MiniMax-M2.7 vs MiniMax-M3.1-Flash-Preview vs GPT-5.4 nano
MiniMax-M3.1-Flash-Preview comes out ahead, 66 to 60 and 34 on our weighted score.
MiniMaxMiniMax-M2.7
34/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
- Our pickMiniMax
MiniMax-M3.1-Flash-Preview
66/100- ECI—
- Price—
- Context1M
OpenAIGPT-5.4 nano
60/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
MiniMax-M3.1-Flash-Preview is our pick
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against GPT-5.4 nano (60) and MiniMax-M2.7 (34). It leads on 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 priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend) · MiniMax-M3.1-Flash-Preview unpriced
- Longest contextMiniMax-M3.1-Flash-PreviewMiniMax-M3.1-Flash-Preview 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsMiniMax-M3.1-Flash-PreviewMiniMax-M2.7: Text · MiniMax-M3.1-Flash-Preview: Text, Images, Video · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | MiniMax-M3.1-Flash-Preview | GPT-5.4 nano |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 70 | 70 |
| Context window | 40% | 32 | 60 | 44 |
| Overall | 100% | 34/100 | 66/100 | 60/100 |
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 | MiniMax-M2.7MiniMax | MiniMax-M3.1-Flash-PreviewMiniMax | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | — | 145.8 |
| ECI rank | #73 of 148 (best) | — | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 87.8% |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | — | $0.20 (best) |
| Output | $1.20 (best) | — | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | — | $0.463 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official MiniMax (minimax.io) API | — | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 1,000,000 tokens (best) | 400,000 tokens |
| Max output | 131,072 tokens | 512,000 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | MiniMax-M2.7 | — | gpt-5.4-nano |
| API providers | 29 (best) | — | 26 |
| Released | Mar 18, 2026 | Sep 27, 2026 | Mar 17, 2026 |
| Knowledge cutoff | — | — | Aug 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiniMax-M2.7$5.40
- MiniMax-M3.1-Flash-Preview—
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, MiniMax-M3.1-Flash-Preview or GPT-5.4 nano?
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against GPT-5.4 nano (60) and MiniMax-M2.7 (34). It leads on 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, MiniMax-M2.7, MiniMax-M3.1-Flash-Preview or GPT-5.4 nano?
GPT-5.4 nano is cheaper at $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 $0.463 per million tokens for GPT-5.4 nano versus $0.525 for MiniMax-M2.7 (1.1× as much). MiniMax-M3.1-Flash-Preview has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.7 has an ECI of 145.9, MiniMax-M3.1-Flash-Preview has not been scored yet and GPT-5.4 nano has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.7, MiniMax-M3.1-Flash-Preview and GPT-5.4 nano 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?
MiniMax-M3.1-Flash-Preview has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.4 nano and 204,800 for MiniMax-M2.7. Maximum output per response: MiniMax-M2.7 up to 131,072, MiniMax-M3.1-Flash-Preview up to 512,000, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; MiniMax-M3.1-Flash-Preview accepts text, images and video; GPT-5.4 nano accepts text and images. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 publishes its weights and can be self-hosted; MiniMax-M3.1-Flash-Preview and GPT-5.4 nano is proprietary.
Which is newer?
MiniMax-M3.1-Flash-Preview is the newest, released Sep 27, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 nano Aug 31, 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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