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[AlphaSignal @AlphaSignalAI]

Kimi K3 is getting called Fable/Sol level, and it's 7th in our tests.

Arena Frontend Code: #1 at 1679 points.
Artificial Analysis: #3 at Intelligence Index of 57.

We ran it the next day on our coding-agent repair harness against GPT-5.6 Sol, Fable 5, Grok 4.5, Opus 4.8, GLM-5.2, and Gemini 3.1 Pro.

Results:
> Last of 7 models
> 53 of 67 attempts (79%)
> $0.186 per successful fix
> 702s average wall time

Sol hit 100% (70/70) on the same suite. Grok sat at 99% and 46s.

So why does the internet sound so sure K3 is crushing coding agents, if our tests have it at the bottom?
-----
> Full write-up: https://t.co/B0ae9dbP5A
> 5-min daily signals: https://t.co/ZGatS9hxp5

[Artificial Analysis @ArtificialAnlys]

Kimi K3 scores 57 on the Artificial Analysis Intelligence Index. Its intelligence is comparable to Opus 4.8 and GPT-5.5 but remains behind Fable 5 and GPT-5.6 Sol. Moonshot AI has expressed plans to release the 2.8T parameter model's weights, which would make it the leading open weights model

Key results:

➤ Strong agentic task performance: @Kimi_Moonshot's Kimi K3 reaches an Elo rating of 1668 on GDPval v2. This is a marked improvement over K2.6’s 1190, surpassing GLM-5.2 (1514), GPT-5.5 (1494), and Claude Opus 4.8 (1600). However, it still lags behind Claude Fable 5 (1760). Kimi K3 also scores an impressive 53% and takes the #1 position on AutomationBench-AA, our implementation of Zapier’s Agentic SaaS workflow evaluation.

➤ Second-highest performance on AA-Briefcase (agentic knowledge work): On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5. It is well-rounded: its rubric scoring and analytical quality almost reach Claude Fable 5’s scores, while GPT-5.6 Sol continues to outperform other leading models on presentation quality.

➤ Set to lead open weights models once weights are released: Moonshot AI has not yet released the weights but expressed plans to do so. Once available, Kimi K3 would clearly lead other open weights models including GLM-5.2 (51) and DeepSeek v4 Pro (44). However, at 2.8T parameters, it is significantly larger than its open weights peers (eg. GLM-5.2 at 753B params and DeepSeek V4 Pro at 1.6T), as well as the Kimi K2 to K2.6 models (1T params).

➤ Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers: Moonshot AI’s pricing for K3 is significantly higher than their K2 pricing (K3’s output token price is $15/1M tokens while K2.6 was $4). This positions the model as cheaper on a cost per task basis than Opus 4.8, similar to GPT-5.6 Sol ($1.04) and more expensive than open weights peers, GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04)

➤ Improved token efficiency alongside higher intelligence: Kimi K3’s token usage on the Artificial Analysis Intelligence Index decreased significantly, using 21% fewer output tokens than K2.6. The new model used approximately 132M output tokens to complete all nine evaluations, compared to approximately 166M for K2.6, while achieving higher scores.

➤ Native multimodal capabilities: Kimi K3, like K2.6, is released with native image and text multimodal input. If weights are released, this will position Kimi K3 as one of the leading open weights models with multimodal input capabilities

Other model details:

Context window: 1M

Size: 2.8T total parameters

Pricing: The first-party API is priced at $3.00/$15.00 per 1M input/output tokens, with cached input discounted 90% to $0.30 per 1M tokens.

Modality: Native multimodal input supports text and images, and the model remains text-only for output.

Accessibility: Accessible at launch through Moonshot’s first party API. Model weights are not yet released but Moonshot AI has expressed plans to do so.

[AlphaSignal @AlphaSignalAI]

Follow up:

Method:
> 13 planted-bug tasks · 7 models · same rules for all
> Network-off Docker sandboxes
> Held-out tests only at scoring
> Pass = visible + held-out + full regression, no test-file edits

Caveats: small task set, one harness, K3 needs full thinking history each turn. We re-checked K3 traces for missing reasoning_content and unexpected stops and found No mass trajectory breaks. Ranking still stands on the scored attempts.

[AlphaSignal @AlphaSignalAI]

Kimi K3 is getting called Fable/Sol level, and it's 7th in our tests.

Arena Frontend Code: #1 at 1679 points.
Artificial Analysis: #3 at Intelligence Index of 57.

We ran it the next day on our coding-agent repair harness against GPT-5.6 Sol, Fable 5, Grok 4.5, Opus 4.8, GLM-5.2, and Gemini 3.1 Pro.

Results:
> Last of 7 models
> 53 of 67 attempts (79%)
> $0.186 per successful fix
> 702s average wall time

Sol hit 100% (70/70) on the same suite. Grok sat at 99% and 46s.

So why does the internet sound so sure K3 is crushing coding agents, if our tests have it at the bottom?
-----
> Full write-up: https://t.co/B0ae9dbP5A
> 5-min daily signals: https://t.co/ZGatS9hxp5

[Artificial Analysis @ArtificialAnlys]

Kimi K3 scores 57 on the Artificial Analysis Intelligence Index. Its intelligence is comparable to Opus 4.8 and GPT-5.5 but remains behind Fable 5 and GPT-5.6 Sol. Moonshot AI has expressed plans to release the 2.8T parameter model's weights, which would make it the leading open weights model

Key results:

➤ Strong agentic task performance: @Kimi_Moonshot's Kimi K3 reaches an Elo rating of 1668 on GDPval v2. This is a marked improvement over K2.6’s 1190, surpassing GLM-5.2 (1514), GPT-5.5 (1494), and Claude Opus 4.8 (1600). However, it still lags behind Claude Fable 5 (1760). Kimi K3 also scores an impressive 53% and takes the #1 position on AutomationBench-AA, our implementation of Zapier’s Agentic SaaS workflow evaluation.

➤ Second-highest performance on AA-Briefcase (agentic knowledge work): On our private long-horizon knowledge work evaluation, Kimi K3 reaches an overall Elo of 1547, +732 points from Kimi K2.6 and behind only Claude Fable 5. It is well-rounded: its rubric scoring and analytical quality almost reach Claude Fable 5’s scores, while GPT-5.6 Sol continues to outperform other leading models on presentation quality.

➤ Set to lead open weights models once weights are released: Moonshot AI has not yet released the weights but expressed plans to do so. Once available, Kimi K3 would clearly lead other open weights models including GLM-5.2 (51) and DeepSeek v4 Pro (44). However, at 2.8T parameters, it is significantly larger than its open weights peers (eg. GLM-5.2 at 753B params and DeepSeek V4 Pro at 1.6T), as well as the Kimi K2 to K2.6 models (1T params).

➤ Cost per task ($0.94) is similar to GPT-5.6 Sol ($1.04), ~1/2 the price of Opus 4.8 ($1.80) and higher than open weights peers: Moonshot AI’s pricing for K3 is significantly higher than their K2 pricing (K3’s output token price is $15/1M tokens while K2.6 was $4). This positions the model as cheaper on a cost per task basis than Opus 4.8, similar to GPT-5.6 Sol ($1.04) and more expensive than open weights peers, GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04)

➤ Improved token efficiency alongside higher intelligence: Kimi K3’s token usage on the Artificial Analysis Intelligence Index decreased significantly, using 21% fewer output tokens than K2.6. The new model used approximately 132M output tokens to complete all nine evaluations, compared to approximately 166M for K2.6, while achieving higher scores.

➤ Native multimodal capabilities: Kimi K3, like K2.6, is released with native image and text multimodal input. If weights are released, this will position Kimi K3 as one of the leading open weights models with multimodal input capabilities

Other model details:

Context window: 1M

Size: 2.8T total parameters

Pricing: The first-party API is priced at $3.00/$15.00 per 1M input/output tokens, with cached input discounted 90% to $0.30 per 1M tokens.

Modality: Native multimodal input supports text and images, and the model remains text-only for output.

Accessibility: Accessible at launch through Moonshot’s first party API. Model weights are not yet released but Moonshot AI has expressed plans to do so.

[AlphaSignal @AlphaSignalAI]

Follow up:

Method:
> 13 planted-bug tasks · 7 models · same rules for all
> Network-off Docker sandboxes
> Held-out tests only at scoring
> Pass = visible + held-out + full regression, no test-file edits

Caveats: small task set, one harness, K3 needs full thinking history each turn. We re-checked K3 traces for missing reasoning_content and unexpected stops and found No mass trajectory breaks. Ranking still stands on the scored attempts.

[AlphaSignal @AlphaSignalAI]

What our test is (and is not):

We run a coding-agent repair harness (Signaldesk), not a frontend board and not Kimi Code.

> 13 planted-bug tasks · 7 models · same rules for all
> Network-off Docker sandboxes
> Held-out tests only at scoring
> Pass = visible + held-out + full regression, no test-file edits

K3 on that run: 53/67 (79%), last of 7. Sol 100% (70/70). Grok 99% at 46s.

Arena Frontend #1 (1679) can still be real. Different job: UI generation preference vs multi-file repair under held-out tests.

Caveats: small task set, one harness, K3 needs full thinking history each turn. We re-checked K3 traces for missing reasoning_content and unexpected stops and found No mass trajectory breaks. Ranking still stands on the scored attempts.

Full method: https://t.co/B0ae9dbP5A

[AlphaSignal @AlphaSignalAI]

@elonmusk Thanks!!!

[AlphaSignal @AlphaSignalAI]

@TuanPham672604 We checked the traces, not tool-loop stuck, just weak on this suite, 80% resolve, slow and expensive for what it got done

[AlphaSignal @AlphaSignalAI]

@CultOfRoyal @arena Makes sense, on our suite GLM-5.2 still hit 94% (65/69), well above Kimi K3 at 79%, so even ���good repair” models have different failure modes

[AlphaSignal @AlphaSignalAI]

Kimi K3 is the 7th in our tests while it hit #1 on @arena Frontend Code at 1679 points!

We ran it the next day on our coding-agent repair harness against six peers: GPT-5.6 Sol, Fable 5, Grok 4.5, Opus 4.8, GLM-5.2, and Gemini 3.1 Pro.

It finished last of 7 the models with 53 of 67 attempts (79%).

So, How can both be true?

Read why the boards disagree (and the full scoreboard) in replies ↓↓

[Kimi.ai @Kimi_Moonshot]

Introducing Kimi K3: Open Frontier Intelligence

🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows

Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.

🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f

[AlphaSignal @AlphaSignalAI]

@LmbtnE @arena hahaa

[AlphaSignal @AlphaSignalAI]

@Draymen424 @arena Thanks, different boards measuring different jobs is the whole point here

[AlphaSignal @AlphaSignalAI]

@_Suresh2 Yep, frontend Elo can crown a model that still struggles once the task is multi-file repair under held-out tests

[AlphaSignal @AlphaSignalAI]

@TuanPham672604 Same bottom band as Kimi on our run, gemini 3.1 Pro preview at 80% (63/79), K3 at 79% (53/67), Sol still at 100%

[AlphaSignal @AlphaSignalAI]

@aishikejian Different issue than our bench, but real, unattended shell agents need hard allowlists before destructive ops

[AlphaSignal @AlphaSignalAI]

@AGTPinsights Fair, our post is capability on a repair harness, not enterprise governance, different layer than orchestration and policy

[AlphaSignal @AlphaSignalAI]

@onprssss This is one repair suite, not a lifestyle ranking, on our run Gemini was 80% and K3 79%, both bottom tier vs Sol at 100%

[AlphaSignal @AlphaSignalAI]

@notanazgd0 Not saying Gemini wins everywhere, on this one repair harness Gemini edged 80% vs K3 at 79%, different job than day-to-day product feel

[AlphaSignal @AlphaSignalAI]

@xwhynoted Pretty clean split, 1679 is frontend preference, 79% is whether the fix survives tests

[AlphaSignal @AlphaSignalAI]

@ajs6888 That’s the whole post, frontend Elo and held-out repair are different jobs

[AlphaSignal @AlphaSignalAI]

@soul99soul Not Kimi Code, its our Signaldesk repair harness, same rules for all models. On thinking history, yes, that is a real Kimi constraint, full chain must come back each turn or quality falls off, we are re-checking those trajectories

[AlphaSignal @AlphaSignalAI]

Harness is our own Signaldesk coding-agent suite, not Kimi Code, same setup for all 7 models, network-off sandboxes, held-out tests at score time

On the reasoning_content point, fair call, Kimi is known to be sensitive to full thinking history, so we will re-check traces for drops and unexpected stops

[AlphaSignal @AlphaSignalAI]

@qiuqiucong Pretty much, Arena #1 is frontend, our harness is repair agents on a full-stack app, and it landed last of 7

[AlphaSignal @AlphaSignalAI]

@Adham__Khaled__ yess