Kimi K3: A Forensic Analysis

Moonshot AI announced Kimi K3 on 2026-07-16. Open weights and the technical report are due 2026-07-27. Neither is published yet, so architecture figures below are vendor-stated. Each claim in this audit is marked Verified (independent measurement), Vendor-stated (awaiting inspection), or Not disclosed.

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Release facts

Item	Value	Status
Announcement	2026-07-16	Verified
Weights + technical report	Due 2026-07-27 on Hugging Face	Pending
Access today	API only (Moonshot platform, OpenRouter)	Verified
Variants	K3 Max; K3 Swarm Max (~300 sub-agents, ~4,000 steps)	Vendor-stated
Input price	$3.00 / M tokens; $0.30 cache hit	Verified
Output price	$15.00 / M tokens, reasoning tokens included	Verified
License	Unpublished	Not disclosed

Architecture

Spec	Value
Total parameters	2.8T
Active parameters	Not stated; ~50B community estimate
Experts	896 total, 16 active per token (1.8%)
Shared experts	Not stated
Routing	LatentMoE, Quantile Balancing
Attention	KDA (linear) + gated MLA (full); layer ratio not stated
Residual scheme	Attention Residuals: cross-layer retrieval
Context	1M tokens
Vision	Native input
Optimizer	Per-Head Muon
Precision	MXFP4 weights, MXFP8 activations, QAT from SFT stage
Checkpoint size	~1.4TB at MXFP4
Hidden size, layers, vocab, tokenizer, positional encoding	Not disclosed
Training tokens	Not disclosed; ~2.5x scaling efficiency vs K2 claimed

Figure 1. Vendor-described block structure. Most blocks use KDA (linear attention); a subset uses gated MLA (full attention). Depth and interleave ratio are not disclosed; the predecessor architecture Kimi-Linear used 3:1.

Figure 2. Vendor-described LatentMoE routing. Expert scoring runs in a projected latent space. 16 of 896 experts fire per token (1.8%). Shared-expert count is not stated.

Vendor-reported effects: KDA up to 6.3x faster decoding at 1M context; Attention Residuals ~25% training efficiency gain at <2% overhead.

Lineage

	K2 (Jul 2025)	K2.5 (Jan 2026)	K3 (Jul 2026)
Total parameters	1T	1T (1.1T with vision)	2.8T
Active per token	32B	~32B	Not stated (~50B est.)
Experts	8 of 384 + 1 shared	8 of 384	16 of 896
Attention	MLA	MLA	KDA + gated MLA
Context	128K	256K	1M
Vision	None	Native	Native
Optimizer	MuonClip	MuonClip lineage	Per-Head Muon
Native precision	BF16	INT4 QAT (Thinking)	MXFP4 QAT

Figure 3. K-series release timeline. The final entry is a commitment, not a release.

Benchmarks

Measurement	Vendor	Independent	Source
Artificial Analysis Intelligence Index	n/a	57 (#4 overall)	Artificial Analysis
Terminal-Bench 2.1	88.3	84.6	Artificial Analysis
Frontend Code Arena	n/a	#1, 1679 Elo	Arena.ai
Output speed	260 tok/s	62 tok/s	Artificial Analysis
Cost per completed task	n/a	$0.94	Artificial Analysis

• Index 57: highest recorded for an open-weight model. Level with Opus 4.8. Below Claude Fable 5 and GPT-5.6 Sol. Above GLM-5.2 (51) and DeepSeek V4 Pro (44).
• Frontend Code Arena #1: first first-place arena finish for a Chinese-developed model.
• Vendor table mixes four harnesses: Kimi Code, Claude Code, Codex, mini-SWE-agent. Rows are not directly comparable.
• Absent from vendor table: SWE-bench Verified, AIME, tau-bench.
• SWE-bench figures in circulation: 76.8% and 93.4% (Vals AI). Different harnesses. No same-harness reproduction published.
• Launch figures are upper bounds until reproduced on the target endpoint and harness.

Figure 4. Independent aggregate scores. K3 leads all open-weight models; closed frontier models remain ahead. Opus 4.8 value is approximate as published. Exact values are in the table above.

Hallucination vs K2.6 (AA-Omniscience)

Metric	K2.6	K3
Accuracy	33%	46%
Hallucination rate	39%	51%

More correct answers, and more incorrect assertions in place of declining. Moonshot documentation notes “overly proactive” ambiguity resolution. Test against your workload before unattended agentic use.

Cost

Item	Value
Measured verbosity	~2x median output tokens (Artificial Analysis)
Full AA evaluation cost	$2,690.80
Single SVG generation (Willison)	$0.25; 13,241 of 16,658 output tokens were reasoning
Reasoning budget exhaustion	4 of 20 OpenRouter completions returned no answer
Per-task comparison	K3 $0.94; GPT-5.6 Sol $1.04; Opus 4.8 $1.80; DeepSeek V4 Pro $0.04
Reasoning effort tiers	Max at launch; standard and high added after

New consumer subscriptions are suspended. Moonshot cites GPU constraints.

Hardware

Precision	Checkpoint size
MXFP4 (native)	~1.4TB
BF16	5.6TB
2-bit quantization (estimate)	650GB to 1TB

• Minimum deployment: ~64-accelerator scale-up domain (SemiAnalysis estimate). Exceeds one DGX B200 node at 4-bit.
• No GGUF or MLX conversions exist. No weights to convert.
• KDA and LatentMoE are not in stable vLLM or SGLang. A KDA vLLM implementation is reported to land with the weights.
• Local deployment: not feasible at this model size.

Harness contract

• K3 is trained with reasoning traces preserved across turns.
• Dropping or compacting thinking history degrades output quality (Moonshot statement).
• Officially supported harnesses: Kimi Code, Claude Code, OpenCode, Roo Code.
• Same weights produce different results under different context management. Background: the agentic harness.

Figure 5. The harness contract. K3 expects prior reasoning traces in context on later turns. The degraded path is Moonshot’s own characterization.

Claim ledger

Claim	Status
2.8T total, 16-of-896 MoE, KDA, 1M context	Vendor-stated
Active parameter count	Not disclosed
Highest open-weight score on an independent index	Verified
#1 Frontend Code Arena	Verified
Overall parity with closed frontier	Not shown; independent aggregates place K3 below Fable 5 and GPT-5.6 Sol
260 tok/s	Not reproduced; 62 tok/s measured
Hallucination change vs K2.6	Verified: 39% to 51%
Open weights and license	Unpublished; due 2026-07-27
Local deployment feasibility	Not feasible at stated size

Verification plan for the weights release

Figure 6. Audit state machine. Every vendor-stated claim moves to Verified or Discrepancy once the weights and report are inspectable.

Item	Test
Expert counts, attention layout, undisclosed dimensions	config.json vs launch blog
License terms	Published text vs K2 modified MIT
Checkpoint size	~1.4TB expected at MXFP4
Launch benchmarks	Fixed-harness reproduction
Training data provenance	Technical report data section

Provenance context: Anthropic’s February 2026 report states Moonshot harvested 3.4M+ Claude exchanges via fraudulent accounts. No public evidence connects that dataset to K3 training. Moonshot has not commented.

Results of this checklist will be published here.

Scope

• No evidence found that any Moonshot figure is wrong.
• No first-hand capability testing performed yet.
• This is an audit of public claims against public evidence, following the method from our JEPA validation.

Current independent standings: AI model matrix.

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