GPT-5.4 nano vs Ling 3.1 Flash vs MiniMax-M3.1-Flash-Preview
MiniMax-M3.1-Flash-Preview comes out ahead, 66 to 60 and 36 on our weighted score.
OpenAIGPT-5.4 nano
60/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
inclusionAILing 3.1 Flash
36/100- ECI—
- Price$0.075 / $0.22
- Context262K
- Our pick
MiniMaxMiniMax-M3.1-Flash-Preview
66/100- ECI—
- Price—
- Context1M
MiniMax-M3.1-Flash-Preview is our pick
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against GPT-5.4 nano (60) and Ling 3.1 Flash (36). It leads on context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · MiniMax-M3.1-Flash-Preview unpriced
- Longest contextMiniMax-M3.1-Flash-PreviewMiniMax-M3.1-Flash-Preview 1,000,000 · GPT-5.4 nano 400,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsMiniMax-M3.1-Flash-PreviewGPT-5.4 nano: Text, Images · Ling 3.1 Flash: Text · MiniMax-M3.1-Flash-Preview: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-5.4 nano | Ling 3.1 Flash | MiniMax-M3.1-Flash-Preview |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 35 | 70 |
| Context window | 40% | 44 | 37 | 60 |
| Overall | 100% | 60/100 | 36/100 | 66/100 |
Left out because at least one model lacks the data: capability and price. 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-M3.1-Flash-PreviewMiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | — |
| ECI rank | #75 of 148 | — | — |
| 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) | — |
| Output | $1.25 | $0.22 (best) | — |
| Cached input | $0.02 | — | — |
| Blended (3:1) | $0.463 | $0.111 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official OpenAI API | Median of 1 providers | — |
| Limits | |||
| Context window | 400,000 tokens | 262,144 tokens | 1,000,000 tokens (best) |
| Max output | 128,000 tokens | 32,768 tokens | 512,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-5.4-nano | — | — |
| API providers | 26 (best) | 3 | — |
| Released | Mar 17, 2026 | Sep 29, 2026 | Sep 27, 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-M3.1-Flash-Preview—
Which should you choose?
Which is better: GPT-5.4 nano, Ling 3.1 Flash or MiniMax-M3.1-Flash-Preview?
MiniMax-M3.1-Flash-Preview is the better all-round choice, scoring 66/100 against GPT-5.4 nano (60) and Ling 3.1 Flash (36). It leads on context window. The score weighs inputs & features 60%, context window 40%. 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-M3.1-Flash-Preview?
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). 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). MiniMax-M3.1-Flash-Preview has no published per-token price.
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-M3.1-Flash-Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, Ling 3.1 Flash and MiniMax-M3.1-Flash-Preview 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?
MiniMax-M3.1-Flash-Preview has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.4 nano and 262,144 for Ling 3.1 Flash. Maximum output per response: GPT-5.4 nano up to 128,000, Ling 3.1 Flash up to 32,768, MiniMax-M3.1-Flash-Preview up to 512,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; Ling 3.1 Flash accepts text; MiniMax-M3.1-Flash-Preview accepts text, images and video. MiniMax-M3.1-Flash-Preview handles the widest range of inputs.
Are any of these open source?
No. GPT-5.4 nano, Ling 3.1 Flash and MiniMax-M3.1-Flash-Preview are proprietary and only available through APIs and apps.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 2026. MiniMax-M3.1-Flash-Preview came out Sep 27, 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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