Mistral Large 2.1 vs Qwen2.5-VL 7B Instruct vs Qwen2.5 72B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 28 and 26 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Our pick
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
- Alibaba (Qwen)
Qwen2.5 72B Instruct
28/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5 72B Instruct (28) and Mistral Large 2.1 (26). It leads on price and inputs & features. 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-VL 7B InstructQwen2.5-VL 7B Instruct $0.525 · Qwen2.5 72B Instruct $2.45 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameMistral Large 2.1 131,072 · Qwen2.5-VL 7B Instruct 131,072 · Qwen2.5 72B Instruct 131,072 tokens
- Widest inputsQwen2.5-VL 7B InstructMistral Large 2.1: Text · Qwen2.5-VL 7B Instruct: Text, Images · Qwen2.5 72B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Large 2.1 | Qwen2.5-VL 7B Instruct | Qwen2.5 72B Instruct |
|---|---|---|---|---|
| Price | 50% | 27 | 63 | 31 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 26/100 | 51/100 | 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 | Qwen2.5-VL 7B InstructAlibaba (Qwen) | 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 | $0.35 (best) | $1.40 |
| Output | $6.00 | $1.05 (best) | $5.60 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.525 (best) | $2.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | mistral-large-2411 | qwen2-5-vl-7b-instruct | qwen2-5-72b-instruct |
| API providers | 2 (best) | 1 | 1 |
| Released | Nov 18, 2024 | Sep 2024 | Sep 19, 2024 |
| Knowledge cutoff | Nov 2024 | Apr 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.
- Mistral Large 2.1$32.00
- Qwen2.5-VL 7B Instruct$5.60
- Qwen2.5 72B Instruct$25.20
Which should you choose?
Which is better: Mistral Large 2.1, Qwen2.5-VL 7B Instruct or Qwen2.5 72B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5 72B Instruct (28) and Mistral Large 2.1 (26). It leads on price and inputs & features. 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, Qwen2.5-VL 7B Instruct or Qwen2.5 72B Instruct?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 output per million tokens (official Alibaba API price). Qwen2.5 72B Instruct costs $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). At a typical mix of three input tokens to one output token, that is $0.525 per million tokens for Qwen2.5-VL 7B Instruct versus $2.45 for Qwen2.5 72B Instruct (4.7× as much) and $3.00 for Mistral Large 2.1 (5.7× 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, Qwen2.5-VL 7B Instruct 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, Qwen2.5-VL 7B Instruct and Qwen2.5 72B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Mistral Large 2.1, Qwen2.5-VL 7B Instruct and Qwen2.5 72B Instruct share the same 131,072-token context window. Maximum output per response: Mistral Large 2.1 up to 16,384, Qwen2.5-VL 7B Instruct 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; Qwen2.5-VL 7B Instruct accepts text and images; Qwen2.5 72B Instruct accepts text. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Qwen2.5 72B Instruct came out Sep 19, 2024; Qwen2.5-VL 7B Instruct came out Sep 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Qwen2.5-VL 7B Instruct Apr 2024, 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.
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