Kimi K1.5 Instruct vs Sonar Pro Max: which model should you choose?
We compared Kimi K1.5 Instruct and Sonar Pro Max on price, context window and 12 benchmarks. Sonar Pro Max has the stronger average benchmark score; Kimi K1.5 Instruct is cheaper on a blended 3:1 basis.
Kimi K1.5 Instruct
Kimi K1.5 Instruct is a multimodal model from Moonshot AI. It offers a 200,000-token context window, costs $5.15 per million input tokens and $28.20 per million output tokens, and averages 80.5 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $28.20 / 1M
- Context
- 200,000
- Avg score
- 80.5
- Open weights
- Yes
Sonar Pro Max
Sonar Pro Max is a multimodal model from Perplexity. It offers a 128,000-token context window, costs $14.01 per million input tokens and $69.33 per million output tokens, and averages 81.1 across the index's benchmark suite.
- Input
- $14.01 / 1M
- Output
- $69.33 / 1M
- Context
- 128,000
- Avg score
- 81.1
- Open weights
- No
Specification comparison
| Spec | Kimi K1.5 Instruct | Sonar Pro Max | Edge |
|---|---|---|---|
| Input price / 1M | $5.15 | $14.01 | A |
| Output price / 1M | $28.20 | $69.33 | A |
| Context window | 200,000 | 128,000 | A |
| Max output | 4,096 | 4,096 | Tie |
| Average score | 80.5 | 81.1 | B |
| Arena Elo | 1431.8 | 1437.8 | B |
| Open weights | Yes | No | A |
Benchmark comparison
Kimi K1.5 Instruct wins 4, Sonar Pro Max wins 8, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Kimi K1.5 Instruct | Sonar Pro Max | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.0 | 83.7 | 0.7 | Sonar Pro Max |
| GPQA Diamond | 77.4 | 78.8 | 1.4 | Sonar Pro Max |
| SWE-bench Verified | 67.8 | 71.7 | 3.9 | Sonar Pro Max |
| HumanEval | 84.0 | 85.1 | 1.1 | Sonar Pro Max |
| MATH-500 | 82.6 | 86.2 | 3.6 | Sonar Pro Max |
| AIME 2025 | 80.4 | 74.9 | 5.5 | Kimi K1.5 Instruct |
| LiveCodeBench | 71.1 | 75.7 | 4.6 | Sonar Pro Max |
| IFEval | 86.2 | 88.3 | 2.1 | Sonar Pro Max |
| TruthfulQA | 84.3 | 82.0 | 2.3 | Kimi K1.5 Instruct |
| HellaSwag | 90.2 | 88.0 | 2.2 | Kimi K1.5 Instruct |
| MMMU | 78.6 | 77.2 | 1.4 | Kimi K1.5 Instruct |
| Arena Elo | 1431.8 | 1437.8 | 6.0 | Sonar Pro Max |
Price analysis
On a blended 3:1 input-to-output basis, Kimi K1.5 Instruct costs about $10.91 per million tokens and Sonar Pro Max costs about $27.84. Kimi K1.5 Instruct is roughly 61% cheaper than Sonar Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Kimi K1.5 Instruct has the larger context window at 200,000 tokens, compared with 128,000 for Sonar Pro Max. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Kimi K1.5 Instruct wins 4, Sonar Pro Max wins 8, and 0 are tied. On the index average, Sonar Pro Max leads Kimi K1.5 Instruct by 0.6 points (81.1 versus 80.5).
The price picture favours Kimi K1.5 Instruct. The context picture favours Kimi K1.5 Instruct. 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 Sonar Pro Max for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Kimi K1.5 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 200,000-token context window.
- You need open weights for self-hosting or fine-tuning.
Choose Sonar Pro Max if
- You want the higher average benchmark score in this index.
FAQ
Is Kimi K1.5 Instruct better than Sonar Pro Max?
On the index average, Sonar Pro Max leads Kimi K1.5 Instruct by 0.6 points (81.1 versus 80.5). The better choice depends on price, context and your task. Kimi K1.5 Instruct wins 4 benchmarks, Sonar Pro Max wins 8.
Which is cheaper, Kimi K1.5 Instruct or Sonar Pro Max?
Kimi K1.5 Instruct is cheaper on a blended 3:1 basis at about $10.91 per million tokens, versus $27.84 for Sonar Pro Max.
Which has the larger context window?
Kimi K1.5 Instruct has the larger context window at 200,000 tokens. Sonar Pro Max has 128,000 tokens.
Are these models open weights?
Kimi K1.5 Instruct is open weights; Sonar Pro Max is proprietary.
Which should I choose for production?
If you need a single default, start with Sonar Pro Max for quality-sensitive work and Kimi K1.5 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.