Kimi K3: A Forensic Analysis

Moonshot AI announced Kimi K3 on 2026-07-16, a 2.8-trillion-parameter mixture-of-experts model with 896 total experts and 16 active per token, achieving an Artificial Analysis Intelligence Index score of 57 (#4 overall) and a #1 Frontend Code Arena Elo of 1679, the first first-place arena finish for a Chinese-developed model. Open weights and the technical report are due 2026-07-27 on Hugging Face; pricing is set at $3.00 per million input tokens and $15.00 per million output tokens, with cache hits at $0.30 per million tokens.

Kimi K3: A Forensic Analysis A claim-by-claim audit of Kimi K3: architecture, benchmarks, pricing, and hardware. 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 . 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$0.30 cache hit | Verified | | Output price | $15.00 / M tokens, reasoning tokens included | Verified | | License | Unpublished | Not disclosed | Architecture | Spec | Value | |---|---| | Total parameters | 2.8T | | Active parameters | Not stated; ~50B community estimate | | Experts |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 /blog/the-agentic-harness-architecture/ . 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 /blog/what-we-learned-validating-jepa/ . Current independent standings: AI model matrix /research/ai-models/ . Veso AI evaluates AI systems on evidence. Assessing K3 for your stack? Get in touch.