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 | 1M |
| 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. | $0.95 | — |
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. | $4.00 | — |
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. | $0.19 | — |
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. | $0.6712Inceptron | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00StreamLake | — |
| $293StreamLake | — | |
| These models have no shared benchmark scores. | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 31% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 74% | — |
| Overview | ||
| Company | Moonshot AI | SpaceXSI |
| Release date | Jun 12 2026 | Feb 17 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5Grok 3vsClaude Haiku 5.5Kimi K2.7 CodevsGPT-6.1 SolGrok 3vsGPT-6.1 SolKimi K2.7 CodevsGemini 4 ArgonGrok 3vsGemini 4 ArgonKimi K2.7 CodevsMuse Spark OpenGrok 3vsMuse Spark OpenKimi K2.7 CodevsDeepSeek-V4.1-Flash-1017Grok 3vsDeepSeek-V4.1-Flash-1017Kimi K2.7 CodevsMistral Large 4Grok 3vsMistral Large 4Frequently asked questions
Kimi K2.7 Code and Grok 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Kimi K2.7 Code has a verified first-party API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Grok 3. Grok 3 shipped 480 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.7 Code) vs 1M (Grok 3). Kimi K2.7 Code is open weight, while Grok 3 is closed.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Grok 3 don't publish scores on any of the same benchmarks.