Gemini 3 Flash vs Kimi K2: which model should you choose?
We compared Gemini 3 Flash and Kimi K2 on price, context window and 12 benchmarks. Gemini 3 Flash has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
Gemini 3 Flash
Gemini 3 Flash is a multimodal model from Google DeepMind. It offers a 128,000-token context window, costs $15.67 per million input tokens and $70.15 per million output tokens, and averages 83.6 across the index's benchmark suite.
- Input
- $15.67 / 1M
- Output
- $70.15 / 1M
- Context
- 128,000
- Avg score
- 83.6
- Open weights
- No
Kimi K2
Kimi K2 is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.85 per million input tokens and $21.72 per million output tokens, and averages 83.0 across the index's benchmark suite.
- Input
- $5.85 / 1M
- Output
- $21.72 / 1M
- Context
- 128,000
- Avg score
- 83.0
- Open weights
- Yes
Specification comparison
| Spec | Gemini 3 Flash | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $15.67 | $5.85 | B |
| Output price / 1M | $70.15 | $21.72 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 65,536 | 65,536 | Tie |
| Average score | 83.6 | 83.0 | A |
| Arena Elo | 1450.7 | 1458.5 | B |
| Open weights | No | Yes | B |
Benchmark comparison
Gemini 3 Flash wins 6, Kimi K2 wins 5, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 3 Flash | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.7 | 83.3 | 0.4 | Gemini 3 Flash |
| GPQA Diamond | 80.3 | 77.5 | 2.8 | Gemini 3 Flash |
| SWE-bench Verified | 72.5 | 70.2 | 2.3 | Gemini 3 Flash |
| HumanEval | 86.2 | 87.1 | 0.9 | Kimi K2 |
| MATH-500 | 89.0 | 85.3 | 3.7 | Gemini 3 Flash |
| AIME 2025 | 84.5 | 85.1 | 0.6 | Kimi K2 |
| LiveCodeBench | 79.3 | 78.5 | 0.8 | Gemini 3 Flash |
| IFEval | 91.0 | 91.5 | 0.5 | Kimi K2 |
| TruthfulQA | 84.8 | 84.8 | 0.0 | Tie |
| HellaSwag | 89.8 | 92.3 | 2.5 | Kimi K2 |
| MMMU | 78.2 | 77.5 | 0.7 | Gemini 3 Flash |
| Arena Elo | 1450.7 | 1458.5 | 7.8 | Kimi K2 |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 3 Flash costs about $29.29 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 66% cheaper than Gemini 3 Flash. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 3 Flash wins 6, Kimi K2 wins 5, and 1 are tied. On the index average, Gemini 3 Flash leads Kimi K2 by 0.6 points (83.6 versus 83.0).
The price picture favours Kimi K2. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Gemini 3 Flash for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Gemini 3 Flash if
- You want the higher average benchmark score in this index.
Choose Kimi K2 if
- Unit cost matters and you want the cheaper blended price.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is Gemini 3 Flash better than Kimi K2?
On the index average, Gemini 3 Flash leads Kimi K2 by 0.6 points (83.6 versus 83.0). The better choice depends on price, context and your task. Gemini 3 Flash wins 6 benchmarks, Kimi K2 wins 5.
Which is cheaper, Gemini 3 Flash or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $29.29 for Gemini 3 Flash.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
Gemini 3 Flash is proprietary; Kimi K2 is open weights.
Which should I choose for production?
If you need a single default, start with Gemini 3 Flash for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.