Sonar Reasoning Pro vs Mistral Large 2.1 vs Qwen Max
Too close to call on our weighted score (Sonar Reasoning Pro 27, Mistral Large 2.1 26, Qwen Max 22). The right pick depends on what you value most.
PerplexitySonar Reasoning Pro
27/100- ECI—
- Price$2.00 / $8.00
- Context128K
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
Alibaba (Qwen)Qwen Max
22/100- ECI—
- Price$1.60 / $6.40
- Context33K
Too close to call
It is close. Our weighted score puts them within 1 points (Sonar Reasoning Pro 27/100, Mistral Large 2.1 26/100, Qwen Max 22/100), so choose by what matters most for your work: Qwen Max on price and Mistral Large 2.1 for long inputs. 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 · Sonar Reasoning Pro $3.50 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1Mistral Large 2.1 131,072 · Sonar Reasoning Pro 128,000 · Qwen Max 32,768 tokens
- Widest inputsSonar Reasoning ProSonar Reasoning Pro: Text, Images · Mistral Large 2.1: Text · Qwen Max: Text
- Self-hostingMistral Large 2.1Publishes downloadable weights
| Measure | Weight | Sonar Reasoning Pro | Mistral Large 2.1 | Qwen Max |
|---|---|---|---|---|
| Price | 50% | 24 | 27 | 29 |
| Inputs & features | 30% | 35 | 25 | 25 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 27/100 | 26/100 | 22/100 |
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 | Sonar Reasoning ProPerplexity | Mistral Large 2.1Mistral AI | Qwen MaxAlibaba (Qwen) |
|---|---|---|---|
| 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 | $2.00 | $2.00 | $1.60 (best) |
| Output | $8.00 | $6.00 (best) | $6.40 |
| Cached input | — | — | — |
| Blended (3:1) | $3.50 | $3.00 | $2.80 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Perplexity API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 32,768 tokens |
| Max output | 4,096 tokens | 16,384 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yesminimal · low · medium · high | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | sonar-reasoning-pro | mistral-large-2411 | qwen-max |
| API providers | 4 | 2 | 6 (best) |
| Released | Jan 1, 2024 | Nov 18, 2024 | Apr 3, 2024 |
| Knowledge cutoff | Sep 1, 2025 | Nov 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Sonar Reasoning Pro$36.00
- Mistral Large 2.1$32.00
- Qwen Max$28.80
Which should you choose?
Which is better: Sonar Reasoning Pro, Mistral Large 2.1 or Qwen Max?
It is close. Our weighted score puts them within 1 points (Sonar Reasoning Pro 27/100, Mistral Large 2.1 26/100, Qwen Max 22/100), so choose by what matters most for your work: Qwen Max on price and Mistral Large 2.1 for long inputs. 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, Sonar Reasoning Pro, Mistral Large 2.1 or Qwen Max?
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); Sonar Reasoning Pro costs $2.00 input / $8.00 output per million tokens (official Perplexity 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) and $3.50 for Sonar Reasoning Pro (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Sonar Reasoning Pro has not been scored yet, Mistral Large 2.1 has an ECI of 128.5 and Qwen Max has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sonar Reasoning Pro, Mistral Large 2.1 and Qwen Max yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sonar Reasoning Pro does not support tool calling, which most coding agents need.
Which has the bigger context window?
Mistral Large 2.1 has the largest context window at 131,072 tokens, against 128,000 for Sonar Reasoning Pro and 32,768 for Qwen Max. Maximum output per response: Sonar Reasoning Pro up to 4,096, Mistral Large 2.1 up to 16,384, Qwen Max up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Sonar Reasoning Pro accepts text and images; Mistral Large 2.1 accepts text; Qwen Max accepts text. Sonar Reasoning Pro handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 publishes its weights and can be self-hosted; Sonar Reasoning Pro and 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; Sonar Reasoning Pro came out Jan 1, 2024. Knowledge cutoff: Sonar Reasoning Pro Sep 1, 2025, Mistral Large 2.1 Nov 2024, Qwen Max Apr 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.
