Ling 3.1 Flash vs GPT-5.4 nano
Too close to call on our weighted score (Ling 3.1 Flash 65, GPT-5.4 nano 63). The right pick depends on what you value most.
inclusionAILing 3.1 Flash
65/100- ECI—
- Price$0.075 / $0.22
- Context262K
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Add a model
Make it a three-way comparison.
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), 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 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Ling 3.1 Flash 262,144 tokens
- Widest inputsGPT-5.4 nanoLing 3.1 Flash: Text · GPT-5.4 nano: Text, Images
- Self-hostingNo open weightsBoth are available only through APIs
| Measure | Weight | Ling 3.1 Flash | GPT-5.4 nano |
|---|---|---|---|
| Price | 50% | 95 | 66 |
| Inputs & features | 30% | 35 | 70 |
| Context window | 20% | 37 | 44 |
| Overall | 100% | 65/100 | 63/100 |
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 | Ling 3.1 FlashinclusionAI | GPT-5.4 nanoOpenAI |
|---|---|---|
| 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 | $0.075 (best) | $0.20 |
| Output | $0.22 (best) | $1.25 |
| Cached input | — | $0.02 |
| Blended (3:1) | $0.111 (best) | $0.463 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official OpenAI API |
| Limits | ||
| Context window | 262,144 tokens | 400,000 tokens (best) |
| Max output | 32,768 tokens | 128,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Proprietary | Proprietary |
| API model ID | — | gpt-5.4-nano |
| API providers | 3 | 26 (best) |
| Released | Sep 29, 2026 | Mar 17, 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.
- Ling 3.1 Flash$1.19
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: Ling 3.1 Flash or GPT-5.4 nano?
It is close. Our weighted score puts them within 3 points (Ling 3.1 Flash 65/100, GPT-5.4 nano 63/100), 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, Ling 3.1 Flash or GPT-5.4 nano?
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).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Ling 3.1 Flash has not been scored yet and GPT-5.4 nano has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for Ling 3.1 Flash and GPT-5.4 nano yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both 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. Maximum output per response: Ling 3.1 Flash up to 32,768, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Ling 3.1 Flash accepts text; GPT-5.4 nano accepts text and images. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
No. Ling 3.1 Flash and GPT-5.4 nano are proprietary and only available through APIs and apps.
Which is newer?
Ling 3.1 Flash is the newest, released Sep 29, 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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