Qwen2.5 72B Instruct vs GPT-5.1 Codex mini vs Mistral Large 2.1
GPT-5.1 Codex mini comes out ahead, 59 to 28 and 26 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen2.5 72B Instruct
28/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
- Our pick
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
Mistral AIMistral Large 2.1
26/100- ECI128.5
- Price$2.00 / $6.00
- 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 · Qwen2.5 72B Instruct 131,072 · Mistral Large 2.1 131,072 tokens
- Widest inputsGPT-5.1 Codex miniQwen2.5 72B Instruct: Text · GPT-5.1 Codex mini: Text, Images · Mistral Large 2.1: Text
- Self-hostingQwen2.5 72B Instruct and Mistral Large 2.1Publishes downloadable weights
| Measure | Weight | Qwen2.5 72B Instruct | GPT-5.1 Codex mini | Mistral Large 2.1 |
|---|---|---|---|---|
| Price | 50% | 31 | 58 | 27 |
| Inputs & features | 30% | 25 | 70 | 25 |
| Context window | 20% | 24 | 44 | 24 |
| Overall | 100% | 28/100 | 59/100 | 26/100 |
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 | Qwen2.5 72B InstructAlibaba (Qwen) | GPT-5.1 Codex miniOpenAI | Mistral Large 2.1Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | — | 128.5 |
| ECI rank | #128 of 148 (best) | — | #130 of 148 |
| GPQA DiamondGraduate-level science questions | 49.2% | — | 51.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 8.1% (best) | — | 7.8% |
| Price per million tokens | |||
| Input | $1.40 | $0.25 (best) | $2.00 |
| Output | $5.60 | $2.00 (best) | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $2.45 | $0.688 (best) | $3.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 10 providers | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens | 400,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 128,000 tokens (best) | 16,384 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 | qwen2-5-72b-instruct | — | mistral-large-2411 |
| API providers | 1 | 10 (best) | 2 |
| Released | Sep 19, 2024 | Nov 13, 2025 | Nov 18, 2024 |
| Knowledge cutoff | Apr 2024 | 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.
- Qwen2.5 72B Instruct$25.20
- GPT-5.1 Codex mini$6.50
- Mistral Large 2.1$32.00
Which should you choose?
Which is better: Qwen2.5 72B Instruct, GPT-5.1 Codex mini or Mistral Large 2.1?
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, Qwen2.5 72B Instruct, GPT-5.1 Codex mini or Mistral Large 2.1?
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. Qwen2.5 72B Instruct has an ECI of 129.0, GPT-5.1 Codex mini has not been scored yet and Mistral Large 2.1 has an ECI of 128.5.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5 72B Instruct, GPT-5.1 Codex mini and Mistral Large 2.1 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 Qwen2.5 72B Instruct and 131,072 for Mistral Large 2.1. Maximum output per response: Qwen2.5 72B Instruct up to 8,192, GPT-5.1 Codex mini up to 128,000, Mistral Large 2.1 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 72B Instruct accepts text; GPT-5.1 Codex mini accepts text and images; Mistral Large 2.1 accepts text. GPT-5.1 Codex mini handles the widest range of inputs.
Are any of these open source?
Qwen2.5 72B Instruct and Mistral Large 2.1 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: Qwen2.5 72B Instruct Apr 2024, GPT-5.1 Codex mini Sep 30, 2024, Mistral Large 2.1 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.
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