GPT-5.1 Codex mini vs Mistral Large 3 vs Qwen3 Coder Next
GPT-5.1 Codex mini comes out ahead, 59 to 51 and 50 on our weighted score, though Qwen3 Coder Next is 35% cheaper per token.
- Our pick
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 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
GPT-5.1 Codex mini is our pick
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51) and Mistral Large 3 (50). It leads on inputs & features and context window. Qwen3 Coder Next wins on price. 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 Coder NextQwen3 Coder Next $0.45 · 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 Coder Next 262,144 tokens
- Widest inputsGPT-5.1 Codex mini and Mistral Large 3GPT-5.1 Codex mini: Text, Images · Mistral Large 3: Text, Images · Qwen3 Coder Next: Text
- Self-hostingMistral Large 3 and Qwen3 Coder NextPublishes downloadable weights
| Measure | Weight | GPT-5.1 Codex mini | Mistral Large 3 | Qwen3 Coder Next |
|---|---|---|---|---|
| Price | 50% | 58 | 56 | 66 |
| Inputs & features | 30% | 70 | 50 | 35 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 59/100 | 50/100 | 51/100 |
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 Coder NextAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.50 | $0.20 (best) |
| Output | $2.00 | $1.50 | $1.20 (best) |
| Cached input | — | $0.05 | — |
| Blended (3:1) | $0.688 | $0.75 | $0.45 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Official Mistral API | Median of 11 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 | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | mistral-large-2512 | — |
| API providers | 10 | 13 (best) | 11 |
| Released | Nov 13, 2025 | Dec 2, 2025 | Feb 3, 2026 |
| Knowledge cutoff | Sep 30, 2024 | Nov 2024 | Sep 2025 |
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 Coder Next$4.40
Which should you choose?
Which is better: GPT-5.1 Codex mini, Mistral Large 3 or Qwen3 Coder Next?
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen3 Coder Next (51) and Mistral Large 3 (50). It leads on inputs & features and context window. Qwen3 Coder Next wins on price. 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 Coder Next?
Qwen3 Coder Next is cheaper at $0.20 input / $1.20 output per million tokens (median across 11 API providers). GPT-5.1 Codex mini costs $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). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Qwen3 Coder Next versus $0.688 for GPT-5.1 Codex mini (1.5× as much) and $0.75 for Mistral Large 3 (1.7× 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 Coder Next 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 Coder Next 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 Coder Next. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Mistral Large 3 up to 262,144, Qwen3 Coder Next 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 Coder Next accepts text. GPT-5.1 Codex mini handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 and Qwen3 Coder Next publishes its weights and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 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, Qwen3 Coder Next Sep 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.
