GPT-5.4 nano vs Ling 3.1 Flash vs MiniMax-M2.7
Too close to call on our weighted score (Ling 3.1 Flash 65, GPT-5.4 nano 63, MiniMax-M2.7 48). The right pick depends on what you value most.
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
inclusionAILing 3.1 Flash
65/100- ECI—
- Price$0.075 / $0.22
- Context262K
MiniMaxMiniMax-M2.7
48/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
Too close to call
It is close. Our weighted score puts them within 3 points (Ling 3.1 Flash 65/100, GPT-5.4 nano 63/100, MiniMax-M2.7 48/100), so choose by what matters most for your work: Ling 3.1 Flash on price and GPT-5.4 nano for long inputs. 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 priceLing 3.1 FlashLing 3.1 Flash $0.111 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Ling 3.1 Flash 262,144 · MiniMax-M2.7 204,800 tokens
- Widest inputsGPT-5.4 nanoGPT-5.4 nano: Text, Images · Ling 3.1 Flash: Text · MiniMax-M2.7: Text
- Self-hostingMiniMax-M2.7Publishes downloadable weights
| Measure | Weight | GPT-5.4 nano | Ling 3.1 Flash | MiniMax-M2.7 |
|---|---|---|---|---|
| Price | 50% | 66 | 95 | 63 |
| Inputs & features | 30% | 70 | 35 | 35 |
| Context window | 20% | 44 | 37 | 32 |
| Overall | 100% | 63/100 | 65/100 | 48/100 |
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 | Ling 3.1 FlashinclusionAI | MiniMax-M2.7MiniMax |
|---|---|---|---|
| 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 | $0.075 (best) | $0.30 |
| Output | $1.25 | $0.22 (best) | $1.20 |
| Cached input | $0.02 (best) | — | $0.06 |
| Blended (3:1) | $0.463 | $0.111 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 1 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 204,800 tokens |
| Max output | 128,000 tokens | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | 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 | — | MiniMax-M2.7 |
| API providers | 26 | 3 | 29 (best) |
| Released | Mar 17, 2026 | Sep 29, 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
- Ling 3.1 Flash$1.19
- MiniMax-M2.7$5.40
Which should you choose?
Which is better: GPT-5.4 nano, Ling 3.1 Flash or MiniMax-M2.7?
It is close. Our weighted score puts them within 3 points (Ling 3.1 Flash 65/100, GPT-5.4 nano 63/100, MiniMax-M2.7 48/100), so choose by what matters most for your work: Ling 3.1 Flash on price and GPT-5.4 nano for long inputs. 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, Ling 3.1 Flash or MiniMax-M2.7?
Ling 3.1 Flash is cheaper at $0.075 input / $0.22 output per million tokens (median across 1 API provider). 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 $0.111 per million tokens for Ling 3.1 Flash versus $0.463 for GPT-5.4 nano (4.2× as much) and $0.525 for MiniMax-M2.7 (4.7× 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, Ling 3.1 Flash has not been scored yet and MiniMax-M2.7 has an ECI of 145.9.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, Ling 3.1 Flash and MiniMax-M2.7 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 Ling 3.1 Flash and 204,800 for MiniMax-M2.7. Maximum output per response: GPT-5.4 nano up to 128,000, Ling 3.1 Flash up to 32,768, MiniMax-M2.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; Ling 3.1 Flash accepts text; MiniMax-M2.7 accepts text. GPT-5.4 nano 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 Ling 3.1 Flash is proprietary.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 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.
