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 | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $10.00Azure |
| — | $750Azure | |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Oct 30 2025 | Nov 19 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Kimi Linear and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi Linear shipped 20 days before GPT-5.1-Codex-Max, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Kimi Linear) vs 400k (GPT-5.1-Codex-Max). Kimi Linear is open weight, while GPT-5.1-Codex-Max is closed.
Direct benchmark comparisons are unavailable — Kimi Linear and GPT-5.1-Codex-Max don't publish scores on any of the same benchmarks.