GPT-5.4 nano vs MiniMax-M2.7 vs Qwen3.5 122B-A10B
GPT-5.4 nano comes out ahead, 63 to 58 and 48 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
MiniMaxMiniMax-M2.7
48/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Alibaba (Qwen)Qwen3.5 122B-A10B
58/100- ECI—
- Price$0.40 / $3.20
- Context262K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 63/100 against Qwen3.5 122B-A10B (58) and MiniMax-M2.7 (48). It leads on price and context window. Qwen3.5 122B-A10B wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · Qwen3.5 122B-A10B $1.10 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Qwen3.5 122B-A10B 262,144 · MiniMax-M2.7 204,800 tokens
- Widest inputsQwen3.5 122B-A10BGPT-5.4 nano: Text, Images · MiniMax-M2.7: Text · Qwen3.5 122B-A10B: Text, Images, Audio, Video
- Self-hostingMiniMax-M2.7 and Qwen3.5 122B-A10BPublishes downloadable weights
| Measure | Weight | GPT-5.4 nano | MiniMax-M2.7 | Qwen3.5 122B-A10B |
|---|---|---|---|---|
| Price | 50% | 66 | 63 | 48 |
| Inputs & features | 30% | 70 | 35 | 90 |
| Context window | 20% | 44 | 32 | 37 |
| Overall | 100% | 63/100 | 48/100 | 58/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | MiniMax-M2.7MiniMax | Qwen3.5 122B-A10BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 145.9 (best) | — |
| ECI rank | #75 of 148 | #73 of 148 (best) | — |
| 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 | $0.20 (best) | $0.30 | $0.40 |
| Output | $1.25 | $1.20 (best) | $3.20 |
| Cached input | $0.02 (best) | $0.06 | — |
| Blended (3:1) | $0.463 (best) | $0.525 | $1.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official MiniMax (minimax.io) API | Official Alibaba API |
| Limits | |||
| Context window | 400,000 tokens (best) | 204,800 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-5.4-nano | MiniMax-M2.7 | qwen3.5-122b-a10b |
| API providers | 26 | 29 (best) | 19 |
| Released | Mar 17, 2026 | Mar 18, 2026 | Feb 23, 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
- MiniMax-M2.7$5.40
- Qwen3.5 122B-A10B$10.40
Which should you choose?
Which is better: GPT-5.4 nano, MiniMax-M2.7 or Qwen3.5 122B-A10B?
GPT-5.4 nano is the better all-round choice, scoring 63/100 against Qwen3.5 122B-A10B (58) and MiniMax-M2.7 (48). It leads on price and context window. Qwen3.5 122B-A10B wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-5.4 nano, MiniMax-M2.7 or Qwen3.5 122B-A10B?
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); Qwen3.5 122B-A10B costs $0.40 input / $3.20 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 $1.10 for Qwen3.5 122B-A10B (2.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.4 nano has an ECI of 145.8, MiniMax-M2.7 has an ECI of 145.9 and Qwen3.5 122B-A10B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, MiniMax-M2.7 and Qwen3.5 122B-A10B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 262,144 for Qwen3.5 122B-A10B and 204,800 for MiniMax-M2.7. Maximum output per response: GPT-5.4 nano up to 128,000, MiniMax-M2.7 up to 131,072, Qwen3.5 122B-A10B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; MiniMax-M2.7 accepts text; Qwen3.5 122B-A10B accepts text, images, audio and video. Qwen3.5 122B-A10B handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and Qwen3.5 122B-A10B 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; Qwen3.5 122B-A10B 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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