Kimi (Moonshot AI)

Sources#

• Kimi K3 Model Card
• Ramp's latest data on China vs. the American AI Labs
• Recursive Self Improvement for Coding Agents

What it is#

The Kimi line from Moonshot AI — the Chinese open-weight MoE family that, alongside GLM, DeepSeek, MiMo and Qwen, constitutes the approach-the-frontier-with-sparsity pole of The Open-Weight Frontier Gap. Members already present in this corpus before the family had a page:

Model	What's known here
Kimi K2.5	The open model whose synthetic output Inkling used to bootstrap its SFT stage — a competitor's open weights seeding a Western lab's from-scratch post-training.
Kimi K2.6	1T total / 32B active. Arena Text rank 34, Elo 1460, in Gemma 4's June 2026 table — 48 Elo behind Claude Fable 5 at rank 1. Cited by Inkling as one of the two open models it self-reports behind on hard reasoning and coding.
Kimi K3	July 26, 2026. 2.8T total / 104B active. Moonshot's claim: "the world's first open 3T-class model." Weights under a bespoke Kimi K3 License, not Apache/MIT. Everything below.

Kimi K3 architecture (vendor-claim)#

Spec	Value
Total / activated parameters	2.8T / 104B (3.7% active)
Layers	93 (1 dense)
Attention composition	69 Kimi Delta Attention (KDA) + 24 Gated MLA
Attention hidden dim / heads	7168 / 96
Experts	896 routed, 16 selected per token, 2 shared (LatentMoE, latent dim 3584, per-expert hidden 3072)
Vocabulary	160K
Context	1,048,576 tokens
Activation	SiTU-GLU
Vision encoder	MoonViT-V2, 401M
Quantization	MXFP4 weights / MXFP8 activations, QAT from the SFT stage onward
Modality (spec table)	Text, Image

Moonshot names three architectural bets — KDA, Attention Residuals (AttnRes), and a "Stable LatentMoE framework" — and attributes to their combination "an approximate 2.5× improvement in overall scaling efficiency over Kimi K2." No definition of the efficiency metric, no ablation, no curve: the number is a headline claim with nothing behind it in the card. See Inference Efficiency as Capability for why a claim of that shape is a capability claim, and why this one is unauditable as stated.

Two operational details worth recording. K3 always thinks — reasoning_effort takes low/high/max with max as the default, and there is no thinking-off mode. And it was trained in preserved-thinking-history mode: multi-turn callers must pass the complete prior assistant message back, reasoning_content and tool_calls included, not just content. That makes the reasoning trace part of the API contract rather than a discardable artifact — relevant to Chain-of-Thought Monitorability, where whether traces survive the harness is the live question.

Where it lands (Moonshot's own 45-benchmark table)#

The card grades K3 against Claude Fable 5, GPT-5.6 Sol, Claude Opus 4.8, GPT-5.5 and GLM-5.2 across reasoning, coding, agentic and vision suites. Counting top cells: K3 holds the best score on 14 of 45 rows; Fable 5 holds most of the remainder. The card's framing is proximity, not parity, and the shape of the gap is legible:

K3 leads on retrieval, tool orchestration, and document throughput — BrowseComp 91.2 (Fable 5: 88.0), DeepSearchQA F1 95.0, ResearchRubrics 76.2, MCPMark-Verified 94.5 (Fable 5: 87.4), AutomationBench 30.8, τ³-Banking 33.4, Harvey Lab-AA 94.6, OmniDocBench 91.1, Video-MME 90.0, MMVU 82.1, SWE-Marathon 42.0.

K3 trails on hard reasoning and on the hardest long-horizon coding — HLE-Full 43.5 vs Fable 5's 53.3; CritPt 23.4 vs GPT-5.6 Sol's 32.3; FrontierSWE 81.2 vs 86.6; DeepSWE 67.5 vs 73.0; Kimi Code Bench 2.0 72.9 vs 76.9 on Moonshot's own in-house benchmark.

It also trails on messy GUI and office work — OSWorld 2.0 58.3 vs 66.1, OfficeQA Pro 63.3 vs 69.9, JobBench 54.3 vs 57.4 — while matching on the older OSWorld-Verified (84.8 vs 85.0). Whatever the newer OSWorld revision added, K3 lost more of it than Fable 5 did.

On the two third-party Elo agentic measures the card cites from Artificial Analysis, K3 sits 61 Elo behind Fable 5 on GDPval-AA v2 (1686 vs 1747) and 35 behind on AA-Briefcase (1548 vs 1583) — both wider than the 33-Elo open/closed chat gap The Open-Weight Frontier Gap recorded a month earlier.

The card's footnotes are the interesting part#

K3's evaluation footnotes are the most granular harness disclosure in this corpus, and they are unusual in a specific way: several of them work against Moonshot's own numbers. The card discloses that Fable 5 hit fallbacks on 35% of SWE-Marathon tasks "which may have negatively impacted its measured performance," that 13 of 80 Kimi Code Bench tasks fell back plus 1 refusal for Fable 5 (10 refusals for GPT-5.6 Sol, 3 for GPT-5.5), and that K3 scores higher on its own in-house benchmark under the rival Claude Code harness (73.7) than under Kimi Code (72.9) — reporting the lower figure. It also notes BrowseComp 91.2 uses 300K-token context compaction, against 90.4 with the full 1M window and no context management.

Against that: K3 is evaluated on the Kimi Code harness throughout while competitors are reported at "the best score across harnesses" or from leaderboards — harness-controlled, not compute-controlled, and asymmetrically so. See Compute-Controlled Benchmarking, where this card is now the corpus's third vendor half-defection from the benchmark grid.

K3 under someone else's harness (July 2026)#

Every number above is Moonshot's. The first outside result on K3 comes from Cline a few days after release (Agent-Authored Harness Optimization, case-study): running K3 through OpenRouter on Cline's general-purpose coding harness, a stock configuration scored 69/89 (77.5%) at $79 on Terminal-Bench 2.1, and after 17 hours of agent-authored harness patching — retry-on-429, output-aware loop detection, an async-worker liveness fix, PID-based process handling — 79/89 (88.8%) at $49.8. Cline claims that matches Moonshot's own vendor-reported SOTA of 88.3% on the same suite with a general-purpose harness rather than the Kimi Code harness.

Two reasons to hold this loosely and one reason it matters. It is a vendor benchmarking its own harness, self-reported and unreplicated, on a suite Cline has been optimizing against since January 2026; and the harness was tuned for 17 hours specifically against it, so the comparison to Fable 5's $552 and GPT-5.6 Terra's $400 is not harness-controlled (Compute-Controlled Benchmarking). What it does supply is the counterpart to the card's own most interesting footnote — that K3 scored higher on Kimi Code Bench under the rival Claude Code harness than under Kimi Code. Both point the same way: K3's agentic numbers are not harness-locked, and a third-party scaffold can reach the vendor's headline figure. It also strengthens the harness-vs-model caveat on the 45-benchmark table — an 11.3pp swing on a fixed model, from scaffold alone, is larger than most of the gaps the card reports between models.

What none of the above measures: whether anyone is buying it#

K3's card, Cline's harness campaign and the Arena table are all capability. Ramp's July 2026 AI Index (empirical, US corporate-card and bill-pay records) is the corpus's only reading on paid demand, and it has no Kimi line — Ramp cannot see individual models, so it counts firms paying model-serving and inference platforms as one proxy for all open-source and Chinese model access. That proxy reaches 5.8% of AI-spending US businesses in June 2026, up from 4.5% in January; the only vendor-level Chinese line in the whole dataset is DeepSeek's direct spend at 0.29%. So the ceiling on paid US business adoption of everything in this family is under 6%, and among the firms inside it 96.4% still pay OpenAI or Anthropic directly — currently additive, not substitutive. Hold the number loosely in both directions: the proxy also counts American open weights, and it is blind to self-hosting entirely. See The Open-Weight Frontier Gap.

The encoder that shouldn't be there#

K3 ships a 401M MoonViT-V2 vision encoder at 2.8T scale. Encoder-Free Early Fusion documents three instances of the opposite move — TML-Interaction-Small, Gemma 4's 12B, and Inkling at 975B — and K3 is a same-scale, same-year counter-instance from a fourth lab. It is also the strongest reported model in this corpus on the exact task family where the encoder-free 12B regressed (dense text in images: OmniDocBench 91.1, and an OfficeQA Pro setup in which the entire PDF corpus is supplied as images with no machine-readable text). Suggestive, not decisive — different scale, different lab, vendor-claim numbers — but it is the first evidence pointing the other way.

A competitor's placement of K3 (July 2026)#

Musk, asked about Chinese frontier labs in his 2026-07-29 Economist interview, singles K3 out: it "was almost as good as Fable" per the interviewer, and Musk's own correction is that Fable "is still clearly the smartest model… but the Kimi model is getting quite close." Worth almost nothing as a measurement — it is prediction-tier hearsay from someone who ships a rival model — but two framings from the same exchange are useful:

• He treats K3's efficiency as the headline, not its score. Chinese labs are "doing as well as they are with a relatively small amount of compute," so "if they had a lot of compute there's a good chance that they would be the leaders." His compute geography (Effective Compute Scaling) makes chip starvation the only thing holding it, and lithography the thing that ends it.
• He wants Chinese labs inside frontier-safety coordination, volunteering that any cross-lab arrangement "probably should include the Chinese companies that are doing frontier AI models too" — see Cross-Lab Pre-Release Review.

An unresolved contradiction inside the card#

§1 says K3 "understands text, images, and video within the same model." The §2 spec table says Modality | Text, Image. Video benchmarks are reported (Video-MME 90.0, MMVU 82.1). The card does not reconcile these; the likely explanation is video-as-sampled-frames through MoonViT-V2, but Moonshot does not say so.

Connections#

• The Open-Weight Frontier Gap — K2.6 sits in that page's Arena table; K3 is the largest open release it tracks, and the first to put agentic Elo numbers next to the chat-Elo gap
• Inference Efficiency as Capability — the 2.5×-scaling-efficiency claim, 3.7% activation sparsity, hybrid linear/full attention, and MXFP4 QAT, all as capability levers at the opposite scale pole from Gemma
• Compute-Controlled Benchmarking — the footnotes: per-model harness pinning, cross-harness cherry-picking for rivals, and self-disclosed grader-side confounds
• Encoder-Free Early Fusion — the counter-instance: a large vision encoder retained at frontier-open scale
• Open-Weight Elicitation Irreversibility — the largest open-weight release in the corpus, shipped with no safety section at all
• Capability-Gated Model Fallback — the card is the first third-party measurement of how often Fable 5's classifier fallback fires during ordinary technical benchmarks
• GLM (Z.AI) — the sibling Chinese open MoE line; GLM-5.2 is the comparison column K3 beats most consistently
• Inkling — bootstrapped its post-training on Kimi K2.5 synthetic data; the Western open MoE that declines the race Kimi is running
• Claude Fable 5 — the model K3 is benchmarked against, and the one the card's fallback footnotes are about
• Cline — the third party that ran K3 on a general-purpose harness and reports matching Moonshot's own Terminal-Bench 2.1 SOTA; also the ClinePass vendor that resells K3 inference by subscription
• Agent-Authored Harness Optimization — the campaign that produced the outside number, and the reasons to discount it

Open Questions#

• What does "2.5× scaling efficiency over K2" measure — loss at fixed FLOPs, benchmark score at fixed active parameters, or tokens per dollar? Moonshot reports a ratio with no definition, no baseline curve, and no ablation separating KDA from AttnRes from LatentMoE.
• K3 leads on retrieval and tool orchestration and trails on HLE, CritPt, FrontierSWE and OSWorld 2.0. Is that a durable division — sparse open MoEs buying breadth and throughput while closed models keep the hard-reasoning head — or an artifact of which harness each model was pinned to?
• 16 of 896 experts is the sparsest routing in this corpus by a wide margin (Inkling: 6 of 256; Gemma 4 26B-A4B: 3.8B of 26B dense-equivalent). Is there a sparsity ceiling, and does K3 sit near it?

Sources#

• Kimi K3 Model Card — Moonshot AI's Hugging Face model card for Kimi K3 (published 2026-07-26, vendor-claim): §1 key features and the 2.5× scaling-efficiency claim, §2 spec table, §3 the 45-benchmark evaluation table and its footnotes, §4 MXFP4 QAT, §6 reasoning-effort and preserved-thinking-history API contract, §7 the Kimi K3 License
• Ramp's latest data on China vs. the American AI Labs — Ara Kharazian, Ramp AI Index (2026-07-08, empirical, third-party): the model-serving-platform proxy (5.8% of AI-spending US businesses, up from 4.5% in January) and the 96.4% of those firms that also pay OpenAI or Anthropic. No Kimi-specific figure exists — Ramp has no per-model visibility and never names Moonshot; the DeepSeek 0.29% line comes from the raw file's recovered Datawrapper chart dataset, not the letter's prose. COI: Ramp's own VC-forward-skewed card customer base