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. | 1M | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.10 | $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. | $0.50 | $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. | $0.01 | $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 |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $3.00StreamLake |
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% |
FrontierCode v1.1 (Main) · main splitAgentic coding — A set of very hard, frontier-difficulty coding tasks an AI agent has to complete end to end. The score is the share of tasks in the main split it solves. Higher is better. | 46.4% | — |
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% |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 39.2% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 45.9% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 57.4% | — |
OSWorld 2.1 (offline)Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | 72.4% | — |
GDPval-AA v2.1Knowledge work — economically valuable knowledge work (v2.1, Crowd-BT Elo fit) | 1620 | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1578 | — |
Chartography · no toolsChart tasks — The same chart-centred test with no tools: the AI has to read each chart unaided. Scores run far lower than the with-tools version, so read the two as separate tests. Higher is better. | 46.4% | — |
| Overview | ||
| Company | Anthropic | Moonshot AI |
| Release date | Oct 7 2026 | Jun 12 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Haiku 5.5vsGPT-6.1 SolKimi K2.7 CodevsGPT-6.1 SolClaude Haiku 5.5vsGemini 4 ArgonKimi K2.7 CodevsGemini 4 ArgonClaude Haiku 5.5vsMuse Spark 1.3Kimi K2.7 CodevsMuse Spark 1.3Claude Haiku 5.5vsGrok 4.7Kimi K2.7 CodevsGrok 4.7Claude Haiku 5.5vsDeepSeek-V4.1-FlashKimi K2.7 CodevsDeepSeek-V4.1-FlashClaude Haiku 5.5vsMistral Large 4Kimi K2.7 CodevsMistral Large 4Frequently asked questions
Claude Haiku 5.5 and Kimi K2.7 Code don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude Haiku 5.5 is cheaper on both input and output: $0.10 vs $0.95 per million input tokens, and $0.50 vs $4.00 per million output tokens. Figures are base-tier rates. Kimi K2.7 Code shipped 117 days before Claude Haiku 5.5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Haiku 5.5) vs 256k (Kimi K2.7 Code). Claude Haiku 5.5 is closed, while Kimi K2.7 Code is open weight.
Direct benchmark comparisons are unavailable — Claude Haiku 5.5 and Kimi K2.7 Code don't publish scores on any of the same benchmarks.