GPT-5 Nano vs Qwen3.5 Flash vs Qwen3.5 9B
Too close to call on our weighted score (Qwen3.5 Flash 75, Qwen3.5 9B 72, GPT-5 Nano 70). The right pick depends on what you value most.
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
Alibaba (Qwen)Qwen3.5 Flash
75/100- ECI144.0
- Price$0.10 / $0.40
- Context1M
- 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 3 points (Qwen3.5 Flash 75/100, Qwen3.5 9B 72/100, GPT-5 Nano 70/100), so choose by what matters most for your work: Qwen3.5 Flash for raw capability and Qwen3.5 9B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 FlashCapabilities Index (ECI): Qwen3.5 Flash 144.0 · Qwen3.5 9B 139.5 · GPT-5 Nano 139.4
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · Qwen3.5 Flash $0.175 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5 Nano 400,000 · Qwen3.5 9B 262,144 tokens
- Widest inputsQwen3.5 Flash and Qwen3.5 9BGPT-5 Nano: Text, Images · Qwen3.5 Flash: Text, Images, Video · Qwen3.5 9B: Text, Images, Video
- Self-hostingQwen3.5 9BPublishes downloadable weights
| Measure | Weight | GPT-5 Nano | Qwen3.5 Flash | Qwen3.5 9B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 71 | 65 |
| Price | 25% | 91 | 86 | 95 |
| Inputs & features | 15% | 70 | 80 | 80 |
| Context window | 10% | 44 | 60 | 37 |
| Overall | 100% | 70/100 | 75/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5 NanoOpenAI | Qwen3.5 FlashAlibaba (Qwen) | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | 144.0 (best) | 139.5 |
| ECI rank | #102 of 148 | #82 of 148 (best) | #101 of 148 |
| GPQA DiamondGraduate-level science questions | 69.4% | 82.3% (best) | 79.0% |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% (best) | 18.3% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% | 84.4% (best) | 61.7% |
| SimpleQA VerifiedShort factual questions | 11.7% | 20.3% (best) | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.10 | $0.10 |
| Output | $0.40 | $0.40 | $0.15 (best) |
| Cached input | $0.005 (best) | $0.01 | — |
| Blended (3:1) | $0.138 | $0.175 | $0.113 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Alibaba API | Median of 14 providers |
| Limits | |||
| Context window | 400,000 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 128,000 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yesminimal · low · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5-nano | qwen3.5-flash | — |
| API providers | 21 (best) | 8 | 15 |
| Released | Aug 7, 2025 | Feb 23, 2026 | Feb 23, 2026 |
| Knowledge cutoff | 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 Nano$1.30
- Qwen3.5 Flash$1.80
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: GPT-5 Nano, Qwen3.5 Flash or Qwen3.5 9B?
It is close. Our weighted score puts them within 3 points (Qwen3.5 Flash 75/100, Qwen3.5 9B 72/100, GPT-5 Nano 70/100), so choose by what matters most for your work: Qwen3.5 Flash 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 Nano, Qwen3.5 Flash 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); Qwen3.5 Flash costs $0.10 input / $0.40 output per million tokens (official Alibaba 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 $0.175 for Qwen3.5 Flash (1.6× as much).
Which scores higher on benchmarks?
Qwen3.5 Flash scores higher on the Capabilities Index (ECI): Qwen3.5 Flash 144.0 (#82 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 (141.6–145.5 vs 136.5–141.3), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.5 Flash 82.3%, Qwen3.5 9B 79.0%, GPT-5 Nano 69.4%; OTIS Mock AIME 2024–2025 — Qwen3.5 Flash 84.4%, 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 Nano, Qwen3.5 Flash and Qwen3.5 9B yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 Flash 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?
Qwen3.5 Flash has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5 Nano and 262,144 for Qwen3.5 9B. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3.5 Flash up to 65,536, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Nano accepts text and images; Qwen3.5 Flash accepts text, images and video; Qwen3.5 9B accepts text, images and video. Qwen3.5 Flash 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 Nano and Qwen3.5 Flash is proprietary.
Which is newer?
Qwen3.5 Flash is the newest, released Feb 23, 2026. Qwen3.5 9B came out Feb 23, 2026; GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: 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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