DeepSeek-V3.2 Base vs ERNIE 4.5 Turbo: which model should you choose?
We compared DeepSeek-V3.2 Base and ERNIE 4.5 Turbo on price, context window and 12 benchmarks. DeepSeek-V3.2 Base has the stronger average benchmark score; DeepSeek-V3.2 Base is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Base
DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $27.10 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
ERNIE 4.5 Turbo
ERNIE 4.5 Turbo is a multimodal model from Baidu. It offers a 128,000-token context window, costs $13.75 per million input tokens and $54.36 per million output tokens, and averages 79.7 across the index's benchmark suite.
- Input
- $13.75 / 1M
- Output
- $54.36 / 1M
- Context
- 128,000
- Avg score
- 79.7
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 Base | ERNIE 4.5 Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $13.75 | A |
| Output price / 1M | $27.10 | $54.36 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 128,000 | 32,768 | A |
| Average score | 80.2 | 79.7 | A |
| Arena Elo | 1417.9 | 1419.8 | B |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 Base wins 8, ERNIE 4.5 Turbo wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | ERNIE 4.5 Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 84.0 | 0.3 | DeepSeek-V3.2 Base |
| GPQA Diamond | 77.1 | 73.0 | 4.1 | DeepSeek-V3.2 Base |
| SWE-bench Verified | 69.1 | 67.5 | 1.6 | DeepSeek-V3.2 Base |
| HumanEval | 82.8 | 86.4 | 3.6 | ERNIE 4.5 Turbo |
| MATH-500 | 83.7 | 86.4 | 2.7 | ERNIE 4.5 Turbo |
| AIME 2025 | 74.3 | 80.7 | 6.4 | ERNIE 4.5 Turbo |
| LiveCodeBench | 73.1 | 70.5 | 2.6 | DeepSeek-V3.2 Base |
| IFEval | 86.9 | 85.2 | 1.7 | DeepSeek-V3.2 Base |
| TruthfulQA | 83.5 | 82.5 | 1.0 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 87.0 | 3.2 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 73.7 | 3.0 | DeepSeek-V3.2 Base |
| Arena Elo | 1417.9 | 1419.8 | 1.9 | ERNIE 4.5 Turbo |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and ERNIE 4.5 Turbo costs about $23.90. DeepSeek-V3.2 Base is roughly 56% cheaper than ERNIE 4.5 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 8, ERNIE 4.5 Turbo wins 4, and 0 are tied. On the index average, DeepSeek-V3.2 Base leads ERNIE 4.5 Turbo by 0.5 points (80.2 versus 79.7).
The price picture favours DeepSeek-V3.2 Base. 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 DeepSeek-V3.2 Base for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-V3.2 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 128,000-token output limit.
- You need open weights for self-hosting or fine-tuning.
Choose ERNIE 4.5 Turbo if
- You prefer Baidu's tooling or already use it.
FAQ
Is DeepSeek-V3.2 Base better than ERNIE 4.5 Turbo?
On the index average, DeepSeek-V3.2 Base leads ERNIE 4.5 Turbo by 0.5 points (80.2 versus 79.7). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 8 benchmarks, ERNIE 4.5 Turbo wins 4.
Which is cheaper, DeepSeek-V3.2 Base or ERNIE 4.5 Turbo?
DeepSeek-V3.2 Base is cheaper on a blended 3:1 basis at about $10.56 per million tokens, versus $23.90 for ERNIE 4.5 Turbo.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Base is open weights; ERNIE 4.5 Turbo is proprietary.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work, and check whether its price holds at your expected volume before committing.