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 | — |
| 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. | $0.60CoreWeave | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00CoreWeave | — |
| $270CoreWeave · int4 | — | |
| Benchmarks | ||
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. | 75% | 57% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 73.7% |
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% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 61.7% |
| Overview | ||
| Company | Moonshot AI | SpaceXSI |
| Release date | Jun 12 2026 | Mar 19 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Fable 5.2Composer 2vsClaude Fable 5.2Kimi K2.7 CodevsGPT-6.1 TerraComposer 2vsGPT-6.1 TerraKimi K2.7 CodevsGemini 4 HydrogenComposer 2vsGemini 4 HydrogenKimi K2.7 CodevsMuse EmberComposer 2vsMuse EmberKimi K2.7 CodevsDeepSeek-V4.1-Flash-1017Composer 2vsDeepSeek-V4.1-Flash-1017Kimi K2.7 CodevsMistral Large 4Composer 2vsMistral Large 4Frequently asked questions
Kimi K2.7 Code leads Composer 2 on 1 of the 1 benchmark they both report (Next.js Evals). Only Kimi K2.7 Code has a verified first-party price for an available API: $0.95 per million input tokens and $4.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Composer 2. Composer 2 shipped 85 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code is open weight, while Composer 2 is closed.
On Next.js Evals, Kimi K2.7 Code leads at 75% vs Composer 2 at 57%.