Gemini 3.1 Pro Preview vs Kimi K2
Head-to-head specifications
| Metric | Gemini 3.1 Pro Preview | Kimi K2 | Difference |
|---|---|---|---|
| Intelligence Index | 46.0 | 24.0 | +91.7% |
| Context window | 1M tokens | 200K tokens | — |
| Blended price ($/1M tokens) | $0.91 | $0.51 | +78.4% |
| Output speed (tokens/s) | 117 | 35 | +234.3% |
| Access | Proprietary API | Open weights | — |
- Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 24.0).
- Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.8× cheaper.
- Gemini 3.1 Pro Preview offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Gemini 3.1 Pro Preview or Kimi K2?
Gemini 3.1 Pro Preview advantages
- General intelligence (+48%)
- Context window (+80%)
- Output speed (+70%)
Kimi K2 advantages
- Affordability (+44%)
Which should you choose?
- Choose the Gemini 3.1 Pro Preview if you need the strongest overall reasoning and accuracy.
- Choose the Kimi K2 if you want the lowest cost per token at scale.
- Choose the Gemini 3.1 Pro Preview if you work with long documents or large codebases.
Value for money
Gemini 3.1 Pro Preview offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemini 3.1 Pro Preview vs Kimi K2: which should you choose?
Gemini 3.1 Pro Preview — Google multimodal model with an Intelligence Index of 46, a 1M-token context window and a blended price of $0.91/1M tokens.
Kimi K2 — Moonshot AI text model with an Intelligence Index of 24, a 200K-token context window and a blended price of $0.51/1M tokens (open weights).
Gemini 3.1 Pro Preview vs Kimi K2: Gemini 3.1 Pro Preview scores higher on the Intelligence Index. Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 24.0). Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.8× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Gemini 3.1 Pro Preview scores 46.0 versus 24.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemini 3.1 Pro Preview 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 Pro Preview generates faster (117 vs 35 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Kimi K2 is the cheaper model to run ($0.51 vs $0.91 per 1M tokens). Gemini 3.1 Pro Preview is proprietary api and Kimi K2 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 Pro Preview better than the Kimi K2?
Gemini 3.1 Pro Preview takes the overall edge, though Kimi K2 wins in specific areas worth weighing. Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 24.0).
What is the main difference between the Gemini 3.1 Pro Preview and the Kimi K2?
Gemini 3.1 Pro Preview leads overall capability (Intelligence Index 46.0 vs 24.0). Kimi K2 is the cheaper model to run at $0.51/1M blended tokens — about 1.8× cheaper.
Which is better value?
Gemini 3.1 Pro Preview offers more intelligence per dollar (1.1× 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 Pro Preview if you need the strongest overall reasoning and accuracy. Choose the Kimi K2 if you want the lowest cost per token at scale.
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.