Kimi K2.5vso1-preview
Kimi K2.5 | o1-preview | |
|---|---|---|
| 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. | $0.60 | $15.00 |
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 | $60.00 |
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 | $7.50 |
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. | $0.45DeepInfra | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $2.25DeepInfra | — |
| Benchmarks | ||
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% | 73.3% |
| BenchmarksPublished by one model only | ||
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. | 19% | — |
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% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jan 27 2026 | Sep 12 2024 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
Kimi K2.5 leads o1-preview on 1 of the 1 benchmark they both report (GPQA Diamond). Kimi K2.5 is cheaper on both input and output: $0.60 vs $15.00 per million input tokens, and $3.00 vs $60.00 per million output tokens. o1-preview shipped 502 days before Kimi K2.5, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.5) vs 128k (o1-preview). Kimi K2.5 is open weight, while o1-preview is proprietary.
On GPQA Diamond, Kimi K2.5 leads at 87.6% vs o1-preview at 73.3%.
Kimi K2.5 was released by Moonshot AI on Jan 27 2026.
o1-preview was released by OpenAI on Sep 12 2024.
Kimi K2.5 leads on GPQA Diamond — Kimi K2.5 87.6% vs o1-preview 73.3%.
Kimi K2.5 is cheaper on both input and output: $0.60 vs $15.00 per million input tokens, and $3.00 vs $60.00 per million output tokens. Rates are pay-as-you-go API prices verified on August 18, 2026.
Kimi K2.5 has a 256k context window; o1-preview has 128k.
Kimi K2.5 is an open weight model released by Moonshot AI. o1-preview is a proprietary model released by OpenAI.