Kimi K2vso3-mini
Kimi K2 | o3-mini | |
|---|---|---|
| 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 | — |
| 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. | — | $1.10 |
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. | — | $4.40 |
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. | — | $0.55 |
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 | ||
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% | 49.3% |
| 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. | 10% | — |
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% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jul 11 2025 | Jan 31 2025 |
| Access | Open Weight | Proprietary |
Other comparisons
Frequently asked questions
Kimi K2 leads o3-mini on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only o3-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. o3-mini shipped 161 days before Kimi K2, so benchmark comparisons should account for the intervening progress.
Kimi K2 is open weight, while o3-mini is proprietary.
On SWE-Bench Verified, Kimi K2 leads at 65.8% vs o3-mini at 49.3%.
Kimi K2 was released by Moonshot AI on Jul 11 2025.
o3-mini was released by OpenAI on Jan 31 2025.
Kimi K2 leads on SWE-Bench Verified — Kimi K2 65.8% vs o3-mini 49.3%.
Only o3-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 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 is an open weight model released by Moonshot AI. o3-mini is a proprietary model released by OpenAI.