MiniMax-M2.7 vs DeepSeek V3 0324 vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 68 to 61 and 54 on our weighted score, though DeepSeek V3 0324 is 12% cheaper per token.
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
DeepSeekDeepSeek V3 0324
54/100- ECI135.9
- Price$0.24 / $0.90
- Context164K
- Our pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
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 0324 (54). It leads on inputs & features and context window. DeepSeek V3 0324 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 · DeepSeek V3 0324 135.9
- Lowest priceDeepSeek V3 0324DeepSeek V3 0324 $0.405 · 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 0324 163,840 tokens
- Widest inputsGPT-5.4 nanoMiniMax-M2.7: Text · DeepSeek V3 0324: Text · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and DeepSeek V3 0324Publishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | DeepSeek V3 0324 | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 60 | 73 |
| Price | 25% | 63 | 68 | 66 |
| Inputs & features | 15% | 35 | 25 | 70 |
| Context window | 10% | 32 | 28 | 44 |
| Overall | 100% | 61/100 | 54/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | DeepSeek V3 0324DeepSeek | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | 135.9 | 145.8 |
| ECI rank | #73 of 148 (best) | #114 of 148 | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | 67.6% | 78.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 37.8% | 87.8% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.24 | $0.20 (best) |
| Output | $1.20 | $0.90 (best) | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.405 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 5 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 163,840 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens | 163,840 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | — | gpt-5.4-nano |
| API providers | 29 (best) | 5 | 26 |
| Released | Mar 18, 2026 | Mar 24, 2025 | 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
- DeepSeek V3 0324$4.20
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, DeepSeek V3 0324 or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and DeepSeek V3 0324 (54). It leads on inputs & features and context window. DeepSeek V3 0324 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.7, DeepSeek V3 0324 or GPT-5.4 nano?
DeepSeek V3 0324 is cheaper at $0.24 input / $0.90 output per million tokens (median across 5 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 $0.405 per million tokens for DeepSeek V3 0324 versus $0.463 for GPT-5.4 nano (1.1× as much) and $0.525 for MiniMax-M2.7 (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 0324 135.9 (#114 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 MiniMax-M2.7, DeepSeek V3 0324 and GPT-5.4 nano 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 163,840 for DeepSeek V3 0324. Maximum output per response: MiniMax-M2.7 up to 131,072, DeepSeek V3 0324 up to 163,840, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; DeepSeek V3 0324 accepts text; GPT-5.4 nano accepts text and images. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and DeepSeek V3 0324 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; DeepSeek V3 0324 came out Mar 24, 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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