Qwen Max vs Mistral Large 2.1
Mistral Large 2.1 comes out ahead, 26 to 22 on our weighted score, though Qwen Max is 7% cheaper per token.
Alibaba (Qwen)Qwen Max
22/100- ECI—
- Price$1.60 / $6.40
- Context33K
- Our pick
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
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Make it a three-way comparison.
Mistral Large 2.1 is our pick
Mistral Large 2.1 is the better all-round choice, scoring 26/100 against Qwen Max (22). It leads on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen MaxQwen Max $2.80 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1Mistral Large 2.1 131,072 · Qwen Max 32,768 tokens
- Widest inputsSame inputsQwen Max: Text · Mistral Large 2.1: Text
- Self-hostingMistral Large 2.1Publishes downloadable weights
| Measure | Weight | Qwen Max | Mistral Large 2.1 |
|---|---|---|---|
| Price | 50% | 29 | 27 |
| Inputs & features | 30% | 25 | 25 |
| Context window | 20% | 0 | 24 |
| Overall | 100% | 22/100 | 26/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen MaxAlibaba (Qwen) | Mistral Large 2.1Mistral AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 128.5 |
| ECI rank | — | #130 of 148 |
| GPQA DiamondGraduate-level science questions | — | 51.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% |
| Price per million tokens | ||
| Input | $1.60 (best) | $2.00 |
| Output | $6.40 | $6.00 (best) |
| Cached input | — | — |
| Blended (3:1) | $2.80 (best) | $3.00 |
| Long-context rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Mistral API |
| Limits | ||
| Context window | 32,768 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 16,384 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | qwen-max | mistral-large-2411 |
| API providers | 6 (best) | 2 |
| Released | Apr 3, 2024 | Nov 18, 2024 |
| Knowledge cutoff | Apr 2024 | Nov 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen Max$28.80
- Mistral Large 2.1$32.00
Which should you choose?
Which is better: Qwen Max or Mistral Large 2.1?
Mistral Large 2.1 is the better all-round choice, scoring 26/100 against Qwen Max (22). It leads on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Qwen Max or Mistral Large 2.1?
Qwen Max is cheaper at $1.60 input / $6.40 output per million tokens (official Alibaba API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $2.80 per million tokens for Qwen Max versus $3.00 for Mistral Large 2.1 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Qwen Max has not been scored yet and Mistral Large 2.1 has an ECI of 128.5.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen Max and Mistral Large 2.1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Mistral Large 2.1 has the largest context window at 131,072 tokens, against 32,768 for Qwen Max. Maximum output per response: Qwen Max up to 8,192, Mistral Large 2.1 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Qwen Max accepts text; Mistral Large 2.1 accepts text. They handle the same number of input types.
Are any of these open source?
Mistral Large 2.1 publishes its weights and can be self-hosted; Qwen Max is proprietary.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Qwen Max came out Apr 3, 2024. Knowledge cutoff: Qwen Max Apr 2024, Mistral Large 2.1 Nov 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
