MiniMax-M2.7 vs DeepSeek V4 Flash vs GPT-5.4 nano
DeepSeek V4 Flash comes out ahead, 71 to 68 and 61 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
- Our pick
DeepSeekDeepSeek V4 Flash
71/100- ECI146.1
- Price$0.14 / $0.28
- Context1M
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
DeepSeek V4 Flash is our pick
DeepSeek V4 Flash is the better all-round choice, scoring 71/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price and context window. GPT-5.4 nano wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4 FlashCapabilities Index (ECI): DeepSeek V4 Flash 146.1 · MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8
- Lowest priceDeepSeek V4 FlashDeepSeek V4 Flash $0.175 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 FlashDeepSeek V4 Flash 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsGPT-5.4 nanoMiniMax-M2.7: Text · DeepSeek V4 Flash: Text · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and DeepSeek V4 FlashPublishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | DeepSeek V4 Flash | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 73 | 73 |
| Price | 25% | 63 | 86 | 66 |
| Inputs & features | 15% | 35 | 45 | 70 |
| Context window | 10% | 32 | 60 | 44 |
| Overall | 100% | 61/100 | 71/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.7MiniMax | DeepSeek V4 FlashDeepSeek | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 | 146.1 (best) | 145.8 |
| ECI rank | #73 of 148 | #71 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 | $0.14 (best) | $0.20 |
| Output | $1.20 | $0.28 (best) | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.175 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 42 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 1,000,000 tokens (best) | 400,000 tokens |
| Max output | 131,072 tokens | 384,000 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 | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | — | gpt-5.4-nano |
| API providers | 29 | 48 (best) | 26 |
| Released | Mar 18, 2026 | Apr 24, 2026 | Mar 17, 2026 |
| Knowledge cutoff | — | May 2025 | 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 V4 Flash$1.96
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, DeepSeek V4 Flash or GPT-5.4 nano?
DeepSeek V4 Flash is the better all-round choice, scoring 71/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price and context window. GPT-5.4 nano wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.7, DeepSeek V4 Flash or GPT-5.4 nano?
DeepSeek V4 Flash is cheaper at $0.14 input / $0.28 output per million tokens (median across 42 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 $0.175 per million tokens for DeepSeek V4 Flash versus $0.463 for GPT-5.4 nano (2.6× as much) and $0.525 for MiniMax-M2.7 (3× as much).
Which scores higher on benchmarks?
DeepSeek V4 Flash scores higher on the Capabilities Index (ECI): DeepSeek V4 Flash 146.1 (#71 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 (143.6–147.9 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 MiniMax-M2.7, DeepSeek V4 Flash and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4 Flash 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?
DeepSeek V4 Flash 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, DeepSeek V4 Flash up to 384,000, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; DeepSeek V4 Flash 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 V4 Flash publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
DeepSeek V4 Flash is the newest, released Apr 24, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: DeepSeek V4 Flash May 2025, 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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