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 | 200k |
| 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. | $0.60 | $150.00 |
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. | $3.00 | $600.00 |
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. | $0.10 | — |
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. | $0.49AtlasCloud | $150.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.50AtlasCloud | $600.00OpenAI |
| $223AtlasCloud · int4 | $60,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | 52% | — |
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. | 76.8% | — |
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. | 16% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 85% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 60.6% | — |
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. | 30.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 87.6% | — |
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. | 63.3% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jan 27 2026 | Mar 19 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.5vsClaude Haiku 5.5o1-provsClaude Haiku 5.5Kimi K2.5vsGemini 4 Argono1-provsGemini 4 ArgonKimi K2.5vsMuse Spark Openo1-provsMuse Spark OpenKimi K2.5vsGrok 4.7o1-provsGrok 4.7Kimi K2.5vsDeepSeek-V4.1-Flash-1017o1-provsDeepSeek-V4.1-Flash-1017Kimi K2.5vsMistral Large 4o1-provsMistral Large 4Frequently asked questions
Kimi K2.5 and o1-pro don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K2.5 is cheaper on both input and output: $0.60 vs $150.00 per million input tokens, and $3.00 vs $600.00 per million output tokens. o1-pro shipped 314 days before Kimi K2.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.5) vs 200k (o1-pro). Kimi K2.5 is open weight, while o1-pro is closed.
Direct benchmark comparisons are unavailable — Kimi K2.5 and o1-pro don't publish scores on any of the same benchmarks.