Gemini 2.5 Pro Turbo vs Kimi K2: which model should you choose?
We compared Gemini 2.5 Pro Turbo and Kimi K2 on price, context window and 12 benchmarks. Gemini 2.5 Pro Turbo has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
Gemini 2.5 Pro Turbo
Gemini 2.5 Pro Turbo is a frontier model from Google DeepMind. It offers a 1,000,000-token context window, costs $16.32 per million input tokens and $72.61 per million output tokens, and averages 83.9 across the index's benchmark suite.
- Input
- $16.32 / 1M
- Output
- $72.61 / 1M
- Context
- 1,000,000
- Avg score
- 83.9
- 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 2.5 Pro Turbo | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $16.32 | $5.85 | B |
| Output price / 1M | $72.61 | $21.72 | B |
| Context window | 1,000,000 | 128,000 | A |
| Max output | 128,000 | 65,536 | A |
| Average score | 83.9 | 83.0 | A |
| Arena Elo | 1464.0 | 1458.5 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Gemini 2.5 Pro Turbo wins 8, Kimi K2 wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 2.5 Pro Turbo | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 82.9 | 83.3 | 0.4 | Kimi K2 |
| GPQA Diamond | 81.7 | 77.5 | 4.2 | Gemini 2.5 Pro Turbo |
| SWE-bench Verified | 71.4 | 70.2 | 1.2 | Gemini 2.5 Pro Turbo |
| HumanEval | 88.1 | 87.1 | 1.0 | Gemini 2.5 Pro Turbo |
| MATH-500 | 90.6 | 85.3 | 5.3 | Gemini 2.5 Pro Turbo |
| AIME 2025 | 83.7 | 85.1 | 1.4 | Kimi K2 |
| LiveCodeBench | 78.6 | 78.5 | 0.1 | Gemini 2.5 Pro Turbo |
| IFEval | 90.7 | 91.5 | 0.8 | Kimi K2 |
| TruthfulQA | 85.9 | 84.8 | 1.1 | Gemini 2.5 Pro Turbo |
| HellaSwag | 91.2 | 92.3 | 1.1 | Kimi K2 |
| MMMU | 78.4 | 77.5 | 0.9 | Gemini 2.5 Pro Turbo |
| Arena Elo | 1464.0 | 1458.5 | 5.5 | Gemini 2.5 Pro Turbo |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 2.5 Pro Turbo costs about $30.39 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 68% cheaper than Gemini 2.5 Pro Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Gemini 2.5 Pro Turbo has the larger context window at 1,000,000 tokens, compared with 128,000 for Kimi K2. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 2.5 Pro Turbo wins 8, Kimi K2 wins 4, and 0 are tied. On the index average, Gemini 2.5 Pro Turbo leads Kimi K2 by 0.9 points (83.9 versus 83.0).
The price picture favours Kimi K2. The context picture favours Gemini 2.5 Pro Turbo. 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 2.5 Pro Turbo 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 2.5 Pro Turbo if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
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 2.5 Pro Turbo better than Kimi K2?
On the index average, Gemini 2.5 Pro Turbo leads Kimi K2 by 0.9 points (83.9 versus 83.0). The better choice depends on price, context and your task. Gemini 2.5 Pro Turbo wins 8 benchmarks, Kimi K2 wins 4.
Which is cheaper, Gemini 2.5 Pro Turbo or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $30.39 for Gemini 2.5 Pro Turbo.
Which has the larger context window?
Gemini 2.5 Pro Turbo has the larger context window at 1,000,000 tokens. Kimi K2 has 128,000 tokens.
Are these models open weights?
Gemini 2.5 Pro Turbo is proprietary; Kimi K2 is open weights.
Which should I choose for production?
If you need a single default, start with Gemini 2.5 Pro Turbo 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.