DeepSeek-R1 Base vs Doubao 1.5 Pro: which model should you choose?
We compared DeepSeek-R1 Base and Doubao 1.5 Pro on price, context window and 12 benchmarks. DeepSeek-R1 Base has the stronger average benchmark score; DeepSeek-R1 Base is cheaper on a blended 3:1 basis.
DeepSeek-R1 Base
DeepSeek-R1 Base is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.05 per million input tokens and $26.24 per million output tokens, and averages 80.1 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $26.24 / 1M
- Context
- 400,000
- Avg score
- 80.1
- Open weights
- Yes
Doubao 1.5 Pro
Doubao 1.5 Pro is a multimodal model from ByteDance. It offers a 200,000-token context window, costs $13.65 per million input tokens and $48.98 per million output tokens, and averages 79.7 across the index's benchmark suite.
- Input
- $13.65 / 1M
- Output
- $48.98 / 1M
- Context
- 200,000
- Avg score
- 79.7
- Open weights
- No
Specification comparison
| Spec | DeepSeek-R1 Base | Doubao 1.5 Pro | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $13.65 | A |
| Output price / 1M | $26.24 | $48.98 | A |
| Context window | 400,000 | 200,000 | A |
| Max output | 65,536 | 128,000 | B |
| Average score | 80.1 | 79.7 | A |
| Arena Elo | 1442.9 | 1428.3 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-R1 Base wins 9, Doubao 1.5 Pro wins 3, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | Doubao 1.5 Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 82.8 | 1.8 | Doubao 1.5 Pro |
| GPQA Diamond | 76.2 | 73.1 | 3.1 | DeepSeek-R1 Base |
| SWE-bench Verified | 71.2 | 70.4 | 0.8 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 82.6 | 0.8 | DeepSeek-R1 Base |
| MATH-500 | 84.9 | 84.4 | 0.5 | DeepSeek-R1 Base |
| AIME 2025 | 73.8 | 75.2 | 1.4 | Doubao 1.5 Pro |
| LiveCodeBench | 76.0 | 73.7 | 2.3 | DeepSeek-R1 Base |
| IFEval | 88.2 | 85.5 | 2.7 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 81.6 | 0.6 | DeepSeek-R1 Base |
| HellaSwag | 90.0 | 88.9 | 1.1 | DeepSeek-R1 Base |
| MMMU | 74.2 | 79.0 | 4.8 | Doubao 1.5 Pro |
| Arena Elo | 1442.9 | 1428.3 | 14.6 | DeepSeek-R1 Base |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and Doubao 1.5 Pro costs about $22.48. DeepSeek-R1 Base is roughly 54% cheaper than Doubao 1.5 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Base has the larger context window at 400,000 tokens, compared with 200,000 for Doubao 1.5 Pro. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 9, Doubao 1.5 Pro wins 3, and 0 are tied. On the index average, DeepSeek-R1 Base leads Doubao 1.5 Pro by 0.4 points (80.1 versus 79.7).
The price picture favours DeepSeek-R1 Base. The context picture favours DeepSeek-R1 Base. 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 DeepSeek-R1 Base for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 Base if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need the larger 400,000-token context window.
- You need open weights for self-hosting or fine-tuning.
Choose Doubao 1.5 Pro if
- You need the larger 128,000-token output limit.
FAQ
Is DeepSeek-R1 Base better than Doubao 1.5 Pro?
On the index average, DeepSeek-R1 Base leads Doubao 1.5 Pro by 0.4 points (80.1 versus 79.7). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 9 benchmarks, Doubao 1.5 Pro wins 3.
Which is cheaper, DeepSeek-R1 Base or Doubao 1.5 Pro?
DeepSeek-R1 Base is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $22.48 for Doubao 1.5 Pro.
Which has the larger context window?
DeepSeek-R1 Base has the larger context window at 400,000 tokens. Doubao 1.5 Pro has 200,000 tokens.
Are these models open weights?
DeepSeek-R1 Base is open weights; Doubao 1.5 Pro is proprietary.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 Base for quality-sensitive work, and check whether its price holds at your expected volume before committing.