GPT-5.4 nano vs Qwen3.6 Plus vs MiniMax-M2.7
Too close to call on our weighted score (GPT-5.4 nano 68, Qwen3.6 Plus 66, MiniMax-M2.7 61). The right pick depends on what you value most.
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Alibaba (Qwen)Qwen3.6 Plus
66/100- ECI147.6
- Price$0.50 / $3.00
- Context1M
MiniMaxMiniMax-M2.7
61/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-5.4 nano 68/100, Qwen3.6 Plus 66/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: Qwen3.6 Plus for raw capability and GPT-5.4 nano on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.6 PlusCapabilities Index (ECI): Qwen3.6 Plus 147.6 · MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · Qwen3.6 Plus $1.13 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 PlusQwen3.6 Plus 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsQwen3.6 PlusGPT-5.4 nano: Text, Images · Qwen3.6 Plus: Text, Images, Video · MiniMax-M2.7: Text
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GPT-5.4 nano | Qwen3.6 Plus | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 75 | 73 |
| Price | 25% | 66 | 47 | 63 |
| Inputs & features | 15% | 70 | 70 | 35 |
| Context window | 10% | 44 | 60 | 32 |
| Overall | 100% | 68/100 | 66/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | Qwen3.6 PlusAlibaba (Qwen) | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 147.6 (best) | 145.9 |
| ECI rank | #75 of 148 | #59 of 148 (best) | #73 of 148 |
| GPQA DiamondGraduate-level science questions | 78.5% | 88.4% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | 44.9% (best) | 38.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% | 93.3% (best) | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.9% | — |
| SimpleQA VerifiedShort factual questions | 11.7% | 44.1% (best) | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.50 | $0.30 |
| Output | $1.25 | $3.00 | $1.20 (best) |
| Cached input | $0.02 (best) | $0.05 | $0.06 |
| Blended (3:1) | $0.463 (best) | $1.13 | $0.525 |
| Long-context rate | Same rate | Over 256K: $2.00 / $6.00 | 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 | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.4-nano | qwen3.6-plus | MiniMax-M2.7 |
| API providers | 26 | 18 | 29 (best) |
| Released | Mar 17, 2026 | Apr 2, 2026 | Mar 18, 2026 |
| Knowledge cutoff | Aug 31, 2025 | Apr 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
- Qwen3.6 Plus$11.00
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-5.4 nano, Qwen3.6 Plus or MiniMax-M2.7?
It is close. Our weighted score puts them within 2 points (GPT-5.4 nano 68/100, Qwen3.6 Plus 66/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: Qwen3.6 Plus for raw capability and GPT-5.4 nano on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.4 nano, Qwen3.6 Plus 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); Qwen3.6 Plus costs $0.50 input / $3.00 output per million tokens (official Alibaba API price). 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 $1.13 for Qwen3.6 Plus (2.4× as much).
Which scores higher on benchmarks?
Qwen3.6 Plus scores higher on the Capabilities Index (ECI): Qwen3.6 Plus 147.6 (#59 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 (145.4–149.3 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 GPT-5.4 nano and MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, Qwen3.6 Plus 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?
Qwen3.6 Plus 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, Qwen3.6 Plus 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; Qwen3.6 Plus accepts text, images and video; MiniMax-M2.7 accepts text. Qwen3.6 Plus 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 Qwen3.6 Plus is proprietary.
Which is newer?
Qwen3.6 Plus is the newest, released Apr 2, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, Qwen3.6 Plus Apr 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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