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. | 128k | 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. | — | $150.00 |
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. | — | $600.00 |
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. | $0.57Novita | $150.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.30Novita | $600.00OpenAI |
| $229Novita · fp8 | $60,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 10% | — |
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. | 65.8% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 4.7% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.1% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jul 11 2025 | Mar 19 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2vsClaude Haiku 5.5o1-provsClaude Haiku 5.5Kimi K2vsGemini 4 Argono1-provsGemini 4 ArgonKimi K2vsMuse Spark Openo1-provsMuse Spark OpenKimi K2vsGrok 4.7o1-provsGrok 4.7Kimi K2vsDeepSeek-V4.1-Flash-1017o1-provsDeepSeek-V4.1-Flash-1017Kimi K2vsMistral Large 4o1-provsMistral Large 4Frequently asked questions
Kimi K2 and o1-pro don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1-pro has a verified first-party API price: $150.00 per million input tokens and $600.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. o1-pro shipped 114 days before Kimi K2, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Kimi K2) vs 200k (o1-pro). Kimi K2 is open weight, while o1-pro is closed.
Direct benchmark comparisons are unavailable — Kimi K2 and o1-pro don't publish scores on any of the same benchmarks.