Mistral Large 2.1 vs Sonar Pro vs Qwen2.5 72B Instruct
Too close to call on our weighted score (Qwen2.5 72B Instruct 28, Mistral Large 2.1 26, Sonar Pro 20). The right pick depends on what you value most.
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
PerplexitySonar Pro
20/100- ECI—
- Price$3.00 / $15.00
- Context200K
Alibaba (Qwen)Qwen2.5 72B Instruct
28/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen2.5 72B Instruct 28/100, Mistral Large 2.1 26/100, Sonar Pro 20/100), so choose by what matters most for your work: Qwen2.5 72B Instruct on price and Sonar Pro 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 priceQwen2.5 72B InstructQwen2.5 72B Instruct $2.45 · Mistral Large 2.1 $3.00 · Sonar Pro $6.00 per 1M tokens (3:1 blend)
- Longest contextSonar ProSonar Pro 200,000 · Mistral Large 2.1 131,072 · Qwen2.5 72B Instruct 131,072 tokens
- Widest inputsSonar ProMistral Large 2.1: Text · Sonar Pro: Text, Images · Qwen2.5 72B Instruct: Text
- Self-hostingMistral Large 2.1 and Qwen2.5 72B InstructPublishes downloadable weights
| Measure | Weight | Mistral Large 2.1 | Sonar Pro | Qwen2.5 72B Instruct |
|---|---|---|---|---|
| Price | 50% | 27 | 13 | 31 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 24 | 32 | 24 |
| Overall | 100% | 26/100 | 20/100 | 28/100 |
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 | Mistral Large 2.1Mistral AI | Sonar ProPerplexity | Qwen2.5 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.5 | — | 129.0 (best) |
| ECI rank | #130 of 148 | — | #128 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | — | 49.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | — | 8.1% (best) |
| Price per million tokens | |||
| Input | $2.00 | $3.00 | $1.40 (best) |
| Output | $6.00 | $15.00 | $5.60 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $6.00 | $2.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Perplexity API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | mistral-large-2411 | sonar-pro | qwen2-5-72b-instruct |
| API providers | 2 | 5 (best) | 1 |
| Released | Nov 18, 2024 | Jan 1, 2024 | Sep 19, 2024 |
| Knowledge cutoff | Nov 2024 | Sep 1, 2025 | 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.
- Mistral Large 2.1$32.00
- Sonar Pro$60.00
- Qwen2.5 72B Instruct$25.20
Which should you choose?
Which is better: Mistral Large 2.1, Sonar Pro or Qwen2.5 72B Instruct?
It is close. Our weighted score puts them within 2 points (Qwen2.5 72B Instruct 28/100, Mistral Large 2.1 26/100, Sonar Pro 20/100), so choose by what matters most for your work: Qwen2.5 72B Instruct on price and Sonar Pro 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, Mistral Large 2.1, Sonar Pro or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is cheaper at $1.40 input / $5.60 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 Pro costs $3.00 input / $15.00 output per million tokens (official Perplexity API price). At a typical mix of three input tokens to one output token, that is $2.45 per million tokens for Qwen2.5 72B Instruct versus $3.00 for Mistral Large 2.1 (1.2× as much) and $6.00 for Sonar Pro (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral Large 2.1 has an ECI of 128.5, Sonar Pro has not been scored yet and Qwen2.5 72B Instruct has an ECI of 129.0.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1, Sonar Pro and Qwen2.5 72B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sonar Pro does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sonar Pro has the largest context window at 200,000 tokens, against 131,072 for Mistral Large 2.1 and 131,072 for Qwen2.5 72B Instruct. Maximum output per response: Mistral Large 2.1 up to 16,384, Sonar Pro up to 8,192, Qwen2.5 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Sonar Pro accepts text and images; Qwen2.5 72B Instruct accepts text. Sonar Pro handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Qwen2.5 72B Instruct publishes its weights and can be self-hosted; Sonar Pro is proprietary.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Qwen2.5 72B Instruct came out Sep 19, 2024; Sonar Pro came out Jan 1, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Sonar Pro Sep 1, 2025, Qwen2.5 72B Instruct 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.
