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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.25 | — |
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. | $1.50 | — |
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. | $0.025 | — |
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. | $0.125Google AI Studio | $0.60Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.75Google AI Studio | $2.50Novita |
| $125Google list price | $245Novita · fp8 | |
| 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. | 11% | — |
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% |
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 | |
| Release date | Mar 3 2026 | Sep 5 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.1 Flash-LitevsClaude Haiku 5.5Kimi K2 (0905)vsClaude Haiku 5.5Gemini 3.1 Flash-LitevsGPT-6.1 SolKimi K2 (0905)vsGPT-6.1 SolGemini 3.1 Flash-LitevsMuse Spark OpenKimi K2 (0905)vsMuse Spark OpenGemini 3.1 Flash-LitevsGrok 4.7Kimi K2 (0905)vsGrok 4.7Gemini 3.1 Flash-LitevsDeepSeek-V4.1-Flash-1017Kimi K2 (0905)vsDeepSeek-V4.1-Flash-1017Gemini 3.1 Flash-LitevsMistral Large 4Kimi K2 (0905)vsMistral Large 4Frequently asked questions
Gemini 3.1 Flash-Lite and Kimi K2 (0905) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 3.1 Flash-Lite has a verified first-party API price: $0.25 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2 (0905). Kimi K2 (0905) shipped 179 days before Gemini 3.1 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 3.1 Flash-Lite) vs 256k (Kimi K2 (0905)). Gemini 3.1 Flash-Lite is closed, while Kimi K2 (0905) is open weight.
Direct benchmark comparisons are unavailable — Gemini 3.1 Flash-Lite and Kimi K2 (0905) don't publish scores on any of the same benchmarks.