Gemini 3.1 Flash-LitevsKimi K2
Gemini 3.1 Flash-Lite | 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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.25 | — |
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. | $1.50 | — |
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. | $0.025 | — |
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 | ||
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. | 11% | 10% |
| BenchmarksPublished by one model only | ||
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% |
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 | |
| Release date | Mar 3 2026 | Jul 11 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Gemini 3.1 Flash-Lite leads Kimi K2 on 1 of the 1 benchmark they both report (BullshitBench v2). Only Gemini 3.1 Flash-Lite has a verified first-party API price: $0.25 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for Kimi K2. Kimi K2 shipped 235 days before Gemini 3.1 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Gemini 3.1 Flash-Lite is proprietary, while Kimi K2 is open weight.
On BullshitBench v2, Gemini 3.1 Flash-Lite leads at 11% vs Kimi K2 at 10%.
Gemini 3.1 Flash-Lite was released by Google on Mar 3 2026.
Kimi K2 was released by Moonshot AI on Jul 11 2025.
Only Gemini 3.1 Flash-Lite has a verified first-party API price: $0.25 per million input tokens and $1.50 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.
Gemini 3.1 Flash-Lite is a proprietary model released by Google. Kimi K2 is an open weight model released by Moonshot AI.