GPT-5.1 Codex mini vs Mistral Large 3 vs Qwen3.5 27B
Too close to call on our weighted score (Qwen3.5 27B 61, GPT-5.1 Codex mini 59, Mistral Large 3 50). The right pick depends on what you value most.
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
Mistral AIMistral Large 3
50/100- ECI—
- Price$0.50 / $1.50
- Context262K
Alibaba (Qwen)Qwen3.5 27B
61/100- ECI—
- Price$0.30 / $2.40
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen3.5 27B 61/100, GPT-5.1 Codex mini 59/100, Mistral Large 3 50/100), 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 · Mistral Large 3 $0.75 · Qwen3.5 27B $0.825 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 27B 262,144 tokens
- Widest inputsQwen3.5 27BGPT-5.1 Codex mini: Text, Images · Mistral Large 3: Text, Images · Qwen3.5 27B: Text, Images, Audio, Video
- Self-hostingMistral Large 3 and Qwen3.5 27BPublishes downloadable weights
| Measure | Weight | GPT-5.1 Codex mini | Mistral Large 3 | Qwen3.5 27B |
|---|---|---|---|---|
| Price | 50% | 58 | 56 | 54 |
| Inputs & features | 30% | 70 | 50 | 90 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 59/100 | 50/100 | 61/100 |
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 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.50 | $0.30 |
| Output | $2.00 | $1.50 (best) | $2.40 |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.688 (best) | $0.75 | $0.825 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Mistral API | Official Alibaba API |
| 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 | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | mistral-large-2512 | qwen3.5-27b |
| API providers | 10 | 13 | 16 (best) |
| Released | Nov 13, 2025 | Dec 2, 2025 | Feb 23, 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 27B$7.80
Which should you choose?
Which is better: GPT-5.1 Codex mini, Mistral Large 3 or Qwen3.5 27B?
It is close. Our weighted score puts them within 3 points (Qwen3.5 27B 61/100, GPT-5.1 Codex mini 59/100, Mistral Large 3 50/100), 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, GPT-5.1 Codex mini, Mistral Large 3 or Qwen3.5 27B?
GPT-5.1 Codex mini is cheaper at $0.25 input / $2.00 output per million tokens (median across 10 API providers). Mistral Large 3 costs $0.50 input / $1.50 output per million tokens (official Mistral API price); Qwen3.5 27B costs $0.30 input / $2.40 output per million tokens (official Alibaba 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 $0.75 for Mistral Large 3 (1.1× as much) and $0.825 for Qwen3.5 27B (1.2× 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 27B 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 27B 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?
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 27B. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Mistral Large 3 up to 262,144, Qwen3.5 27B 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 27B accepts text, images, audio and video. Qwen3.5 27B handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 and Qwen3.5 27B publishes its weights and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Qwen3.5 27B is the newest, released Feb 23, 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 Oct 4, 2026.
