Gemini 2.5 Pro Turbo vs Kimi K2 Instruct: which model should you choose?
We compared Gemini 2.5 Pro Turbo and Kimi K2 Instruct on price, context window and 12 benchmarks. Gemini 2.5 Pro Turbo has the stronger average benchmark score; Kimi K2 Instruct 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 Instruct
Kimi K2 Instruct is a multimodal model from Moonshot AI. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $23.99 per million output tokens, and averages 82.3 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $23.99 / 1M
- Context
- 1,000,000
- Avg score
- 82.3
- Open weights
- Yes
Specification comparison
| Spec | Gemini 2.5 Pro Turbo | Kimi K2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $16.32 | $5.80 | B |
| Output price / 1M | $72.61 | $23.99 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 128,000 | 65,536 | A |
| Average score | 83.9 | 82.3 | A |
| Arena Elo | 1464.0 | 1458.6 | A |
| Open weights | No | Yes | B |
Benchmark comparison
Gemini 2.5 Pro Turbo wins 10, Kimi K2 Instruct wins 2, 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 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 82.9 | 86.5 | 3.6 | Kimi K2 Instruct |
| GPQA Diamond | 81.7 | 78.4 | 3.3 | Gemini 2.5 Pro Turbo |
| SWE-bench Verified | 71.4 | 75.1 | 3.7 | Kimi K2 Instruct |
| HumanEval | 88.1 | 87.3 | 0.8 | Gemini 2.5 Pro Turbo |
| MATH-500 | 90.6 | 88.0 | 2.6 | Gemini 2.5 Pro Turbo |
| AIME 2025 | 83.7 | 77.3 | 6.4 | Gemini 2.5 Pro Turbo |
| LiveCodeBench | 78.6 | 74.1 | 4.5 | Gemini 2.5 Pro Turbo |
| IFEval | 90.7 | 87.5 | 3.2 | Gemini 2.5 Pro Turbo |
| TruthfulQA | 85.9 | 82.8 | 3.1 | Gemini 2.5 Pro Turbo |
| HellaSwag | 91.2 | 90.2 | 1.0 | Gemini 2.5 Pro Turbo |
| MMMU | 78.4 | 78.3 | 0.1 | Gemini 2.5 Pro Turbo |
| Arena Elo | 1464.0 | 1458.6 | 5.4 | 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 Instruct costs about $10.35. Kimi K2 Instruct is roughly 66% 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.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 2.5 Pro Turbo wins 10, Kimi K2 Instruct wins 2, and 0 are tied. On the index average, Gemini 2.5 Pro Turbo leads Kimi K2 Instruct by 1.6 points (83.9 versus 82.3).
The price picture favours Kimi K2 Instruct. 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 2.5 Pro Turbo for quality-sensitive work and Kimi K2 Instruct 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 128,000-token output limit.
Choose Kimi K2 Instruct 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 Instruct?
On the index average, Gemini 2.5 Pro Turbo leads Kimi K2 Instruct by 1.6 points (83.9 versus 82.3). The better choice depends on price, context and your task. Gemini 2.5 Pro Turbo wins 10 benchmarks, Kimi K2 Instruct wins 2.
Which is cheaper, Gemini 2.5 Pro Turbo or Kimi K2 Instruct?
Kimi K2 Instruct is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $30.39 for Gemini 2.5 Pro Turbo.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
Gemini 2.5 Pro Turbo is proprietary; Kimi K2 Instruct 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 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.