GPT-5.1 Codex mini vs Mistral Large 3 vs Qwen3.5 9B Base
Too close to call on our weighted score (GPT-5.1 Codex mini 54, Mistral Large 3 52, Qwen3.5 9B Base 50). The right pick depends on what you value most.
OpenAIGPT-5.1 Codex mini
54/100- ECI—
- Price$0.25 / $2.00
- Context400K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
Alibaba (Qwen)Qwen3.5 9B Base
50/100- ECI—
- Price$0.30 / $0.807
- Context262K
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 9B Base 50/100), so choose by what matters most for your work: Qwen3.5 9B Base 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 priceQwen3.5 9B BaseQwen3.5 9B Base $0.427 · GPT-5.1 Codex mini $0.688 · Mistral Large 3 $0.75 per 1M tokens (3:1 blend)
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · Mistral Large 3 262,144 · Qwen3.5 9B Base 262,144 tokens
- Widest inputsQwen3.5 9B BaseGPT-5.1 Codex mini: Text, Images · Mistral Large 3: Text, Images · Qwen3.5 9B Base: Text, Images, Video
- Self-hostingMistral Large 3 and Qwen3.5 9B BasePublishes downloadable weights (Apache-2.0)
| Measure | Weight | GPT-5.1 Codex mini | Mistral Large 3 | Qwen3.5 9B Base |
|---|---|---|---|---|
| Price | 50% | 55 | 53 | 65 |
| Inputs & features | 30% | 60 | 60 | 35 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 54/100 | 52/100 | 50/100 |
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 | GPT-5.1 Codex miniOpenAI | Mistral Large 3Mistral AI | Qwen3.5 9B BaseAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.50 | $0.30 |
| Output | $2.00 | $1.50 | $0.807 (best) |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.688 | $0.75 | $0.427 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Official Mistral API | Median of 1 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | OpenApache-2.0 |
| API model ID | — | mistral-large-2512 | — |
| API providers | 16 | 17 (best) | 1 |
| Released | Nov 13, 2025 | Dec 2, 2025 | Feb 26, 2026 |
| Knowledge cutoff | Sep 30, 2024 | Nov 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.1 Codex mini$6.50
- Mistral Large 3$8.00
- Qwen3.5 9B Base$4.61
Which should you choose?
Which is better: GPT-5.1 Codex mini, Mistral Large 3 or Qwen3.5 9B Base?
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 9B Base 50/100), so choose by what matters most for your work: Qwen3.5 9B Base 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, GPT-5.1 Codex mini, Mistral Large 3 or Qwen3.5 9B Base?
Qwen3.5 9B Base is cheaper at $0.30 input / $0.807 output per million tokens (median across 1 API provider). GPT-5.1 Codex mini costs $0.25 input / $2.00 output per million tokens (median across 16 API providers); 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 $0.427 per million tokens for Qwen3.5 9B Base versus $0.688 for GPT-5.1 Codex mini (1.6× as much) and $0.75 for Mistral Large 3 (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.1 Codex mini has not been scored yet, Mistral Large 3 has not been scored yet and Qwen3.5 9B Base has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.1 Codex mini, Mistral Large 3 and Qwen3.5 9B Base yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen3.5 9B 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 Mistral Large 3 and 262,144 for Qwen3.5 9B Base. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Mistral Large 3 up to 262,144, Qwen3.5 9B Base up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Codex mini accepts text and images; Mistral Large 3 accepts text and images; Qwen3.5 9B Base accepts text, images and video. Qwen3.5 9B Base handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 and Qwen3.5 9B Base publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Qwen3.5 9B Base is the newest, released Feb 26, 2026. Mistral Large 3 came out Dec 2, 2025; GPT-5.1 Codex mini came out Nov 13, 2025. Knowledge cutoff: GPT-5.1 Codex mini Sep 30, 2024, Mistral Large 3 Nov 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.
