Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 48B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 200k |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Oct 30 2025 | May 16 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi LinearvsClaude Fable 5.2codex-mini-latestvsClaude Fable 5.2Kimi LinearvsGemini 4 Carboncodex-mini-latestvsGemini 4 CarbonKimi LinearvsMuse Embercodex-mini-latestvsMuse EmberKimi LinearvsGrok 4.7codex-mini-latestvsGrok 4.7Kimi LinearvsDeepSeek-V4.1-Flash-1017codex-mini-latestvsDeepSeek-V4.1-Flash-1017Kimi LinearvsMistral Large 4codex-mini-latestvsMistral Large 4Frequently asked questions
Kimi Linear and codex-mini-latest don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. codex-mini-latest shipped 167 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Kimi Linear) vs 200k (codex-mini-latest). Kimi Linear is open weight, while codex-mini-latest is closed.
Direct benchmark comparisons are unavailable — Kimi Linear and codex-mini-latest don't publish scores on any of the same benchmarks.