DeepSeek-R1 Base vs ERNIE 4.5 Pro: which model should you choose?
We compared DeepSeek-R1 Base and ERNIE 4.5 Pro on price, context window and 12 benchmarks. ERNIE 4.5 Pro 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
ERNIE 4.5 Pro
ERNIE 4.5 Pro is a multimodal model from Baidu. It offers a 1,000,000-token context window, costs $13.89 per million input tokens and $65.19 per million output tokens, and averages 80.5 across the index's benchmark suite.
- Input
- $13.89 / 1M
- Output
- $65.19 / 1M
- Context
- 1,000,000
- Avg score
- 80.5
- Open weights
- No
Specification comparison
| Spec | DeepSeek-R1 Base | ERNIE 4.5 Pro | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $13.89 | A |
| Output price / 1M | $26.24 | $65.19 | A |
| Context window | 400,000 | 1,000,000 | B |
| Max output | 65,536 | 32,768 | A |
| Average score | 80.1 | 80.5 | B |
| Arena Elo | 1442.9 | 1414.3 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-R1 Base wins 5, ERNIE 4.5 Pro wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | ERNIE 4.5 Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 80.3 | 0.7 | DeepSeek-R1 Base |
| GPQA Diamond | 76.2 | 74.7 | 1.5 | DeepSeek-R1 Base |
| SWE-bench Verified | 71.2 | 66.5 | 4.7 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 86.9 | 3.5 | ERNIE 4.5 Pro |
| MATH-500 | 84.9 | 87.2 | 2.3 | ERNIE 4.5 Pro |
| AIME 2025 | 73.8 | 76.3 | 2.5 | ERNIE 4.5 Pro |
| LiveCodeBench | 76.0 | 76.4 | 0.4 | ERNIE 4.5 Pro |
| IFEval | 88.2 | 86.0 | 2.2 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 83.1 | 0.9 | ERNIE 4.5 Pro |
| HellaSwag | 90.0 | 90.2 | 0.2 | ERNIE 4.5 Pro |
| MMMU | 74.2 | 78.4 | 4.2 | ERNIE 4.5 Pro |
| Arena Elo | 1442.9 | 1414.3 | 28.6 | 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 ERNIE 4.5 Pro costs about $26.71. DeepSeek-R1 Base is roughly 61% cheaper than ERNIE 4.5 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
ERNIE 4.5 Pro has the larger context window at 1,000,000 tokens, compared with 400,000 for DeepSeek-R1 Base. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 5, ERNIE 4.5 Pro wins 7, and 0 are tied. On the index average, ERNIE 4.5 Pro leads DeepSeek-R1 Base by 0.4 points (80.5 versus 80.1).
The price picture favours DeepSeek-R1 Base. The context picture favours ERNIE 4.5 Pro. 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 ERNIE 4.5 Pro for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Base if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
- You need open weights for self-hosting or fine-tuning.
Choose ERNIE 4.5 Pro if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
FAQ
Is DeepSeek-R1 Base better than ERNIE 4.5 Pro?
On the index average, ERNIE 4.5 Pro leads DeepSeek-R1 Base by 0.4 points (80.5 versus 80.1). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 5 benchmarks, ERNIE 4.5 Pro wins 7.
Which is cheaper, DeepSeek-R1 Base or ERNIE 4.5 Pro?
DeepSeek-R1 Base is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $26.71 for ERNIE 4.5 Pro.
Which has the larger context window?
ERNIE 4.5 Pro has the larger context window at 1,000,000 tokens. DeepSeek-R1 Base has 400,000 tokens.
Are these models open weights?
DeepSeek-R1 Base is open weights; ERNIE 4.5 Pro is proprietary.
Which should I choose for production?
If you need a single default, start with ERNIE 4.5 Pro for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.