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. | 256k | 400k |
| 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. | — | $1.25 |
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. | — | $10.00 |
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. | — | $0.125 |
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 | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.50Google | $10.00Azure |
| $245Novita · bf16 | $750OpenAI 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. | — | 21% |
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 | OpenAI |
| Release date | Nov 6 2025 | Aug 7 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2 ThinkingvsClaude Haiku 5.5GPT-5vsClaude Haiku 5.5Kimi K2 ThinkingvsGemini 4 ArgonGPT-5vsGemini 4 ArgonKimi K2 ThinkingvsMuse Spark OpenGPT-5vsMuse Spark OpenKimi K2 ThinkingvsGrok 4.7GPT-5vsGrok 4.7Kimi K2 ThinkingvsDeepSeek-V4.1-Flash-1017GPT-5vsDeepSeek-V4.1-Flash-1017Kimi K2 ThinkingvsMistral Large 4GPT-5vsMistral Large 4Frequently asked questions
Kimi K2 Thinking and GPT-5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5 has a verified first-party API price: $1.25 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2 Thinking. GPT-5 shipped 91 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2 Thinking) vs 400k (GPT-5). Kimi K2 Thinking is open weight, while GPT-5 is closed.
Direct benchmark comparisons are unavailable — Kimi K2 Thinking and GPT-5 don't publish scores on any of the same benchmarks.