GPT-5.1 Codex mini vs Qwen3.8 27B vs Mistral Large 3
Too close to call on our weighted score (GPT-5.1 Codex mini 54, Qwen3.8 27B 53, Mistral Large 3 52). The right pick depends on what you value most.
OpenAIGPT-5.1 Codex mini
54/100- ECI—
- Price$0.25 / $2.00
- Context400K
Alibaba (Qwen)Qwen3.8 27B
53/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-5.1 Codex mini 54/100, Qwen3.8 27B 53/100, Mistral Large 3 52/100), 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 · Qwen3.8 27B $0.938 per 1M tokens (3:1 blend)
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · Qwen3.8 27B 262,144 · Mistral Large 3 262,144 tokens
- Widest inputsQwen3.8 27BGPT-5.1 Codex mini: Text, Images · Qwen3.8 27B: Text, Images, Video · Mistral Large 3: Text, Images
- Self-hostingQwen3.8 27B and Mistral Large 3Publishes downloadable weights
| Measure | Weight | GPT-5.1 Codex mini | Qwen3.8 27B | Mistral Large 3 |
|---|---|---|---|---|
| Price | 50% | 55 | 49 | 53 |
| Inputs & features | 30% | 60 | 70 | 60 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 54/100 | 53/100 | 52/100 |
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 | Qwen3.8 27BAlibaba (Qwen) | Mistral Large 3Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 151.8 | — |
| ECI rank | — | #54 of 165 | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.40 | $0.50 |
| Output | $2.00 | $2.55 | $1.50 (best) |
| Cached input | — | — | $0.05 |
| Blended (3:1) | $0.688 (best) | $0.938 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Median of 49 providers | Official Mistral API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | — | mistral-large-2512 |
| API providers | 16 | 53 (best) | 17 |
| Released | Nov 13, 2025 | Aug 14, 2026 | Dec 2, 2025 |
| 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
- Qwen3.8 27B$9.10
- Mistral Large 3$8.00
Which should you choose?
Which is better: GPT-5.1 Codex mini, Qwen3.8 27B or Mistral Large 3?
It is close. Our weighted score puts them within 2 points (GPT-5.1 Codex mini 54/100, Qwen3.8 27B 53/100, Mistral Large 3 52/100), 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, Qwen3.8 27B or Mistral Large 3?
GPT-5.1 Codex mini is cheaper at $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); Qwen3.8 27B costs $0.40 input / $2.55 output per million tokens (median across 49 API providers). 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 $0.938 for Qwen3.8 27B (1.4× 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, Qwen3.8 27B has an ECI of 151.8 and Mistral Large 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.1 Codex mini, Qwen3.8 27B and Mistral Large 3 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 Qwen3.8 27B and 262,144 for Mistral Large 3. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Qwen3.8 27B up to 32,768, Mistral Large 3 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Codex mini accepts text and images; Qwen3.8 27B accepts text, images and video; Mistral Large 3 accepts text and images. Qwen3.8 27B handles the widest range of inputs.
Are any of these open source?
Qwen3.8 27B and Mistral Large 3 publishes its weights and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 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.
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