GPT-4.1 nano vs MiniMax-M2.7
Too close to call on our weighted score (GPT-4.1 nano 63, MiniMax-M2.7 61). The right pick depends on what you value most.
OpenAIGPT-4.1 nano
63/100- ECI129.6
- Price$0.10 / $0.40
- Context1.05M
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-4.1 nano 63/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: MiniMax-M2.7 for raw capability, GPT-4.1 nano on price and GPT-4.1 nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMiniMax-M2.7Capabilities Index (ECI): MiniMax-M2.7 145.9 · GPT-4.1 nano 129.6
- Lowest priceGPT-4.1 nanoGPT-4.1 nano $0.175 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextGPT-4.1 nanoGPT-4.1 nano 1,047,576 · MiniMax-M2.7 204,800 tokens
- Widest inputsGPT-4.1 nanoGPT-4.1 nano: Text, Images · MiniMax-M2.7: Text
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GPT-4.1 nano | MiniMax-M2.7 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 52 | 73 |
| Price | 25% | 86 | 63 |
| Inputs & features | 15% | 60 | 35 |
| Context window | 10% | 61 | 32 |
| Overall | 100% | 63/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-4.1 nanoOpenAI | MiniMax-M2.7MiniMax |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 129.6 | 145.9 (best) |
| ECI rank | #127 of 148 | #73 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 48.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 28.9% | — |
| SimpleQA VerifiedShort factual questions | 6.0% | — |
| Price per million tokens | ||
| Input | $0.10 (best) | $0.30 |
| Output | $0.40 (best) | $1.20 |
| Cached input | $0.025 (best) | $0.06 |
| Blended (3:1) | $0.175 (best) | $0.525 |
| Long-context rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official MiniMax (minimax.io) API |
| Limits | ||
| Context window | 1,047,576 tokens (best) | 204,800 tokens |
| Max output | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | gpt-4.1-nano | MiniMax-M2.7 |
| API providers | 20 | 29 (best) |
| Released | Apr 14, 2025 | Mar 18, 2026 |
| Knowledge cutoff | Apr 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-4.1 nano$1.80
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-4.1 nano or MiniMax-M2.7?
It is close. Our weighted score puts them within 2 points (GPT-4.1 nano 63/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: MiniMax-M2.7 for raw capability, GPT-4.1 nano on price and GPT-4.1 nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-4.1 nano or MiniMax-M2.7?
GPT-4.1 nano is cheaper at $0.10 input / $0.40 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 $0.175 per million tokens for GPT-4.1 nano versus $0.525 for MiniMax-M2.7 (3× 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) and GPT-4.1 nano 129.6 (#127 of 148). Their confidence ranges do not overlap (138.2–148.0 vs 123.2–132.0), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4.1 nano 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. Both support tool calling for agent workflows.
Which has the bigger context window?
GPT-4.1 nano has the largest context window at 1,047,576 tokens, against 204,800 for MiniMax-M2.7. Maximum output per response: GPT-4.1 nano up to 32,768, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-4.1 nano accepts text and images; MiniMax-M2.7 accepts text. GPT-4.1 nano handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 publishes its weights and can be self-hosted; GPT-4.1 nano is proprietary.
Which is newer?
MiniMax-M2.7 is the newest, released Mar 18, 2026. GPT-4.1 nano came out Apr 14, 2025. Knowledge cutoff: GPT-4.1 nano Apr 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.
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