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. | — | 1T |
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 | 256k |
| 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. | — | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.50Google |
| — | $245Novita · bf16 | |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 71.3% |
| Overview | ||
| Company | Moonshot AI | |
| Release date | Feb 15 2024 | Nov 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
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Gemini 1.5 Pro and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.5 Pro shipped 630 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 1.5 Pro) vs 256k (Kimi K2 Thinking). Gemini 1.5 Pro is closed, while Kimi K2 Thinking is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.5 Pro and Kimi K2 Thinking don't publish scores on any of the same benchmarks.