GPT-5.4 nano vs Mistral Small 3.2 vs MiniMax-M2.7
GPT-5.4 nano comes out ahead, 68 to 61 and 60 on our weighted score, though Mistral Small 3.2 is 3.1× cheaper per token.
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Mistral AIMistral Small 3.2
60/100- ECI131.7
- Price$0.10 / $0.30
- Context128K
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Mistral Small 3.2 (60). It leads on inputs & features and context window. Mistral Small 3.2 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMiniMax-M2.7Capabilities Index (ECI): MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8 · Mistral Small 3.2 131.7
- Lowest priceMistral Small 3.2Mistral Small 3.2 $0.15 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 · Mistral Small 3.2 128,000 tokens
- Widest inputsGPT-5.4 nano and Mistral Small 3.2GPT-5.4 nano: Text, Images · Mistral Small 3.2: Text, Images · MiniMax-M2.7: Text
- Self-hostingMistral Small 3.2 and MiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GPT-5.4 nano | Mistral Small 3.2 | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 55 | 73 |
| Price | 25% | 66 | 89 | 63 |
| Inputs & features | 15% | 70 | 50 | 35 |
| Context window | 10% | 44 | 24 | 32 |
| Overall | 100% | 68/100 | 60/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | Mistral Small 3.2Mistral AI | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 131.7 | 145.9 (best) |
| ECI rank | #75 of 148 | #123 of 148 | #73 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 78.5% (best) | 49.1% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 44.9% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 30.3% | — |
| SimpleQA VerifiedShort factual questions | 11.7% | — | — |
| Price per million tokens | |||
| Input | $0.20 | $0.10 (best) | $0.30 |
| Output | $1.25 | $0.30 (best) | $1.20 |
| Cached input | $0.02 (best) | — | $0.06 |
| Blended (3:1) | $0.463 | $0.15 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Mistral API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 400,000 tokens (best) | 128,000 tokens | 204,800 tokens |
| Max output | 128,000 tokens | 16,384 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-5.4-nano | mistral-small-2506 | MiniMax-M2.7 |
| API providers | 26 | 6 | 29 (best) |
| Released | Mar 17, 2026 | Jun 20, 2025 | Mar 18, 2026 |
| Knowledge cutoff | Aug 31, 2025 | Mar 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.4 nano$4.50
- Mistral Small 3.2$1.60
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-5.4 nano, Mistral Small 3.2 or MiniMax-M2.7?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Mistral Small 3.2 (60). It leads on inputs & features and context window. Mistral Small 3.2 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.4 nano, Mistral Small 3.2 or MiniMax-M2.7?
Mistral Small 3.2 is cheaper at $0.10 input / $0.30 output per million tokens (official Mistral API price). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price); MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Small 3.2 versus $0.463 for GPT-5.4 nano (3.1× as much) and $0.525 for MiniMax-M2.7 (3.5× as much).
Which scores higher on benchmarks?
MiniMax-M2.7 scores higher on the Capabilities Index (ECI): MiniMax-M2.7 145.9 (#73 of 148), GPT-5.4 nano 145.8 (#75 of 148) and Mistral Small 3.2 131.7 (#123 of 148). The confidence ranges of the top two overlap (138.2–148.0 vs 143.2–147.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, Mistral Small 3.2 and MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, MiniMax-M2.7 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2.7 and 128,000 for Mistral Small 3.2. Maximum output per response: GPT-5.4 nano up to 128,000, Mistral Small 3.2 up to 16,384, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; Mistral Small 3.2 accepts text and images; MiniMax-M2.7 accepts text. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
Mistral Small 3.2 and MiniMax-M2.7 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
MiniMax-M2.7 is the newest, released Mar 18, 2026. GPT-5.4 nano came out Mar 17, 2026; Mistral Small 3.2 came out Jun 20, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, Mistral Small 3.2 Mar 2025.
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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