Model comparison
Qwen2.5-Max vs DeepSeek-V3
- Input
- $1.60 / 1M tokens
- Output
- $6.40 / 1M tokens
- Context
- ~32K tokens
- Released
- 2025-01-29
- Input
- $0.27 / 1M tokens (cache miss)
- Output
- $1.10 / 1M tokens
- Context
- 128K tokens
- Released
- 2024-12-26
Qwen2.5-Max scores higher on 3 of the 3 benchmarks both models report; DeepSeek-V3 is cheaper on input tokens ($0.3 vs $1.6 per 1M, 5.9×); DeepSeek-V3 has the larger context window (128K vs 32K); DeepSeek-V3 ships open weights while Qwen2.5-Max is API-only.
Specs and pricing side by side
| Spec | Qwen2.5-Max | DeepSeek-V3 |
|---|---|---|
| Maker | Alibaba (Qwen) | DeepSeek |
| Released | 2025-01-29 | 2024-12-26 |
| Context window | ~32K tokens | 128K tokens |
| Max output | Not officially disclosed | not published |
| Input price | $1.60 / 1M tokens | $0.27 / 1M tokens (cache miss) |
| Output price | $6.40 / 1M tokens | $1.10 / 1M tokens |
| Parameters | Not disclosed (Mixture-of-Experts; total parameter count never published by Alibaba) | 671B total / 37B active per token (Mixture-of-Experts) |
| License | Proprietary (closed weights). Unlike the smaller Apache-2.0 open-weight Qwen2.5 models, the Max tier is API-only. | DeepSeek Model License (commercial use permitted); code under MIT |
| Weights | API only | Open weights |
| Modalities | text input, text output | text |
| Knowledge cutoff | Not officially disclosed | not published |
Benchmarks both models report
| Benchmark | Qwen2.5-Max | DeepSeek-V3 |
|---|---|---|
| MMLU-Pro | 76.1% | 75.9% |
| GPQA-Diamond | 60.1% | 59.1% |
| LiveCodeBench | 38.7% | 37.6% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick Qwen2.5-Max if…
- You care about MMLU-Pro, GPQA-Diamond, LiveCodeBench (it scores higher on 3 shared benchmarks).
- You need text input / text output input, which DeepSeek-V3 lacks.
- Your use case is: general-purpose chat assistants and Q&A in English and Chinese.
Pick DeepSeek-V3 if…
- Cost matters: input tokens cost 5.9× less ($0.3 vs $1.6 per 1M).
- You need the longer context (128K vs 32K).
- You want to self-host or fine-tune (open weights, DeepSeek Model License (commercial use permitted); code under MIT).
- You need text input, which Qwen2.5-Max lacks.
- Your use case is: general-purpose chat and instruction following at low cost.
FAQ
Which is better, Qwen2.5-Max or DeepSeek-V3?
Qwen2.5-Max scores higher on 3 of the 3 benchmarks both models report; DeepSeek-V3 is cheaper on input tokens ($0.3 vs $1.6 per 1M, 5.9×); DeepSeek-V3 has the larger context window (128K vs 32K); DeepSeek-V3 ships open weights while Qwen2.5-Max is API-only. The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is DeepSeek-V3?
Qwen2.5-Max: $1.60 / 1M tokens input, $6.40 / 1M tokens output. DeepSeek-V3: $0.27 / 1M tokens (cache miss) input, $1.10 / 1M tokens output (list API prices as published by each maker).
What are the context windows of Qwen2.5-Max and DeepSeek-V3?
Qwen2.5-Max offers ~32K tokens with up to Not officially disclosed output; DeepSeek-V3 offers 128K tokens.
Is Qwen2.5-Max newer than DeepSeek-V3?
Qwen2.5-Max was released 2025-01-29; DeepSeek-V3 was released 2024-12-26.