GPT-5.4 nano vs Qwen3.5 Flash vs MiniMax-M2.7
Qwen3.5 Flash comes out ahead, 75 to 68 and 61 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
- Our pick
Alibaba (Qwen)Qwen3.5 Flash
75/100- ECI144.0
- Price$0.10 / $0.40
- Context1M
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Qwen3.5 Flash is our pick
Qwen3.5 Flash is the better all-round choice, scoring 75/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). 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 · Qwen3.5 Flash 144.0
- Lowest priceQwen3.5 FlashQwen3.5 Flash $0.175 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsQwen3.5 FlashGPT-5.4 nano: Text, Images · Qwen3.5 Flash: Text, Images, Video · MiniMax-M2.7: Text
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GPT-5.4 nano | Qwen3.5 Flash | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 71 | 73 |
| Price | 25% | 66 | 86 | 63 |
| Inputs & features | 15% | 70 | 80 | 35 |
| Context window | 10% | 44 | 60 | 32 |
| Overall | 100% | 68/100 | 75/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | Qwen3.5 FlashAlibaba (Qwen) | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 144.0 | 145.9 (best) |
| ECI rank | #75 of 148 | #82 of 148 | #73 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 78.5% | 82.3% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 44.9% (best) | 18.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 84.4% | — |
| SimpleQA VerifiedShort factual questions | 11.7% | 20.3% (best) | — |
| Price per million tokens | |||
| Input | $0.20 | $0.10 (best) | $0.30 |
| Output | $1.25 | $0.40 (best) | $1.20 |
| Cached input | $0.02 | $0.01 (best) | $0.06 |
| Blended (3:1) | $0.463 | $0.175 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Alibaba API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 400,000 tokens | 1,000,000 tokens (best) | 204,800 tokens |
| Max output | 128,000 tokens | 65,536 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high · xhigh | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.4-nano | qwen3.5-flash | MiniMax-M2.7 |
| API providers | 26 | 8 | 29 (best) |
| Released | Mar 17, 2026 | Feb 23, 2026 | 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
- Qwen3.5 Flash$1.80
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-5.4 nano, Qwen3.5 Flash or MiniMax-M2.7?
Qwen3.5 Flash is the better all-round choice, scoring 75/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). 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, Qwen3.5 Flash or MiniMax-M2.7?
Qwen3.5 Flash is cheaper at $0.10 input / $0.40 output per million tokens (official Alibaba API price). 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 Qwen3.5 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?
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 Qwen3.5 Flash 144.0 (#82 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, Qwen3.5 Flash 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?
Qwen3.5 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: GPT-5.4 nano up to 128,000, Qwen3.5 Flash up to 65,536, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; Qwen3.5 Flash accepts text, images and video; MiniMax-M2.7 accepts text. Qwen3.5 Flash handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 publishes its weights and can be self-hosted; GPT-5.4 nano and Qwen3.5 Flash is proprietary.
Which is newer?
MiniMax-M2.7 is the newest, released Mar 18, 2026. GPT-5.4 nano came out Mar 17, 2026; Qwen3.5 Flash came out Feb 23, 2026. 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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