Gemini 3.0 ProvsKimi K2
Gemini 3.0 Pro | Kimi K2 | |
|---|---|---|
| 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 | ||
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. | 76.2% | 65.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.9% | 75.1% |
| 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% |
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. | 62% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
| Overview | ||
| Company | Moonshot AI | |
| Release date | Nov 18 2025 | Jul 11 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Gemini 3.0 Pro leads Kimi K2 on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Kimi K2 shipped 130 days before Gemini 3.0 Pro, so benchmark comparisons should account for the intervening progress.
Gemini 3.0 Pro is proprietary, while Kimi K2 is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs Kimi K2 at 65.8%. On GPQA Diamond, Gemini 3.0 Pro leads at 91.9% vs Kimi K2 at 75.1%.
Gemini 3.0 Pro was released by Google on Nov 18 2025.
Kimi K2 was released by Moonshot AI on Jul 11 2025.
Gemini 3.0 Pro leads on SWE-Bench Verified — Gemini 3.0 Pro 76.2% vs Kimi K2 65.8%.
Gemini 3.0 Pro leads on GPQA Diamond — Gemini 3.0 Pro 91.9% vs Kimi K2 75.1%.
Gemini 3.0 Pro is a proprietary model released by Google. Kimi K2 is an open weight model released by Moonshot AI.