Qwen3.5 35B-A3B Base vs Mistral Large 3 vs GPT-5.1 Codex mini
Too close to call on our weighted score (GPT-5.1 Codex mini 54, Mistral Large 3 52, Qwen3.5 35B-A3B Base 45). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 35B-A3B Base
45/100- ECI—
- Price$0.54 / $1.33
- Context262K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
OpenAIGPT-5.1 Codex mini
54/100- ECI—
- Price$0.25 / $2.00
- Context400K
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-5.1 Codex mini 54/100, Mistral Large 3 52/100, Qwen3.5 35B-A3B Base 45/100), so choose by what matters most for your work: GPT-5.1 Codex mini on price and GPT-5.1 Codex mini 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 priceGPT-5.1 Codex miniGPT-5.1 Codex mini $0.688 · Qwen3.5 35B-A3B Base $0.739 · Mistral Large 3 $0.75 per 1M tokens (3:1 blend)
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · Qwen3.5 35B-A3B Base 262,144 · Mistral Large 3 262,144 tokens
- Widest inputsQwen3.5 35B-A3B BaseQwen3.5 35B-A3B Base: Text, Images, Video · Mistral Large 3: Text, Images · GPT-5.1 Codex mini: Text, Images
- Self-hostingQwen3.5 35B-A3B Base and Mistral Large 3Publishes downloadable weights (Apache-2.0)
| Measure | Weight | Qwen3.5 35B-A3B Base | Mistral Large 3 | GPT-5.1 Codex mini |
|---|---|---|---|---|
| Price | 50% | 54 | 53 | 55 |
| Inputs & features | 30% | 35 | 60 | 60 |
| Context window | 20% | 37 | 37 | 44 |
| Overall | 100% | 45/100 | 52/100 | 54/100 |
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 | Qwen3.5 35B-A3B BaseAlibaba (Qwen) | Mistral Large 3Mistral AI | GPT-5.1 Codex miniOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.54 | $0.50 | $0.25 (best) |
| Output | $1.33 (best) | $1.50 | $2.00 |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.739 | $0.75 | $0.688 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Mistral API | Median of 16 providers |
| Limits | |||
| Context window | 262,144 tokens | 262,144 tokens | 400,000 tokens (best) |
| Max output | 65,536 tokens | 262,144 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | No | No | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | mistral-large-2512 | — |
| API providers | 1 | 17 (best) | 16 |
| Released | Feb 24, 2026 | Dec 2, 2025 | Nov 13, 2025 |
| Knowledge cutoff | — | Nov 2024 | Sep 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.5 35B-A3B Base$8.07
- Mistral Large 3$8.00
- GPT-5.1 Codex mini$6.50
Which should you choose?
Which is better: Qwen3.5 35B-A3B Base, Mistral Large 3 or GPT-5.1 Codex mini?
It is close. Our weighted score puts them within 2 points (GPT-5.1 Codex mini 54/100, Mistral Large 3 52/100, Qwen3.5 35B-A3B Base 45/100), so choose by what matters most for your work: GPT-5.1 Codex mini on price and GPT-5.1 Codex mini 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, Qwen3.5 35B-A3B Base, Mistral Large 3 or GPT-5.1 Codex mini?
GPT-5.1 Codex mini is cheaper at $0.25 input / $2.00 output per million tokens (median across 16 API providers). Qwen3.5 35B-A3B Base costs $0.54 input / $1.33 output per million tokens (median across 1 API provider); Mistral Large 3 costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.688 per million tokens for GPT-5.1 Codex mini versus $0.739 for Qwen3.5 35B-A3B Base (1.1× as much) and $0.75 for Mistral Large 3 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.5 35B-A3B Base has not been scored yet, Mistral Large 3 has not been scored yet and GPT-5.1 Codex mini has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 35B-A3B Base, Mistral Large 3 and GPT-5.1 Codex mini yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen3.5 35B-A3B Base does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5.1 Codex mini has the largest context window at 400,000 tokens, against 262,144 for Qwen3.5 35B-A3B Base and 262,144 for Mistral Large 3. Maximum output per response: Qwen3.5 35B-A3B Base up to 65,536, Mistral Large 3 up to 262,144, GPT-5.1 Codex mini up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 35B-A3B Base accepts text, images and video; Mistral Large 3 accepts text and images; GPT-5.1 Codex mini accepts text and images. Qwen3.5 35B-A3B Base handles the widest range of inputs.
Are any of these open source?
Qwen3.5 35B-A3B Base and Mistral Large 3 publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Qwen3.5 35B-A3B Base is the newest, released Feb 24, 2026. Mistral Large 3 came out Dec 2, 2025; GPT-5.1 Codex mini came out Nov 13, 2025. Knowledge cutoff: Mistral Large 3 Nov 2024, GPT-5.1 Codex mini Sep 30, 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 Nov 2, 2026.
