Kimi K3 benchmarks, without the leaderboard fog
Kimi K3 benchmark results are strongest when they help you choose a test plan. They are weakest when a single percentage is treated as proof that every real task will work.
Published scores
The values below were displayed by OpenRouter from Artificial Analysis on July 17, 2026. Scores can change when evaluators revise datasets, prompts, or model endpoints.
Composite indexes
Reasoning and knowledge
Coding and reliability
What the scores suggest
GPQA Diamond and the long-context evaluation support Kimi K3’s position as a serious reasoning model. The Coding Index and SciCode result make it a credible candidate for repository analysis and scientific code. The Agentic Index suggests promise for multi-step workflows.
Those are model-selection signals, not deployment guarantees. A model can score well and still misunderstand a proprietary codebase, cite a nonexistent fact, or choose the wrong tool sequence.
How to evaluate Kimi K3
- 01Collect 20–50 real tasks from your own workflow.
- 02Define what a correct answer must include before testing.
- 03Measure accuracy, latency, output length, and total token cost.
- 04Review hallucinations separately from incomplete answers.
- 05Repeat the test after prompt or retrieval changes.
Source and interpretation
See the current provider card, methodology links, and updated values on OpenRouter’s Kimi K3 page. K3 Kimi does not present these third-party benchmark scores as its own testing.