Moonshot AI Ships Kimi K3: 2.8T Open-Weight Multimodal Model at $3/$15 Per Million
Moonshot AI launched Kimi K3 on July 16, making it immediately available via API through providers including OpenRouter. The model is 2.8 trillion parameters, open-weight, and multimodal, built around an architecture that uses KDA (Key-value Decomposed Attention) and Attention Residuals for computational efficiency.
What K3 Is Built For
K3 targets the specific failure modes of frontier coding agents: navigating large repositories, chaining tool calls across multiple steps, debugging against images and runtime logs, and iterating across test and feedback cycles. The architecture choices — KDA and Attention Residuals — are explicitly aimed at reducing the KV-cache overhead that causes performance degradation on long agentic runs.
The 1M-token context window gives K3 room to hold entire codebases in context. That matters for repository-level tasks where retrieving the right file at step 40 of a 60-step agent run determines whether the job completes.
Pricing and Positioning
| Metric | Kimi K3 | Kimi K2.7-Code | Claude Opus 4.8 |
|---|---|---|---|
| Input ($/M) | $3.00 | $4.00 | $15.00 |
| Output ($/M) | $15.00 | $4.00 | $75.00 |
| Context | 1M | 1M | 200K |
| Parameters | 2.8T | undisclosed | undisclosed |
K3 positions as a frontier-class reasoning model at roughly one-fifth the output cost of Opus 4.8. That gap will be the benchmark story: if K3 matches Opus 4.8 on agentic coding benchmarks, the cost differential becomes a structural argument for open-weight models in enterprise agent deployments.
Benchmark Expectations
K3 was expected, before launch, to match or surpass Anthropic’s Opus 4.8 on frontier benchmarks — according to pre-launch reporting citing anonymous sources familiar with Moonshot’s internal evaluations. Specific SWE-bench Verified, Agent Arena, or Terminal-Bench numbers have not been published at launch.
The K2 line preceded K3 with strong credentials: Kimi K2.6 hit 80.2% on SWE-bench Verified and 96.0% on tau2-bench Telecom, putting it in Opus 4.7 territory on agentic evals. Kimi K2.7-Code scored 81% on MCP Mark Verified and was the first open-weight model accepted into GitHub Copilot’s model picker. K3 at 2.8T parameters is a substantially larger system.
The Open-Weight Equation
Kimi K3 joins a small group of frontier-scale open-weight models — alongside DeepSeek V4 Pro (1.6T params, Apache 2.0) and GLM-5.2 (744B at 2-bit quantisation) — that are compressing the performance gap with closed API models. The prior Kimi K2.7-Code was already in GitHub Copilot, meaning K3’s open weights land in a market with proven enterprise distribution.
Moonshot AI is expected to publish full evaluation numbers, including SWE-bench and agentic benchmark results, in the days following launch.