Apertus 70B vs Mistral Medium 3.1 vs Qwen3 VL 235B A22B Thinking
Too close to call on our weighted score (Mistral Medium 3.1 50, Qwen3 VL 235B A22B Thinking 48, Apertus 70B 33). The right pick depends on what you value most.
Swiss AI
Apertus 70B
33/100- ECI—
- Price$0.82 / $2.42
- Context66K
Mistral AIMistral Medium 3.1
50/100- ECI—
- Price$0.40 / $2.00
- Context262K
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
48/100- ECI—
- Price$0.40 / $4.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (Mistral Medium 3.1 50/100, Qwen3 VL 235B A22B Thinking 48/100, Apertus 70B 33/100), so choose by what matters most for your work: Mistral Medium 3.1 on price and Mistral Medium 3.1 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 priceMistral Medium 3.1Mistral Medium 3.1 $0.80 · Apertus 70B $1.22 · Qwen3 VL 235B A22B Thinking $1.30 per 1M tokens (3:1 blend)
- Longest contextMistral Medium 3.1Mistral Medium 3.1 262,144 · Qwen3 VL 235B A22B Thinking 131,072 · Apertus 70B 65,536 tokens
- Widest inputsMistral Medium 3.1 and Qwen3 VL 235B A22B ThinkingApertus 70B: Text · Mistral Medium 3.1: Text, Images · Qwen3 VL 235B A22B Thinking: Text, Images
- Self-hostingApertus 70B and Qwen3 VL 235B A22B ThinkingPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Apertus 70B | Mistral Medium 3.1 | Qwen3 VL 235B A22B Thinking |
|---|---|---|---|---|
| Price | 50% | 46 | 54 | 44 |
| Inputs & features | 30% | 25 | 50 | 70 |
| Context window | 20% | 12 | 37 | 24 |
| Overall | 100% | 33/100 | 50/100 | 48/100 |
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 | Apertus 70BSwiss AI | Mistral Medium 3.1Mistral AI | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.82 | $0.40 (best) | $0.40 (best) |
| Output | $2.42 | $2.00 (best) | $4.00 |
| Cached input | — | — | — |
| Blended (3:1) | $1.22 | $0.80 (best) | $1.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Official Mistral API | Median of 9 providers |
| Limits | |||
| Context window | 65,536 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 262,144 tokens (best) | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Proprietary | Open |
| API model ID | — | mistral-medium-2508 | — |
| API providers | 3 | 1 | 9 (best) |
| Released | Sep 2, 2025 | Aug 12, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Sep 2025 | May 2025 | Mar 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Apertus 70B$13.04
- Mistral Medium 3.1$8.00
- Qwen3 VL 235B A22B Thinking$12.00
Which should you choose?
Which is better: Apertus 70B, Mistral Medium 3.1 or Qwen3 VL 235B A22B Thinking?
It is close. Our weighted score puts them within 1 points (Mistral Medium 3.1 50/100, Qwen3 VL 235B A22B Thinking 48/100, Apertus 70B 33/100), so choose by what matters most for your work: Mistral Medium 3.1 on price and Mistral Medium 3.1 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, Apertus 70B, Mistral Medium 3.1 or Qwen3 VL 235B A22B Thinking?
Mistral Medium 3.1 is cheaper at $0.40 input / $2.00 output per million tokens (official Mistral API price). Apertus 70B costs $0.82 input / $2.42 output per million tokens (median across 3 API providers); Qwen3 VL 235B A22B Thinking costs $0.40 input / $4.00 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.80 per million tokens for Mistral Medium 3.1 versus $1.22 for Apertus 70B (1.5× as much) and $1.30 for Qwen3 VL 235B A22B Thinking (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Apertus 70B has not been scored yet, Mistral Medium 3.1 has not been scored yet and Qwen3 VL 235B A22B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Apertus 70B, Mistral Medium 3.1 and Qwen3 VL 235B A22B Thinking 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 Medium 3.1 has the largest context window at 262,144 tokens, against 131,072 for Qwen3 VL 235B A22B Thinking and 65,536 for Apertus 70B. Maximum output per response: Apertus 70B up to 8,192, Mistral Medium 3.1 up to 262,144, Qwen3 VL 235B A22B Thinking up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Apertus 70B accepts text; Mistral Medium 3.1 accepts text and images; Qwen3 VL 235B A22B Thinking accepts text and images. Mistral Medium 3.1 handles the widest range of inputs.
Are any of these open source?
Apertus 70B and Qwen3 VL 235B A22B Thinking publishes its weights (Apache-2.0) and can be self-hosted; Mistral Medium 3.1 is proprietary.
Which is newer?
Qwen3 VL 235B A22B Thinking is the newest, released Sep 23, 2025. Apertus 70B came out Sep 2, 2025; Mistral Medium 3.1 came out Aug 12, 2025. Knowledge cutoff: Apertus 70B Sep 2025, Mistral Medium 3.1 May 2025, Qwen3 VL 235B A22B Thinking Mar 31, 2025.
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.
