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. | $0.95 | $1.75 |
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 | $14.00 |
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 | $0.175 |
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 | $1.75Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00CoreWeave | $14.00Azure |
| $270CoreWeave · int4 | $1,050OpenAI list price | |
| 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. | 74% | 68% |
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. | — | 24% |
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. | — | 85% |
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% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 64.7% |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | — | 1153 |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jun 12 2026 | Feb 5 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5GPT-5.3-CodexvsClaude Haiku 5.5Kimi K2.7 CodevsGemini 4 CarbonGPT-5.3-CodexvsGemini 4 CarbonKimi K2.7 CodevsMuse Spark OpenGPT-5.3-CodexvsMuse Spark OpenKimi K2.7 CodevsGrok 4.7GPT-5.3-CodexvsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-Flash-1017GPT-5.3-CodexvsDeepSeek-V4.1-Flash-1017Kimi K2.7 CodevsMistral Large 4GPT-5.3-CodexvsMistral Large 4Frequently asked questions
Kimi K2.7 Code leads GPT-5.3-Codex on 1 of the 1 benchmark they both report (Next.js Evals). Kimi K2.7 Code is cheaper on both input and output: $0.95 vs $1.75 per million input tokens, and $4.00 vs $14.00 per million output tokens. GPT-5.3-Codex shipped 127 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.7 Code) vs 400k (GPT-5.3-Codex). Kimi K2.7 Code is open weight, while GPT-5.3-Codex is closed.
On Next.js Evals, Kimi K2.7 Code leads at 74% vs GPT-5.3-Codex at 68%.