Qwen3.6 Flash vs GPT-5.4 nano vs MiniMax-M2.7
Too close to call on our weighted score (Qwen3.6 Flash 70, GPT-5.4 nano 68, MiniMax-M2.7 61). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.6 Flash
70/100- ECI143.3
- Price$0.188 / $1.13
- Context1M
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
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.6 Flash 70/100, GPT-5.4 nano 68/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: MiniMax-M2.7 for raw capability, Qwen3.6 Flash on price and Qwen3.6 Flash for long inputs. 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 · Qwen3.6 Flash 143.3
- Lowest priceQwen3.6 FlashQwen3.6 Flash $0.422 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 FlashQwen3.6 Flash 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsQwen3.6 FlashQwen3.6 Flash: Text, Images, Video · GPT-5.4 nano: Text, Images · MiniMax-M2.7: Text
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | Qwen3.6 Flash | GPT-5.4 nano | MiniMax-M2.7 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 70 | 73 | 73 |
| Price | 25% | 68 | 66 | 63 |
| Inputs & features | 15% | 80 | 70 | 35 |
| Context window | 10% | 60 | 44 | 32 |
| Overall | 100% | 70/100 | 68/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.6 FlashAlibaba (Qwen) | GPT-5.4 nanoOpenAI | MiniMax-M2.7MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.3 | 145.8 | 145.9 (best) |
| ECI rank | #85 of 148 | #75 of 148 | #73 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% (best) | 78.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 22.5% | 44.9% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.4% | 87.8% (best) | — |
| SimpleQA VerifiedShort factual questions | 15.9% (best) | 11.7% | — |
| Price per million tokens | |||
| Input | $0.188 (best) | $0.20 | $0.30 |
| Output | $1.13 (best) | $1.25 | $1.20 |
| Cached input | — | $0.02 (best) | $0.06 |
| Blended (3:1) | $0.422 (best) | $0.463 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official OpenAI API | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 400,000 tokens | 204,800 tokens |
| Max output | 65,536 tokens | 128,000 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 | Yes | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | qwen3.6-flash | gpt-5.4-nano | MiniMax-M2.7 |
| API providers | 16 | 26 | 29 (best) |
| Released | Apr 27, 2026 | Mar 17, 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.
- Qwen3.6 Flash$4.13
- GPT-5.4 nano$4.50
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: Qwen3.6 Flash, GPT-5.4 nano or MiniMax-M2.7?
It is close. Our weighted score puts them within 2 points (Qwen3.6 Flash 70/100, GPT-5.4 nano 68/100, MiniMax-M2.7 61/100), so choose by what matters most for your work: MiniMax-M2.7 for raw capability, Qwen3.6 Flash on price and Qwen3.6 Flash for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.6 Flash, GPT-5.4 nano or MiniMax-M2.7?
Qwen3.6 Flash is cheaper at $0.188 input / $1.13 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 $0.422 per million tokens for Qwen3.6 Flash versus $0.463 for GPT-5.4 nano (1.1× as much) and $0.525 for MiniMax-M2.7 (1.2× 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 Qwen3.6 Flash 143.3 (#85 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 Qwen3.6 Flash, GPT-5.4 nano 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?
Qwen3.6 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: Qwen3.6 Flash up to 65,536, GPT-5.4 nano up to 128,000, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.6 Flash accepts text, images and video; GPT-5.4 nano accepts text and images; MiniMax-M2.7 accepts text. Qwen3.6 Flash handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 publishes its weights and can be self-hosted; Qwen3.6 Flash and GPT-5.4 nano is proprietary.
Which is newer?
Qwen3.6 Flash is the newest, released Apr 27, 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.
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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