Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 1T | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 256k | 200k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $15.00 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $60.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $7.50 |
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 | $15.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.50Google | $60.00OpenAI |
| $245Novita · bf16 | $6,000OpenAI list price | |
| 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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.7% |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Nov 6 2025 | Dec 5 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
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Kimi K2 Thinking and o1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1 has a verified first-party API price: $15.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2 Thinking. o1 shipped 336 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2 Thinking) vs 200k (o1). Kimi K2 Thinking is open weight, while o1 is closed.
Direct benchmark comparisons are unavailable — Kimi K2 Thinking and o1 don't publish scores on any of the same benchmarks.