GPT-5.1 Codex mini vs Ministral 14B vs Mistral Large 3
Ministral 14B comes out ahead, 64 to 59 and 50 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.1 Codex mini
59/100- ECI—
- Price$0.25 / $2.00
- Context400K
- Our pick
Mistral AIMinistral 14B
64/100- ECI—
- Price$0.20 / $0.20
- Context262K
- Mistral AI
Mistral Large 3
50/100- ECI—
- Price$0.50 / $1.50
- Context262K
Ministral 14B is our pick
Ministral 14B is the better all-round choice, scoring 64/100 against GPT-5.1 Codex mini (59) and Mistral Large 3 (50). It leads on price. GPT-5.1 Codex mini wins on 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 priceMinistral 14BMinistral 14B $0.20 · 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 · Ministral 14B 262,144 · Mistral Large 3 262,144 tokens
- Widest inputsSame inputsGPT-5.1 Codex mini: Text, Images · Ministral 14B: Text, Images · Mistral Large 3: Text, Images
- Self-hostingMinistral 14B and Mistral Large 3Publishes downloadable weights (Apache-2.0)
| Measure | Weight | GPT-5.1 Codex mini | Ministral 14B | Mistral Large 3 |
|---|---|---|---|---|
| Price | 50% | 58 | 83 | 56 |
| Inputs & features | 30% | 70 | 50 | 50 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 59/100 | 64/100 | 50/100 |
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 | Ministral 14BMistral AI | Mistral Large 3Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.20 (best) | $0.50 |
| Output | $2.00 | $0.20 (best) | $1.50 |
| Cached input | — | — | $0.05 |
| Blended (3:1) | $0.688 | $0.20 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 10 providers | Median of 1 providers | Official Mistral API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 262,144 tokens (best) | 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 | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | Open |
| API model ID | — | — | mistral-large-2512 |
| API providers | 10 | 1 | 13 (best) |
| Released | Nov 13, 2025 | Dec 2, 2025 | 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
- Ministral 14B$2.40
- Mistral Large 3$8.00
Which should you choose?
Which is better: GPT-5.1 Codex mini, Ministral 14B or Mistral Large 3?
Ministral 14B is the better all-round choice, scoring 64/100 against GPT-5.1 Codex mini (59) and Mistral Large 3 (50). It leads on price. GPT-5.1 Codex mini wins on 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, GPT-5.1 Codex mini, Ministral 14B or Mistral Large 3?
Ministral 14B is cheaper at $0.20 input / $0.20 output per million tokens (median across 1 API provider). 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 $0.20 per million tokens for Ministral 14B versus $0.688 for GPT-5.1 Codex mini (3.4× as much) and $0.75 for Mistral Large 3 (3.8× 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, Ministral 14B has not been scored yet 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, Ministral 14B 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 Ministral 14B and 262,144 for Mistral Large 3. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Ministral 14B up to 262,144, 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; Ministral 14B accepts text and images; Mistral Large 3 accepts text and images. They handle the same number of input types.
Are any of these open source?
Ministral 14B and Mistral Large 3 publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Ministral 14B is the newest, released Dec 2, 2025. 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.
