GPT-5.1 Codex mini vs Ministral 14B vs Trinity Nano Preview
GPT-5.1 Codex mini comes out ahead, 60 to 45 and 25 on our weighted score, though Ministral 14B is 3.4× cheaper per token.
- Our pick
OpenAIGPT-5.1 Codex mini
60/100- ECI—
- Price$0.25 / $2.00
- Context400K
Mistral AIMinistral 14B
45/100- ECI—
- Price$0.20 / $0.20
- Context262K
Arcee AITrinity Nano Preview
25/100- ECI—
- Price—
- Context131K
GPT-5.1 Codex mini is our pick
GPT-5.1 Codex mini is the better all-round choice, scoring 60/100 against Ministral 14B (45) and Trinity Nano Preview (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · Trinity Nano Preview unpriced
- Longest contextGPT-5.1 Codex miniGPT-5.1 Codex mini 400,000 · Ministral 14B 262,144 · Trinity Nano Preview 131,072 tokens
- Widest inputsGPT-5.1 Codex mini and Ministral 14BGPT-5.1 Codex mini: Text, Images · Ministral 14B: Text, Images · Trinity Nano Preview: Text
- Self-hostingMinistral 14B and Trinity Nano PreviewPublishes downloadable weights (Apache-2.0 and OpenMDW-1.1)
| Measure | Weight | GPT-5.1 Codex mini | Ministral 14B | Trinity Nano Preview |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 50 | 25 |
| Context window | 40% | 44 | 37 | 24 |
| Overall | 100% | 60/100 | 45/100 | 25/100 |
Left out because at least one model lacks the data: capability and price. 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 | Trinity Nano PreviewArcee AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 | $0.20 (best) | — |
| Output | $2.00 | $0.20 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $0.688 | $0.20 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Median of 10 providers | Median of 1 providers | — |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 131,072 tokens |
| Max output | 128,000 tokens | 262,144 tokens (best) | 131,072 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 | No |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | OpenOpenMDW-1.1 |
| API model ID | — | — | — |
| API providers | 10 (best) | 1 | — |
| Released | Nov 13, 2025 | Dec 2, 2025 | Dec 1, 2025 |
| Knowledge cutoff | Sep 30, 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
- Trinity Nano Preview—
Which should you choose?
Which is better: GPT-5.1 Codex mini, Ministral 14B or Trinity Nano Preview?
GPT-5.1 Codex mini is the better all-round choice, scoring 60/100 against Ministral 14B (45) and Trinity Nano Preview (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 Trinity Nano Preview?
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). 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). Trinity Nano Preview has no published per-token price.
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 Trinity Nano Preview 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 Trinity Nano Preview 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 131,072 for Trinity Nano Preview. Maximum output per response: GPT-5.1 Codex mini up to 128,000, Ministral 14B up to 262,144, Trinity Nano Preview up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.1 Codex mini accepts text and images; Ministral 14B accepts text and images; Trinity Nano Preview accepts text. GPT-5.1 Codex mini handles the widest range of inputs.
Are any of these open source?
Ministral 14B and Trinity Nano Preview publishes its weights (Apache-2.0 and OpenMDW-1.1) and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Ministral 14B is the newest, released Dec 2, 2025. Trinity Nano Preview came out Dec 1, 2025; GPT-5.1 Codex mini came out Nov 13, 2025. Knowledge cutoff: GPT-5.1 Codex mini Sep 30, 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.
