GPT-5.4 nano vs DeepSeek-V3.1 vs MiniMax-M2.7
GPT-5.4 nano comes out ahead, 68 to 61 and 55 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
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 DeepSeek-V3.1 (55). It leads on price, inputs & features and context window. 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 · DeepSeek-V3.1 139.9
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · DeepSeek-V3.1 $0.601 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 · DeepSeek-V3.1 131,072 tokens
- Widest inputsGPT-5.4 nanoGPT-5.4 nano: Text, Images · DeepSeek-V3.1: Text · MiniMax-M2.7: Text
- Self-hostingDeepSeek-V3.1 and MiniMax-M2.7Publishes downloadable weights (MIT License)
| Measure | Weight | GPT-5.4 nano | DeepSeek-V3.1 | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 65 | 73 |
| Price | 25% | 66 | 60 | 63 |
| Inputs & features | 15% | 70 | 35 | 35 |
| Context window | 10% | 44 | 24 | 32 |
| Overall | 100% | 68/100 | 55/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | DeepSeek-V3.1DeepSeek | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 139.9 | 145.9 (best) |
| ECI rank | #75 of 148 | #100 of 148 | #73 of 148 (best) |
| 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 | $0.20 (best) | $0.385 | $0.30 |
| Output | $1.25 | $1.25 | $1.20 (best) |
| Cached input | $0.02 (best) | — | $0.06 |
| Blended (3:1) | $0.463 (best) | $0.601 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 8 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 400,000 tokens (best) | 131,072 tokens | 204,800 tokens |
| Max output | 128,000 tokens | 8,192 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenMIT License | Open |
| API model ID | gpt-5.4-nano | — | MiniMax-M2.7 |
| API providers | 26 | 8 | 29 (best) |
| Released | Mar 17, 2026 | Aug 21, 2025 | Mar 18, 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.
- GPT-5.4 nano$4.50
- DeepSeek-V3.1$6.35
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-5.4 nano, DeepSeek-V3.1 or MiniMax-M2.7?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and DeepSeek-V3.1 (55). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.4 nano, DeepSeek-V3.1 or MiniMax-M2.7?
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); DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 API providers). 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) and $0.601 for DeepSeek-V3.1 (1.3× 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), GPT-5.4 nano 145.8 (#75 of 148) and DeepSeek-V3.1 139.9 (#100 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, DeepSeek-V3.1 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 131,072 for DeepSeek-V3.1. Maximum output per response: GPT-5.4 nano up to 128,000, DeepSeek-V3.1 up to 8,192, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; DeepSeek-V3.1 accepts text; MiniMax-M2.7 accepts text. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
DeepSeek-V3.1 and MiniMax-M2.7 publishes its weights (MIT License) 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; DeepSeek-V3.1 came out Aug 21, 2025. 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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