GPT-5.4 nano vs GPT-5 Nano vs Qwen3.5 9B
Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, GPT-5.4 nano 68). The right pick depends on what you value most.
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
- OpenAI
GPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
Alibaba (Qwen)Qwen3.5 9B
72/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, GPT-5.4 nano 68/100), so choose by what matters most for your work: GPT-5.4 nano for raw capability and Qwen3.5 9B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.4 nanoCapabilities Index (ECI): GPT-5.4 nano 145.8 · Qwen3.5 9B 139.5 · GPT-5 Nano 139.4
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nano and GPT-5 NanoGPT-5.4 nano 400,000 · GPT-5 Nano 400,000 · Qwen3.5 9B 262,144 tokens
- Widest inputsQwen3.5 9BGPT-5.4 nano: Text, Images · GPT-5 Nano: Text, Images · Qwen3.5 9B: Text, Images, Video
- Self-hostingQwen3.5 9BPublishes downloadable weights
| Measure | Weight | GPT-5.4 nano | GPT-5 Nano | Qwen3.5 9B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 65 | 65 |
| Price | 25% | 66 | 91 | 95 |
| Inputs & features | 15% | 70 | 70 | 80 |
| Context window | 10% | 44 | 44 | 37 |
| Overall | 100% | 68/100 | 70/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5.4 nanoOpenAI | GPT-5 NanoOpenAI | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 (best) | 139.4 | 139.5 |
| ECI rank | #75 of 148 (best) | #102 of 148 | #101 of 148 |
| GPQA DiamondGraduate-level science questions | 78.5% | 69.4% | 79.0% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 44.9% (best) | 20.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 81.1% | 61.7% |
| SimpleQA VerifiedShort factual questions | 11.7% | 11.7% | — |
| Price per million tokens | |||
| Input | $0.20 | $0.05 (best) | $0.10 |
| Output | $1.25 | $0.40 | $0.15 (best) |
| Cached input | $0.02 | $0.005 (best) | — |
| Blended (3:1) | $0.463 | $0.138 | $0.113 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official OpenAI API | Median of 14 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 400,000 tokens (best) | 262,144 tokens |
| Max output | 128,000 tokens (best) | 128,000 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh | Yesminimal · low · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.4-nano | gpt-5-nano | — |
| API providers | 26 (best) | 21 | 15 |
| Released | Mar 17, 2026 | Aug 7, 2025 | Feb 23, 2026 |
| Knowledge cutoff | Aug 31, 2025 | May 30, 2024 | — |
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
- GPT-5 Nano$1.30
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: GPT-5.4 nano, GPT-5 Nano or Qwen3.5 9B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, GPT-5.4 nano 68/100), so choose by what matters most for your work: GPT-5.4 nano for raw capability and Qwen3.5 9B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5.4 nano, GPT-5 Nano or Qwen3.5 9B?
Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). GPT-5 Nano costs $0.05 input / $0.40 output per million tokens (official OpenAI API price); 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 $0.113 per million tokens for Qwen3.5 9B versus $0.138 for GPT-5 Nano (1.2× as much) and $0.463 for GPT-5.4 nano (4.1× as much).
Which scores higher on benchmarks?
GPT-5.4 nano scores higher on the Capabilities Index (ECI): GPT-5.4 nano 145.8 (#75 of 148), Qwen3.5 9B 139.5 (#101 of 148) and GPT-5 Nano 139.4 (#102 of 148). Their confidence ranges do not overlap (143.2–147.7 vs 136.5–141.3), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, GPT-5.4 nano 78.5%, GPT-5 Nano 69.4%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, GPT-5 Nano 81.1%, Qwen3.5 9B 61.7%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, GPT-5 Nano and Qwen3.5 9B yet, so there is no like-for-like coding score. On overall capability, GPT-5.4 nano 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?
GPT-5.4 nano and GPT-5 Nano have the largest context windows (400,000 and 400,000 tokens), against 262,144 for Qwen3.5 9B. Maximum output per response: GPT-5.4 nano up to 128,000, GPT-5 Nano up to 128,000, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; GPT-5 Nano accepts text and images; Qwen3.5 9B accepts text, images and video. Qwen3.5 9B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 9B publishes its weights and can be self-hosted; GPT-5.4 nano and GPT-5 Nano is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. Qwen3.5 9B came out Feb 23, 2026; GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, GPT-5 Nano May 30, 2024.
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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