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.

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