Kimi K2vso1
Kimi K2 | o1 | |
|---|---|---|
| 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. | 128k | 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. | — | $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. | — | $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. | — | $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. | $0.57Novita | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $2.30Novita | — |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.1% | 75.7% |
| 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. | 10% | — |
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. | 65.8% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jul 11 2025 | Dec 5 2024 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
o1 leads Kimi K2 on 1 of the 1 benchmark they both report (GPQA Diamond). Only o1 has a verified first-party API price: $15.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. o1 shipped 218 days before Kimi K2, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Kimi K2) vs 200k (o1). Kimi K2 is open weight, while o1 is proprietary.
On GPQA Diamond, o1 leads at 75.7% vs Kimi K2 at 75.1%.
Kimi K2 was released by Moonshot AI on Jul 11 2025.
o1 was released by OpenAI on Dec 5 2024.
o1 leads on GPQA Diamond — Kimi K2 75.1% vs o1 75.7%.
Only o1 has a verified first-party API price: $15.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. Rates are pay-as-you-go API prices verified on August 18, 2026.
Kimi K2 has a 128k context window; o1 has 200k.
Kimi K2 is an open weight model released by Moonshot AI. o1 is a proprietary model released by OpenAI.