DeepSeek V3.2 vs GPT-5.4 nano vs MiniMax-M2.7
GPT-5.4 nano comes out ahead, 68 to 64 and 61 on our weighted score, though DeepSeek V3.2 is 26% cheaper per token.
DeepSeekDeepSeek V3.2
64/100- ECI146.3
- Price$0.296 / $0.48
- Context128K
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
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 DeepSeek V3.2 (64) and MiniMax-M2.7 (61). It leads on inputs & features and context window. DeepSeek V3.2 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V3.2Capabilities Index (ECI): DeepSeek V3.2 146.3 · MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8
- Lowest priceDeepSeek V3.2DeepSeek V3.2 $0.342 · 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 · DeepSeek V3.2 128,000 tokens
- Widest inputsGPT-5.4 nanoDeepSeek V3.2: Text · GPT-5.4 nano: Text, Images · MiniMax-M2.7: Text
- Self-hostingDeepSeek V3.2 and MiniMax-M2.7Publishes downloadable weights (MIT License)
| Measure | Weight | DeepSeek V3.2 | GPT-5.4 nano | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 73 | 73 |
| Price | 25% | 72 | 66 | 63 |
| Inputs & features | 15% | 45 | 70 | 35 |
| Context window | 10% | 24 | 44 | 32 |
| Overall | 100% | 64/100 | 68/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V3.2DeepSeek | GPT-5.4 nanoOpenAI | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.3 (best) | 145.8 | 145.9 |
| ECI rank | #69 of 148 (best) | #75 of 148 | #73 of 148 |
| GPQA DiamondGraduate-level science questions | 83.4% (best) | 78.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 44.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 87.8% | — |
| SimpleQA VerifiedShort factual questions | — | 11.7% | — |
| Price per million tokens | |||
| Input | $0.296 | $0.20 (best) | $0.30 |
| Output | $0.48 (best) | $1.25 | $1.20 |
| Cached input | — | $0.02 (best) | $0.06 |
| Blended (3:1) | $0.342 (best) | $0.463 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 15 providers | Official OpenAI API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 128,000 tokens | 400,000 tokens (best) | 204,800 tokens |
| Max output | 64,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | OpenMIT License | Proprietary | Open |
| API model ID | — | gpt-5.4-nano | MiniMax-M2.7 |
| API providers | 15 | 26 | 29 (best) |
| Released | Dec 1, 2025 | Mar 17, 2026 | Mar 18, 2026 |
| Knowledge cutoff | Jul 2024 | 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.
- DeepSeek V3.2$3.92
- GPT-5.4 nano$4.50
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: DeepSeek V3.2, GPT-5.4 nano or MiniMax-M2.7?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against DeepSeek V3.2 (64) and MiniMax-M2.7 (61). It leads on inputs & features and context window. DeepSeek V3.2 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V3.2, GPT-5.4 nano or MiniMax-M2.7?
DeepSeek V3.2 is cheaper at $0.296 input / $0.48 output per million tokens (median across 15 API providers). 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 $0.342 per million tokens for DeepSeek V3.2 versus $0.463 for GPT-5.4 nano (1.4× as much) and $0.525 for MiniMax-M2.7 (1.5× as much).
Which scores higher on benchmarks?
DeepSeek V3.2 scores higher on the Capabilities Index (ECI): DeepSeek V3.2 146.3 (#69 of 148), MiniMax-M2.7 145.9 (#73 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (144.4–147.5 vs 138.2–148.0), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V3.2, GPT-5.4 nano and MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, DeepSeek V3.2 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 DeepSeek V3.2. Maximum output per response: DeepSeek V3.2 up to 64,000, GPT-5.4 nano up to 128,000, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V3.2 accepts text; GPT-5.4 nano 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?
DeepSeek V3.2 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 V3.2 came out Dec 1, 2025. Knowledge cutoff: DeepSeek V3.2 Jul 2024, 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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