Mistral Large 2.1 vs GPT-5.1 Codex mini vs Qwen2.5 72B Instruct
GPT-5.1 Codex mini comes out ahead, 59 to 28 and 26 on our weighted score, and it is the cheaper option too.
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Our pick
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
Alibaba (Qwen)Qwen2.5 72B Instruct
28/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
GPT-5.1 Codex mini is our pick
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen2.5 72B Instruct (28) and Mistral Large 2.1 (26). It leads on price, inputs & features and context window. 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 · Qwen2.5 72B Instruct $2.45 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · Mistral Large 2.1 131,072 · Qwen2.5 72B Instruct 131,072 tokens
- Widest inputsGPT-5.1 Codex miniMistral Large 2.1: Text · GPT-5.1 Codex mini: Text, Images · Qwen2.5 72B Instruct: Text
- Self-hostingMistral Large 2.1 and Qwen2.5 72B InstructPublishes downloadable weights
| Measure | Weight | Mistral Large 2.1 | GPT-5.1 Codex mini | Qwen2.5 72B Instruct |
|---|---|---|---|---|
| Price | 50% | 27 | 58 | 31 |
| Inputs & features | 30% | 25 | 70 | 25 |
| Context window | 20% | 24 | 44 | 24 |
| Overall | 100% | 26/100 | 59/100 | 28/100 |
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 | Mistral Large 2.1Mistral AI | GPT-5.1 Codex miniOpenAI | Qwen2.5 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.5 | — | 129.0 (best) |
| ECI rank | #130 of 148 | — | #128 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | — | 49.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | — | 8.1% (best) |
| Price per million tokens | |||
| Input | $2.00 | $0.25 (best) | $1.40 |
| Output | $6.00 | $2.00 (best) | $5.60 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.688 (best) | $2.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Median of 10 providers | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 400,000 tokens (best) | 131,072 tokens |
| Max output | 16,384 tokens | 128,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | mistral-large-2411 | — | qwen2-5-72b-instruct |
| API providers | 2 | 10 (best) | 1 |
| Released | Nov 18, 2024 | Nov 13, 2025 | Sep 19, 2024 |
| Knowledge cutoff | Nov 2024 | Sep 30, 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Large 2.1$32.00
- GPT-5.1 Codex mini$6.50
- Qwen2.5 72B Instruct$25.20
Which should you choose?
Which is better: Mistral Large 2.1, GPT-5.1 Codex mini or Qwen2.5 72B Instruct?
GPT-5.1 Codex mini is the better all-round choice, scoring 59/100 against Qwen2.5 72B Instruct (28) and Mistral Large 2.1 (26). It leads on price, inputs & features and context window. 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, Mistral Large 2.1, GPT-5.1 Codex mini or Qwen2.5 72B Instruct?
GPT-5.1 Codex mini is cheaper at $0.25 input / $2.00 output per million tokens (median across 10 API providers). Qwen2.5 72B Instruct costs $1.40 input / $5.60 output per million tokens (official Alibaba API price); Mistral Large 2.1 costs $2.00 input / $6.00 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 $2.45 for Qwen2.5 72B Instruct (3.6× as much) and $3.00 for Mistral Large 2.1 (4.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral Large 2.1 has an ECI of 128.5, GPT-5.1 Codex mini has not been scored yet and Qwen2.5 72B Instruct has an ECI of 129.0.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1, GPT-5.1 Codex mini and Qwen2.5 72B Instruct 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 131,072 for Mistral Large 2.1 and 131,072 for Qwen2.5 72B Instruct. Maximum output per response: Mistral Large 2.1 up to 16,384, GPT-5.1 Codex mini up to 128,000, Qwen2.5 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; GPT-5.1 Codex mini accepts text and images; Qwen2.5 72B Instruct accepts text. GPT-5.1 Codex mini handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Qwen2.5 72B Instruct publishes its weights and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
GPT-5.1 Codex mini is the newest, released Nov 13, 2025. Mistral Large 2.1 came out Nov 18, 2024; Qwen2.5 72B Instruct came out Sep 19, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, GPT-5.1 Codex mini Sep 30, 2024, Qwen2.5 72B Instruct Apr 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.
