Gemini 3.1 Flash-Lite vs Kimi K2.6
Head-to-head specifications
| Metric | Gemini 3.1 Flash-Lite | Kimi K2.6 | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 35.0 | -20.0% |
| Coding Index | 34.7 | 61.8 | -43.9% |
| Agentic Index | 6.2 | 30.3 | — |
| Context window | 1M tokens | 300K tokens | — |
| Blended price ($/1M tokens) | $0.22 | $0.56 | -60.7% |
| Output speed (tokens/s) | 278 | 40 | +595.0% |
| Access | Proprietary API | Open weights | — |
- Kimi K2.6 leads overall capability (Intelligence Index 35.0 vs 28.0).
- Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 2.5× cheaper.
- Gemini 3.1 Flash-Lite offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Gemini 3.1 Flash-Lite or Kimi K2.6?
Gemini 3.1 Flash-Lite advantages
- Context window (+70%)
- Affordability (+61%)
- Output speed (+86%)
Kimi K2.6 advantages
- General intelligence (+20%)
- Coding ability (+44%)
- Agentic task performance (+80%)
Which should you choose?
- Choose the Gemini 3.1 Flash-Lite if you work with long documents or large codebases.
- Choose the Kimi K2.6 if you need the strongest overall reasoning and accuracy.
- Choose the Gemini 3.1 Flash-Lite if you want the lowest cost per token at scale.
Value for money
Gemini 3.1 Flash-Lite offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 3.1 Flash-Lite vs Kimi K2.6: which should you choose?
Gemini 3.1 Flash-Lite — Google multimodal model with an Intelligence Index of 28, a 1M-token context window and a blended price of $0.22/1M tokens.
Kimi K2.6 — Moonshot AI text model with an Intelligence Index of 35, a 300K-token context window and a blended price of $0.56/1M tokens (open weights).
Gemini 3.1 Flash-Lite vs Kimi K2.6: Kimi K2.6 scores higher on the Intelligence Index. Kimi K2.6 leads overall capability (Intelligence Index 35.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 2.5× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Kimi K2.6 scores 35.0 versus 28.0. For software development, the Coding Index puts Kimi K2.6 ahead (61.8 vs 34.7). On agentic, multi-step tool-use tasks, Kimi K2.6 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 3.1 Flash-Lite accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Gemini 3.1 Flash-Lite generates faster (278 vs 40 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Gemini 3.1 Flash-Lite is the cheaper model to run ($0.22 vs $0.56 per 1M tokens). Gemini 3.1 Flash-Lite is proprietary api and Kimi K2.6 is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Gemini 3.1 Flash-Lite better than the Kimi K2.6?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Kimi K2.6 leads overall capability (Intelligence Index 35.0 vs 28.0).
What is the main difference between the Gemini 3.1 Flash-Lite and the Kimi K2.6?
Kimi K2.6 leads overall capability (Intelligence Index 35.0 vs 28.0). Gemini 3.1 Flash-Lite is the cheaper model to run at $0.22/1M blended tokens — about 2.5× cheaper.
Which is better value?
Gemini 3.1 Flash-Lite offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Gemini 3.1 Flash-Lite if you work with long documents or large codebases. Choose the Kimi K2.6 if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.