Model comparison
Qwen2.5-Max vs DeepSeek-R1
- Input
- $1.60 / 1M tokens
- Output
- $6.40 / 1M tokens
- Context
- ~32K tokens
- Released
- 2025-01-29
- Input
- $0.55 / 1M tokens (cache miss; $0.14 cache hit)
- Output
- $2.19 / 1M tokens
- Context
- 128K
- Released
- 2025-01-20
DeepSeek-R1 scores higher on 4 of the 4 benchmarks both models report; DeepSeek-R1 is cheaper on input tokens ($0.6 vs $1.6 per 1M, 2.9×); DeepSeek-R1 has the larger context window (128K vs 32K); DeepSeek-R1 ships open weights while Qwen2.5-Max is API-only.
Specs and pricing side by side
| Spec | Qwen2.5-Max | DeepSeek-R1 |
|---|---|---|
| Maker | Alibaba (Qwen) | DeepSeek |
| Released | 2025-01-29 | 2025-01-20 |
| Context window | ~32K tokens | 128K |
| Max output | Not officially disclosed | 32,768 tokens (max generation length) |
| Input price | $1.60 / 1M tokens | $0.55 / 1M tokens (cache miss; $0.14 cache hit) |
| Output price | $6.40 / 1M tokens | $2.19 / 1M tokens |
| Parameters | Not disclosed (Mixture-of-Experts; total parameter count never published by Alibaba) | 671B total / 37B active (Mixture-of-Experts) |
| License | Proprietary (closed weights). Unlike the smaller Apache-2.0 open-weight Qwen2.5 models, the Max tier is API-only. | MIT |
| Weights | API only | Open weights |
| Modalities | text input, text output | Text |
| Knowledge cutoff | Not officially disclosed | Not officially disclosedsame |
Benchmarks both models report
| Benchmark | Qwen2.5-Max | DeepSeek-R1 |
|---|---|---|
| Arena-Hard | 89.4% | 92.3% |
| MMLU-Pro | 76.1% | 84% |
| GPQA-Diamond | 60.1% | 71.5% |
| LiveCodeBench | 38.7% | 65.9% |
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 need text input / text output input, which DeepSeek-R1 lacks.
- Your use case is: general-purpose chat assistants and Q&A in English and Chinese.
Pick DeepSeek-R1 if…
- You care about Arena-Hard, MMLU-Pro, GPQA-Diamond (it scores higher on 4 shared benchmarks).
- Cost matters: input tokens cost 2.9× less ($0.6 vs $1.6 per 1M).
- You need the longer context (128K vs 32K).
- You want to self-host or fine-tune (open weights, MIT).
- You need Text input, which Qwen2.5-Max lacks.
FAQ
Which is better, Qwen2.5-Max or DeepSeek-R1?
DeepSeek-R1 scores higher on 4 of the 4 benchmarks both models report; DeepSeek-R1 is cheaper on input tokens ($0.6 vs $1.6 per 1M, 2.9×); DeepSeek-R1 has the larger context window (128K vs 32K); DeepSeek-R1 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-R1?
Qwen2.5-Max: $1.60 / 1M tokens input, $6.40 / 1M tokens output. DeepSeek-R1: $0.55 / 1M tokens (cache miss; $0.14 cache hit) input, $2.19 / 1M tokens output (list API prices as published by each maker).
What are the context windows of Qwen2.5-Max and DeepSeek-R1?
Qwen2.5-Max offers ~32K tokens with up to Not officially disclosed output; DeepSeek-R1 offers 128K with up to 32,768 tokens (max generation length) output.
Is Qwen2.5-Max newer than DeepSeek-R1?
Qwen2.5-Max was released 2025-01-29; DeepSeek-R1 was released 2025-01-20.