Mistral Large 3 vs Inkling Small vs GPT-5.1 Codex mini
Inkling Small comes out ahead, 59 to 54 and 52 on our weighted score, though GPT-5.1 Codex mini is 14% cheaper per token.
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
- Our pick
Thinking MachinesInkling Small
59/100- ECI150.5
- Price$0.58 / $1.44
- Context1.05M
OpenAIGPT-5.1 Codex mini
54/100- ECI—
- Price$0.25 / $2.00
- Context400K
Inkling Small is our pick
Inkling Small is the better all-round choice, scoring 59/100 against GPT-5.1 Codex mini (54) and Mistral Large 3 (52). It leads on inputs & features and context window. GPT-5.1 Codex mini 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 priceGPT-5.1 Codex miniGPT-5.1 Codex mini $0.688 · Mistral Large 3 $0.75 · Inkling Small $0.795 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · GPT-5.1 Codex mini 400,000 · Mistral Large 3 262,144 tokens
- Widest inputsInkling SmallMistral Large 3: Text, Images · Inkling Small: Text, Images, Audio · GPT-5.1 Codex mini: Text, Images
- Self-hostingMistral Large 3 and Inkling SmallPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Mistral Large 3 | Inkling Small | GPT-5.1 Codex mini |
|---|---|---|---|---|
| Price | 50% | 53 | 52 | 55 |
| Inputs & features | 30% | 60 | 70 | 60 |
| Context window | 20% | 37 | 61 | 44 |
| Overall | 100% | 52/100 | 59/100 | 54/100 |
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 3Mistral AI | Inkling SmallThinking Machines | GPT-5.1 Codex miniOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 150.5 | — |
| ECI rank | — | #58 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 88.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 46.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 90.0% | — |
| SimpleQA VerifiedShort factual questions | — | 19.1% | — |
| Price per million tokens | |||
| Input | $0.50 | $0.58 | $0.25 (best) |
| Output | $1.50 | $1.44 (best) | $2.00 |
| Cached input | $0.05 (best) | $0.116 | — |
| Blended (3:1) | $0.75 | $0.795 | $0.688 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Thinking Machines API | Median of 16 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,048,576 tokens (best) | 400,000 tokens |
| Max output | 262,144 tokens | 1,048,576 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Proprietary |
| API model ID | mistral-large-2512 | thinkingmachines/Inkling-Small | — |
| API providers | 17 (best) | 12 | 16 |
| Released | Dec 2, 2025 | Jul 30, 2026 | Nov 13, 2025 |
| Knowledge cutoff | Nov 2024 | — | 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.
- Mistral Large 3$8.00
- Inkling Small$8.68
- GPT-5.1 Codex mini$6.50
Which should you choose?
Which is better: Mistral Large 3, Inkling Small or GPT-5.1 Codex mini?
Inkling Small is the better all-round choice, scoring 59/100 against GPT-5.1 Codex mini (54) and Mistral Large 3 (52). It leads on inputs & features and context window. GPT-5.1 Codex mini 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, Mistral Large 3, Inkling Small or GPT-5.1 Codex mini?
GPT-5.1 Codex mini is cheaper at $0.25 input / $2.00 output per million tokens (median across 16 API providers). Mistral Large 3 costs $0.50 input / $1.50 output per million tokens (official Mistral API price); Inkling Small costs $0.58 input / $1.44 output per million tokens (official Thinking Machines 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 $0.75 for Mistral Large 3 (1.1× as much) and $0.795 for Inkling Small (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral Large 3 has not been scored yet, Inkling Small has an ECI of 150.5 and GPT-5.1 Codex mini has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 3, Inkling Small and GPT-5.1 Codex mini 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?
Inkling Small has the largest context window at 1,048,576 tokens, against 400,000 for GPT-5.1 Codex mini and 262,144 for Mistral Large 3. Maximum output per response: Mistral Large 3 up to 262,144, Inkling Small up to 1,048,576, GPT-5.1 Codex mini up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 3 accepts text and images; Inkling Small accepts text, images and audio; GPT-5.1 Codex mini accepts text and images. Inkling Small handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 and Inkling Small publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.1 Codex mini is proprietary.
Which is newer?
Inkling Small is the newest, released Jul 30, 2026. Mistral Large 3 came out Dec 2, 2025; GPT-5.1 Codex mini came out Nov 13, 2025. Knowledge cutoff: Mistral Large 3 Nov 2024, 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 Nov 2, 2026.
