Kimi K2 Thinking Turbo vs Ministral 3 14B vs GPT-5.1 Chat
Too close to call on our weighted score (GPT-5.1 Chat 52, Ministral 3 14B 51, Kimi K2 Thinking Turbo 36). The right pick depends on what you value most.
Moonshot AIKimi K2 Thinking Turbo
36/100- ECI—
- Price—
- Context262K
Mistral AIMinistral 3 14B
51/100- ECI—
- Price$0.268 / $0.325
- Context262K
OpenAIGPT-5.1 Chat
52/100- ECI—
- Price$1.25 / $10.00
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (GPT-5.1 Chat 52/100, Ministral 3 14B 51/100, Kimi K2 Thinking Turbo 36/100), so choose by what matters most for your work: Ministral 3 14B on price. 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 3 14BMinistral 3 14B $0.282 · GPT-5.1 Chat $3.44 per 1M tokens (3:1 blend) · Kimi K2 Thinking Turbo unpriced
- Longest contextKimi K2 Thinking Turbo and Ministral 3 14BKimi K2 Thinking Turbo 262,144 · Ministral 3 14B 262,144 · GPT-5.1 Chat 128,000 tokens
- Widest inputsMinistral 3 14B and GPT-5.1 ChatKimi K2 Thinking Turbo: Text · Ministral 3 14B: Text, Images · GPT-5.1 Chat: Text, Images
- Self-hostingKimi K2 Thinking Turbo and Ministral 3 14BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Kimi K2 Thinking Turbo | Ministral 3 14B | GPT-5.1 Chat |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 60 | 70 |
| Context window | 40% | 37 | 37 | 24 |
| Overall | 100% | 36/100 | 51/100 | 52/100 |
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 | Kimi K2 Thinking TurboMoonshot AI | Ministral 3 14BMistral AI | GPT-5.1 ChatOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.268 (best) | $1.25 |
| Output | — | $0.325 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | — | $0.282 (best) | $3.44 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 2 providers | Median of 2 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 128,000 tokens |
| Max output | 262,144 tokens (best) | 262,144 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Proprietary |
| API model ID | — | — | — |
| API providers | — | 2 | 2 |
| Released | Nov 6, 2025 | Dec 2, 2025 | Nov 13, 2025 |
| Knowledge cutoff | Aug 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.
- Kimi K2 Thinking Turbo—
- Ministral 3 14B$3.33
- GPT-5.1 Chat$32.50
Which should you choose?
Which is better: Kimi K2 Thinking Turbo, Ministral 3 14B or GPT-5.1 Chat?
It is close. Our weighted score puts them within a point (GPT-5.1 Chat 52/100, Ministral 3 14B 51/100, Kimi K2 Thinking Turbo 36/100), so choose by what matters most for your work: Ministral 3 14B on price. 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, Kimi K2 Thinking Turbo, Ministral 3 14B or GPT-5.1 Chat?
Ministral 3 14B is cheaper at $0.268 input / $0.325 output per million tokens (median across 2 API providers). GPT-5.1 Chat costs $1.25 input / $10.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.282 per million tokens for Ministral 3 14B versus $3.44 for GPT-5.1 Chat (12× as much). Kimi K2 Thinking Turbo has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Kimi K2 Thinking Turbo has not been scored yet, Ministral 3 14B has not been scored yet and GPT-5.1 Chat has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Kimi K2 Thinking Turbo, Ministral 3 14B and GPT-5.1 Chat 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?
Kimi K2 Thinking Turbo and Ministral 3 14B have the largest context windows (262,144 and 262,144 tokens), against 128,000 for GPT-5.1 Chat. Maximum output per response: Kimi K2 Thinking Turbo up to 262,144, Ministral 3 14B up to 262,144, GPT-5.1 Chat up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Kimi K2 Thinking Turbo accepts text; Ministral 3 14B accepts text and images; GPT-5.1 Chat accepts text and images. Ministral 3 14B handles the widest range of inputs.
Are any of these open source?
Kimi K2 Thinking Turbo and Ministral 3 14B publishes its weights (Apache 2.0) and can be self-hosted; GPT-5.1 Chat is proprietary.
Which is newer?
Ministral 3 14B is the newest, released Dec 2, 2025. GPT-5.1 Chat came out Nov 13, 2025; Kimi K2 Thinking Turbo came out Nov 6, 2025. Knowledge cutoff: Kimi K2 Thinking Turbo Aug 2024, GPT-5.1 Chat 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.
