Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 1T | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 256k | 128k |
| 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 | $10.00 |
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. | $4.00 | $30.00 |
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 third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.6712Inceptron | $10.00OpenAI |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $3.00StreamLake | $30.00OpenAI |
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 42.5% |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jun 12 2026 | Nov 6 2023 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5GPT-4 TurbovsClaude Haiku 5.5Kimi K2.7 CodevsGemini 4 ArgonGPT-4 TurbovsGemini 4 ArgonKimi K2.7 CodevsMuse Spark 1.3GPT-4 TurbovsMuse Spark 1.3Kimi K2.7 CodevsGrok 4.7GPT-4 TurbovsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-FlashGPT-4 TurbovsDeepSeek-V4.1-FlashKimi K2.7 CodevsMistral Large 4GPT-4 TurbovsMistral Large 4Frequently asked questions
Kimi K2.7 Code and GPT-4 Turbo don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K2.7 Code is cheaper on both input and output: $0.95 vs $10.00 per million input tokens, and $4.00 vs $30.00 per million output tokens. GPT-4 Turbo shipped 949 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.7 Code) vs 128k (GPT-4 Turbo). Kimi K2.7 Code is open weight, while GPT-4 Turbo is closed.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and GPT-4 Turbo don't publish scores on any of the same benchmarks.