’s a rea­son cur­sive was in­vented. Don’t even think about not us­ing it. It is far less tir­ing than print­ing one let­ter at a time. I learned cur­sive as a child. Then I went for many years with­out us­ing it much, and for­got some of it. Later I re-learned it by sit­ting in my kid’s el­e­men­tary school class­room dur­ing a par­ent-teacher con­fer­ence and ex­am­in­ing the forms printed on a long strip above the chalk­board (I still re­mem­bered how to do the lower-case let­ters, but I had for­got­ten some of the cap­i­tals).

Legibility was more im­por­tant back in the day when writ­ten doc­u­ments had to be read by other peo­ple. Hence the need for ex­act­ing pen­man­ship, taught in schools to long-suf­fer­ing chil­dren. This is prob­a­bly the source of a lot of angst around writer’s cramp and ink dis­as­ters. Today, if you’re writ­ing things down with ink on pa­per, you’re prob­a­bly writ­ing just for your­self, or per­haps for fam­ily mem­bers who can learn to rec­og­nize your hand­writ­ing.

To judge from the way peo­ple talk, a lot of them have mem­o­ries of foun­tain pen dis­as­ters where ink got all over the place for some rea­son. Or per­haps it’s just gen­er­a­tional trauma, handed down in an oral tra­di­tion. If the pen is work­ing cor­rectly, ink can only come out of it so fast. A cou­ple of rare ex­cep­tions:

If the pen’s ink reser­voir is partly empty, so that it con­tains an air bub­ble, and if it’s po­si­tioned nib down, then, when you go up in an air­plane, the bub­ble will ex­pand as the am­bi­ent pres­sure drops, forc­ing ink out the nib. Once I fig­ured that out, I got in the habit of mak­ing sure my pens were po­si­tioned nib up when tak­ing off in an air­plane. If I have time I’ll also re­fill the pen be­fore de­par­ture, to min­i­mize the size of the air bub­ble.

Sometimes if a pen gets dirty, or if the nib is some­how dam­aged, the ink will stop com­ing out and you can restart it by giv­ing it a lit­tle shake. If you do it just right, the ink flow restarts with­out in­ci­dent, but if you overdo it, a few drops of ink might shoot out onto the page and be­come blots. This sce­nario hap­pens a few times of year for me, only with one pen that has this prob­lem. I blot it with a piece of scrap pa­per and move on.

Just have note­books ly­ing around, or on your per­son. Write gro­cery lists, doo­dles, notes on meet­ings, to-do lists, or stray ideas. Journal. Copy out good lines from books. Anything that has your men­tal fo­cus will have a more en­dur­ing pres­ence in your brain if you write it down.

I am left handed. I have never had any trou­ble with my hand smear­ing the ink. Yet every con­ver­sa­tion I have about foun­tain pens leads to some­one claim­ing that it can never work for them be­cause they are left handed. I have no idea what they’re talk­ing about. When I was a child, writ­ing at length with pen­cil, the side of my hand some­times be­came gray from graphite picked up as my hand rubbed across the page. And some­times I have got ink on my hand when us­ing a ball­point pen that left an ink glob on the pa­per. But with foun­tain pens it’s easy to find a pen/​pa­per com­bi­na­tion such that the ink soaks into the pa­per and dries quickly enough that it does­n’t smudge when you’re writ­ing the next line. Here’s a sim­ple demon­stra­tion of dry­ing time and how it works with two pens: first a foun­tain pen and then a Pilot G-2 gel pen.

Obviously the Pilot gel pen ink dries faster, and so that might be a bet­ter choice for peo­ple who are re­ally wor­ried about smudg­ing.

Most mod­ern pens al­low you to choose be­tween us­ing pre­loaded plas­tic ink car­tridges and a plunger that en­ables you to draw ink up out of a bot­tle by hand. I use both. Start with the ink car­tridges, es­pe­cially if you travel. There’s no need to com­pli­cate mat­ters by mess­ing around with bot­tles. Since I do a lot of work from one lo­ca­tion, I have a cor­ner of a table­top set up there with ink bot­tles and a folded-up pa­per towel for wip­ing off the nib af­ter it’s filled (I have been us­ing the same pa­per towel for about twenty years). In the­ory this works bet­ter in the long term be­cause it al­lows you to flush the nib by forc­ing ink in and out of it a cou­ple of times when­ever you re­fill. In prac­tice I see no dif­fer­ence at all - pens that I re­fill with car­tridges don’t get clogged.

Even if every­thing works per­fectly you’ll end up with the oc­ca­sional ink-smudged fin­ger. It will wash off quickly - the ink is wa­ter-sol­u­ble. Until then, con­sider it a mark of dis­tinc­tion.

If you’re new to this I think it makes most sense to start by con­sid­er­ing what kind of pa­per is go­ing to work best in your life. Are you writ­ing loose­leaf, or in note­books? Legal pads? Blue books? Remember, it’s okay to use lots of pa­per, so pick some­thing that is­n’t too pre­cious and that is easy to re­plen­ish. I use a lot of mole­sk­ine note­books and Mead comp books, which I can buy in bulk on­line. For com­pos­ing fic­tion I use fancy loose­leaf pa­per.

If you have ac­cess to a store where they sell foun­tain pens, take some of that pa­per there and see what works best. If you’re work­ing with cheaper, thin­ner pa­per, start with finer nibs and work up to fat­ter ones un­til you start to see bleed-through.

Buy cheaper pens un­til you know what you like. I doubt there’s much of a dif­fer­ence be­tween cheaper and more ex­pen­sive foun­tain pens in terms of their ac­tual per­for­mance. What you’re pay­ing for, in an ex­pen­sive pen, is fancy ma­te­ri­als and styling. For ex­am­ple, if you look at the Pilot Vanishing Point line of pens - an in­ge­nious foun­tain pen that you can click, like an old-fash­ioned ball­point, to re­tract the nib in­side the bar­rel - fancier ver­sions cost five times as much as the base model.

In all hon­esty, the Pilot G-2 gel pens are go­ing to give you 80% of what you could ex­pect from a foun­tain pen for min­i­mal cost.

On the other hand, a ten-pack of Pilot G-2 gel pens goes for about twenty bucks. For the same amount you can buy a sim­ple but com­pletely ser­vice­able foun­tain pen that will last longer than you will.

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📰  Tech Blog |     📄  Full Report

1. Model Introduction

Kimi K3 is an open-weight, na­tive mul­ti­modal agen­tic model and our most ca­pa­ble model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with na­tive vi­sion ca­pa­bil­i­ties and a 1-million-token con­text win­dow. It is the world’s first open 3T-class model, de­signed for fron­tier in­tel­li­gence across long-hori­zon cod­ing, knowl­edge work, and rea­son­ing.

Key Features

New Architecture: Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and scales up MoE spar­sity with a Stable LatentMoE frame­work that ac­ti­vates 16 out of 896 ex­perts — yield­ing an ap­prox­i­mate 2.5× im­prove­ment in over­all scal­ing ef­fi­ciency over Kimi K2.

Long-Horizon Coding: Operating with min­i­mal hu­man over­sight, Kimi K3 sus­tains long en­gi­neer­ing ses­sions, nav­i­gates mas­sive repos­i­to­ries, and or­ches­trates ter­mi­nal tools — from GPU ker­nel op­ti­miza­tion and com­piler de­vel­op­ment to vi­sion-in-the-loop game dev, CAD, and even chip de­sign.

Agentic Knowledge Work: Kimi K3 ad­vances end-to-end knowl­edge work, pro­duc­ing deep re­search with in­ter­ac­tive vi­su­al­iza­tions, wid­gets and dash­boards, and mo­tion de­sign and video edit­ing, pow­ered by its na­tive mul­ti­modal ar­chi­tec­ture.

Native Multimodality & Long Context: Kimi K3 un­der­stands text, im­ages, and video within the same model, and sup­ports a 1-million-token con­text win­dow.

Open Frontier Weights: We re­lease the full Kimi K3 model weights un­der the Kimi K3 License, mak­ing fron­tier in­tel­li­gence openly avail­able for re­search, de­ploy­ment, and fur­ther in­no­va­tion.

2. Model Summary

3. Evaluation Results

All Kimi K3 re­sults are ob­tained with rea­son­ing ef­fort set to ‘max’ and tem­per­a­ture = 1.0. For sin­gle-step tasks, such as GPQA Diamond, HLE-Full, and vi­sion bench­marks with­out tools, we set top-p = 0.95; for agen­tic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision, and ZeroBench, each cell re­ports the scores with­out and with tool aug­men­ta­tion (general tools for HLE-Full, Python for the vi­sion bench­marks), in that or­der.

Reasoning & knowl­edge bench­marks CritPt and AA-LCR. Scores are cited from Artificial Analysis as of July 23, 2026.

CritPt and AA-LCR. Scores are cited from Artificial Analysis as of July 23, 2026.

Coding bench­marks DeepSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is taken from the GLM-5.2 re­lease blog; all re­main­ing scores are from the of­fi­cial DeepSWE leader­board, un­der which Kimi K3 at­tains 67.3 with the mini-SWE-agent har­ness. We re­port the DeepSWE v1.1 tasks. Terminal-Bench 2.1. Kimi K3 is eval­u­ated with the Kimi Code har­ness. For all other mod­els, we re­port the best score across har­nesses: GLM-5.2 with Claude Code (GLM-5.2 re­lease blog); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI). ProgramBench. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog; all other scores are from Vals AI. SWE-Marathon. Kimi K3, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.6 Sol is eval­u­ated with the Codex har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog. Our eval­u­a­tion is based on an H20-calibrated branch of the of­fi­cial tasks as of July 9, 2026, prior to the fi­nal v1.1 re­lease: the Docker im­ages, per­for­mance gates, and ref­er­ence or­a­cles for the GPU tasks have been re­cal­i­brated for H20, while the cor­rect­ness and anti-cheat val­ida­tors re­main un­changed. Additionally, Claude Fable 5 hit fall­backs on 35% of the tasks in our eval­u­a­tion, which may have neg­a­tively im­pacted its mea­sured per­for­mance. FrontierSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness and GPT-5.6 Sol with the Codex har­ness; all other re­sults are from FrontierSWE. Dominance scores are re­com­puted from the raw scores us­ing the of­fi­cial eval­u­a­tion script and are cur­rent as of July 16, 2026. PostTrainBench. Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the of­fi­cial PostTrainBench re­sults. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are eval­u­ated with the of­fi­cial Harbor im­ple­men­ta­tion at max­i­mum rea­son­ing ef­fort, av­er­aged over three runs on H20 GPUs (instead of H100 in the of­fi­cial set­ting) — Kimi K3 and Claude Fable 5 with the Claude Code har­ness, and GPT-5.6 Sol with the Codex har­ness. MLS-Bench-Lite. Kimi K3 is eval­u­ated with the Kimi Code har­ness; GLM-5.2 and the Claude mod­els with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. SciCode. Scores are cited from Artificial Analysis as of July 23, 2026. Kimi Code Bench 2.0 (in-house). Kimi K3 is eval­u­ated with the Kimi Code har­ness (it at­tains 73.7 with the Claude Code har­ness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. All mod­els are eval­u­ated at max­i­mum rea­son­ing ef­fort, ex­cept GPT-5.5, which uses the “xhigh” set­ting. As the bench­mark in­cludes cy­ber­se­cu­rity and safety-re­lated tasks, we also dis­close the frac­tion of re­fused or fall­back tasks: Claude Fable 5 hit 13 fall­backs and 1 re­fusal out of 80 tasks; 10 re­fusals out of 80 tasks en­tered GPT-5.6 Sol’s cy­ber guard; GPT-5.5 had 3 re­fusals out of 80 tasks.

DeepSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is taken from the GLM-5.2 re­lease blog; all re­main­ing scores are from the of­fi­cial DeepSWE leader­board, un­der which Kimi K3 at­tains 67.3 with the mini-SWE-agent har­ness. We re­port the DeepSWE v1.1 tasks.

Terminal-Bench 2.1. Kimi K3 is eval­u­ated with the Kimi Code har­ness. For all other mod­els, we re­port the best score across har­nesses: GLM-5.2 with Claude Code (GLM-5.2 re­lease blog); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI).

ProgramBench. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog; all other scores are from Vals AI.

SWE-Marathon. Kimi K3, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.6 Sol is eval­u­ated with the Codex har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog. Our eval­u­a­tion is based on an H20-calibrated branch of the of­fi­cial tasks as of July 9, 2026, prior to the fi­nal v1.1 re­lease: the Docker im­ages, per­for­mance gates, and ref­er­ence or­a­cles for the GPU tasks have been re­cal­i­brated for H20, while the cor­rect­ness and anti-cheat val­ida­tors re­main un­changed. Additionally, Claude Fable 5 hit fall­backs on 35% of the tasks in our eval­u­a­tion, which may have neg­a­tively im­pacted its mea­sured per­for­mance.

FrontierSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness and GPT-5.6 Sol with the Codex har­ness; all other re­sults are from FrontierSWE. Dominance scores are re­com­puted from the raw scores us­ing the of­fi­cial eval­u­a­tion script and are cur­rent as of July 16, 2026.

PostTrainBench. Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the of­fi­cial PostTrainBench re­sults. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are eval­u­ated with the of­fi­cial Harbor im­ple­men­ta­tion at max­i­mum rea­son­ing ef­fort, av­er­aged over three runs on H20 GPUs (instead of H100 in the of­fi­cial set­ting) — Kimi K3 and Claude Fable 5 with the Claude Code har­ness, and GPT-5.6 Sol with the Codex har­ness.

MLS-Bench-Lite. Kimi K3 is eval­u­ated with the Kimi Code har­ness; GLM-5.2 and the Claude mod­els with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness.

SciCode. Scores are cited from Artificial Analysis as of July 23, 2026.

Kimi Code Bench 2.0 (in-house). Kimi K3 is eval­u­ated with the Kimi Code har­ness (it at­tains 73.7 with the Claude Code har­ness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. All mod­els are eval­u­ated at max­i­mum rea­son­ing ef­fort, ex­cept GPT-5.5, which uses the “xhigh” set­ting. As the bench­mark in­cludes cy­ber­se­cu­rity and safety-re­lated tasks, we also dis­close the frac­tion of re­fused or fall­back tasks: Claude Fable 5 hit 13 fall­backs and 1 re­fusal out of 80 tasks; 10 re­fusals out of 80 tasks en­tered GPT-5.6 Sol’s cy­ber guard; GPT-5.5 had 3 re­fusals out of 80 tasks.

Agentic bench­marks OfficeQA Pro. Each test case pro­vides the agent with the en­tire PDF cor­pus, with all PDFs ren­dered as im­ages and no ma­chine-read­able text avail­able. OfficeQA Pro and SpreadsheetBench 2. Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol are eval­u­ated with the Codex har­ness. MCP-Atlas. All mod­els are eval­u­ated on the 500-task pub­lic sub­set with a 100-turn limit, us­ing Gemini 3.1 Pro as the judge. AutomationBench. All mod­els are eval­u­ated on the 600-task pub­lic sub­set, fol­low­ing the of­fi­cial GitHub setup in all other re­spects. BrowseComp. We adopt a con­text-com­paction strat­egy trig­gered at 300K to­kens. When eval­u­ated with the full 1M-token con­text win­dow and no con­text man­age­ment, Kimi K3 achieves a score of 90.4. The re­sults of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from Anthropic and OpenAI. GDPval-AA v2, AA-Briefcase, τ³-Bank­ing, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the APEX-Agents leader­board as of July 23, 2026. For Harvey Lab-AA, we re­port the cri­te­rion pass rate. CorpFin v2, Finance Agent v2, and Legal Research Bench. Scores are cited from Vals AI. Agents’ Last Exam. Scores are cited from the of­fi­cial leader­board as of July 23, 2026; we re­port the leader­board’s pri­mary pass-rate met­ric. On the leader­board, each model is paired with a spe­cific har­ness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. † The Claude Fable 5 en­try runs at xhigh ef­fort with 40% of tasks an­no­tated as down­graded.

OfficeQA Pro. Each test case pro­vides the agent with the en­tire PDF cor­pus, with all PDFs ren­dered as im­ages and no ma­chine-read­able text avail­able.

OfficeQA Pro and SpreadsheetBench 2. Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol are eval­u­ated with the Codex har­ness.

MCP-Atlas. All mod­els are eval­u­ated on the 500-task pub­lic sub­set with a 100-turn limit, us­ing Gemini 3.1 Pro as the judge.

AutomationBench. All mod­els are eval­u­ated on the 600-task pub­lic sub­set, fol­low­ing the of­fi­cial GitHub setup in all other re­spects.

BrowseComp. We adopt a con­text-com­paction strat­egy trig­gered at 300K to­kens. When eval­u­ated with the full 1M-token con­text win­dow and no con­text man­age­ment, Kimi K3 achieves a score of 90.4. The re­sults of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from Anthropic and OpenAI.

GDPval-AA v2, AA-Briefcase, τ³-Bank­ing, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the APEX-Agents leader­board as of July 23, 2026. For Harvey Lab-AA, we re­port the cri­te­rion pass rate.

CorpFin v2, Finance Agent v2, and Legal Research Bench. Scores are cited from Vals AI.

Agents’ Last Exam. Scores are cited from the of­fi­cial leader­board as of July 23, 2026; we re­port the leader­board’s pri­mary pass-rate met­ric. On the leader­board, each model is paired with a spe­cific har­ness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. † The Claude Fable 5 en­try runs at xhigh ef­fort with 40% of tasks an­no­tated as down­graded.

Multimodal bench­marks Except for ZeroBench, which fol­lows the of­fi­cial set­ting and is run five times, all mul­ti­modal scores are av­er­aged over three runs. MMMU-Pro is eval­u­ated fol­low­ing the of­fi­cial pro­to­col, pre­serv­ing the orig­i­nal in­put or­der and prepend­ing im­ages to the text in­put. PerceptionBench is an in-house bench­mark that fo­cuses on atomic vi­sual per­cep­tion ca­pa­bil­i­ties.

Except for ZeroBench, which fol­lows the of­fi­cial set­ting and is run five times, all mul­ti­modal scores are av­er­aged over three runs. MMMU-Pro is eval­u­ated fol­low­ing the of­fi­cial pro­to­col, pre­serv­ing the orig­i­nal in­put or­der and prepend­ing im­ages to the text in­put.

PerceptionBench is an in-house bench­mark that fo­cuses on atomic vi­sual per­cep­tion ca­pa­bil­i­ties.

4. Native MXFP4 Quantization

Kimi K3 ap­plies quan­ti­za­tion-aware train­ing from the SFT stage on­ward, us­ing MXFP4 weights with MXFP8 ac­ti­va­tions for broad hard­ware com­pat­i­bil­ity.

5. Deployment

You can ac­cess Kimi K3′s API on https://​plat­form.kimi.ai by se­lect­ing kimi-k3, and we pro­vide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is rec­om­mended to run on the fol­low­ing in­fer­ence en­gines:

You can ac­cess Kimi K3′s API on https://​plat­form.kimi.ai by se­lect­ing kimi-k3, and we pro­vide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is rec­om­mended to run on the fol­low­ing in­fer­ence en­gines:

vLLM — see recipes

SGLang — see cook­book

TokenSpeed — see recipes

6. Model Usage

Kimi K3 al­ways has think­ing en­abled, and will re­turn rea­son­ing_­con­tent. Thinking ef­fort is con­fig­ured with the top-level rea­son­ing_­ef­fort re­quest field, which sup­ports “low”, “high”, and “max” (default “max”).

Kimi K3 was trained in the pre­served think­ing his­tory mode. For multi-turn con­ver­sa­tions and tool calls, Kimi K3 re­quires the com­plete as­sis­tant mes­sage re­turned by the API to be passed back to mes­sages as-is — in­clud­ing rea­son­ing_­con­tent and tool_­calls, not just con­tent:

im­port ope­nai

def chat_with­_p­re­served_­think­ing(client: ope­nai.Ope­nAI, mod­el_­name: str): mes­sages = [ { “role”: “user”, “content”: “Tell me three ran­dom num­bers.” }, { “role”: “assistant”, “reasoning_content”: “I’ll start by list­ing five num­bers: 473, 921, 235, 215, 222, and I’ll tell you the first three.”, “content”: “473, 921, 235″ }, { “role”: “user”, “content”: “What are the other two num­bers you have in mind?” } ]

re­sponse = client.chat.com­ple­tions.cre­ate( model=mod­el_­name, mes­sages=mes­sages, stream=False, max_­to­kens=4096, rea­son­ing_­ef­fort=“max”, ) # the as­sis­tant should men­tion 215 and 222 that ap­pear in the prior rea­son­ing con­tent print(f”re­sponse: {response.choices[0].message.reasoning}“) re­turn re­sponse.choices[0].mes­sage.con­tent

For full guides and ex­am­ples (vision in­put, struc­tured out­put, par­tial mode, tool choice, dy­namic tool load­ing, con­text caching), see the Kimi K3 Quickstart and Thinking Effort.

Coding Agent Framework

Kimi K3 works best with Kimi Code CLI as its agent frame­work. We warmly in­vite you to give it a try — run Kimi Code in your ter­mi­nal and se­lect Kimi K3 us­ing the /model com­mand. We hope you en­joy build­ing with Kimi K3, and we would love to hear your feed­back!

7. License

Both the code repos­i­tory and the model weights are re­leased un­der the Kimi K3 License.

8. Contact Us

If you have any ques­tions, please reach out at sup­port@moon­shot.ai.

Safetensors

Model tree for moon­shotai/​Kimi-K3

Spaces us­ing moon­shotai/​Kimi-K3 5

Collection in­clud­ing moon­shotai/​Kimi-K3

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A hot potato: A fed­eral case in Atlanta is rais­ing ques­tions about a pri­vacy-fo­cused mo­bile op­er­at­ing sys­tem, with pros­e­cu­tors ar­gu­ing that its fea­tures were used to erase ev­i­dence. The US Department of Justice is at­tempt­ing to pros­e­cute Atlanta res­i­dent Sam Tunick un­der a fed­eral statute that makes it a crime to de­stroy prop­erty in an ef­fort to pre­vent it from be­ing seized.

The case cen­ters on Tunick’s use of GrapheneOS, an open-source op­er­at­ing sys­tem that works on Google Pixel phones and lets users en­ter a pass­code to wipe a de­vice clean.

Experts said the le­gal ap­proach is un­usual and may be the first time the law has been aimed at an op­er­at­ing sys­tem. “It’s con­cern­ing — and sends the mes­sage that [GrapheneOS] is crim­i­nal by de­fault,” said Christophe Boutry, a cy­ber­se­cu­rity and sur­veil­lance ex­pert. Boutry and Bill Buddington, se­nior staff tech­nol­o­gist at the Electronic Frontier Foundation, both said they had not seen a sim­i­lar case.

The in­ci­dent be­gan at Hartsfield-Jackson Atlanta International Airport on January 24 of last year. Tunick had just re­turned from a trip to the Dominican Republic when he was stopped for ques­tion­ing. According to court tes­ti­mony, fed­eral agents had al­ready cir­cu­lated his name and photo in­ter­nally, say­ing he was un­der in­ves­ti­ga­tion for “suspected ter­ror­ism ac­tiv­i­ties” be­cause of his al­leged as­so­ci­a­tion with the move­ment against Cop City.

Tunick was taken to a sec­ondary screen­ing room, where mul­ti­ple agents ques­tioned him. A mo­tion filed by his de­fense ar­gues the in­ter­ro­ga­tion fo­cused on child sex­ual abuse ma­te­r­ial as a pre­text for in­ves­ti­gat­ing his con­nec­tions to the protest move­ment. The mo­tion also states that Tunick asked four times to speak with a lawyer and was de­nied each time. According to the same fil­ing, agents did not pre­sent a war­rant or read him his rights.

Government at­tor­neys and agents pushed back dur­ing Monday’s hear­ing. They de­scribed the en­counter as a rou­tine air­port in­spec­tion. Larry Findley, a Customs and Border Protection of­fi­cer, said agents were “looking for any­thing that’s pro­hib­ited.”

During the ques­tion­ing, agents re­peat­edly asked Tunick to un­lock his phone and warned they would seize it if he re­fused. When he fi­nally pro­vided a pass­code, the phone ap­peared to restart. The de­fense mo­tion states that “the screen went blank, flashed sev­eral times, and the phone ap­peared to restart,” re­sult­ing in the loss of data.

The wipe is now cen­tral to the case. Prosecutors are treat­ing it as an in­ten­tional act to de­stroy ev­i­dence, while the de­fense ar­gues that the search vi­o­lated Tunick’s con­sti­tu­tional rights and that the ev­i­dence should be sup­pressed.

The case raises ques­tions about which con­sti­tu­tional rights ap­ply at US bor­ders, in­clud­ing in­ter­na­tional air­ports, where au­thor­i­ties have broader search pow­ers. A judge is not ex­pected to rule on the de­fense mo­tion un­til at least late October.

GrapheneOS is de­signed to im­prove pri­vacy and se­cu­rity on Pixel phones. Supporters say those tools are le­git­i­mate se­cu­rity pro­tec­tions, not ev­i­dence of crim­i­nal in­tent. Boutry pointed to France and Spain, where au­thor­i­ties have strug­gled to gain ac­cess to se­cured de­vices. He said au­thor­i­ties have treated the use of GrapheneOS it­self as sus­pi­cious. In Catalonia, Spain, po­lice have been pro­fil­ing peo­ple car­ry­ing Pixel phones, as­sum­ing they have GrapheneOS in­stalled and are drug deal­ers or gang mem­bers.

“The main goal [of the op­er­at­ing sys­tem] is pro­tec­tion of pri­vacy,” Boutry said. “They’re our phones and the state can’t tell us how to use them.”

The case is tied to on­go­ing op­po­si­tion to Cop City, a $109 mil­lion po­lice train­ing fa­cil­ity that opened last spring. The pro­ject has drawn op­po­si­tion from ac­tivists con­cerned about po­lice mil­i­ta­riza­tion and en­vi­ron­men­tal im­pacts. Law en­force­ment of­fi­cials have de­fended it as nec­es­sary for train­ing and re­cruit­ment.

Previous at­tempts to pros­e­cute pro­test­ers at the state level have foundered, while fed­eral au­thor­i­ties have more re­cently stepped in, in­clud­ing a sep­a­rate in­dict­ment an­nounced last month.

Stop the track­ing cir­cus.

Tired of mis­lead­ing cookie ban­ners? The EU Commission has fi­nally pro­posed a so­lu­tion: set your pri­vacy pref­er­ences in the browser once, and never see an­other ban­ner. Unfortunately, the track­ing in­dus­try is push­ing back — and so far, they’ve been suc­cess­ful. We need YOUR help to #KillTheCookieBanner!

Cookie ban­ners are made to trick you into waiv­ing your rights

You may think that EU pri­vacy law re­quires cookie ban­ners. But the law is clear: on­line track­ing is pro­hib­ited by de­fault.

Therefore, the track­ing in­dus­try needs you to waive your rights. That’s why they in­vented cookie ban­ners, which of­ten are de­lib­er­ately mis­lead­ing as well as an­noy­ing.

This re­sults in up to 90% of peo­ple say­ing “YES” — even though only around 3% ac­tu­ally want to be tracked on­line. This sys­tem is bro­ken by de­sign.

The so­lu­tion: au­to­mat­i­cally com­mu­ni­cate your pri­vacy pref­er­ence

In Autumn 2025, as part of a big­ger le­gal re­form*, the EU Commission fi­nally pro­posed a so­lu­tion to the cookie ban­ner prob­lem: au­to­mated sig­nals that would com­mu­ni­cate your pri­vacy pref­er­ences be­tween your de­vice and web­sites or apps. You could then choose whether you want to ac­cept, refuse, or limit track­ing.

This idea is nei­ther new nor com­plex. Your browser al­ready au­to­mat­i­cally sig­nals other pref­er­ences to web­sites, for ex­am­ple your pre­ferred lan­guage. In some US states, such sig­nals for pri­vacy pref­er­ences are al­ready legally sup­ported.

The track­ing lobby fights to keep the cookie ban­ner

This would be a sim­ple so­lu­tion. But the track­ing in­dus­try seems afraid that if you can ex­press your pref­er­ences ef­fi­ciently, it could re­sult in lower con­sent rates for track­ing.

Following lob­by­ing ef­forts spear­headed by Google and the track­ing in­dus­try, sev­eral Member States are now block­ing the EU Commission’s pro­posal to get rid of the cookie ban­ner.

But not only that: in­dus­try groups are also lob­by­ing the European Parliament to re­ject the pro­posal.

We need YOUR help!

That’s where you come in: the fight to kill the cookie ban­ner is far from over. The Member States and the European Parliament have not yet de­cided on their po­si­tion on the is­sue yet.

You can take ac­tion by con­tact­ing your rep­re­sen­ta­tive in the European Parliament or in your Member State and ex­press your frus­tra­tion.

*This pro­posal for pri­vacy sig­nals is part of an EU law re­form called the Digital Omnibus. Most other parts of this re­form are prob­lem­atic and would weaken peo­ple’s rights. We want to make clear that we do not sup­port these other as­pects of the pro­posed re­form.

*This pro­posal for pri­vacy sig­nals is part of an EU law re­form called the Digital Omnibus. Most other parts of this re­form are prob­lem­atic and would weaken peo­ple’s rights. We want to make clear that we do not sup­port these other as­pects of the pro­posed re­form.

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