GPT-5 Nano vs Qwen3.5 9B vs QwQ 32B
Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, QwQ 32B 53). 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 9B
72/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
- Alibaba (Qwen)
QwQ 32B
53/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
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, QwQ 32B 53/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 9BCapabilities Index (ECI): Qwen3.5 9B 139.5 · GPT-5 Nano 139.4 · QwQ 32B 137.6
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · QwQ 32B $0.745 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · QwQ 32B 131,072 tokens
- Widest inputsQwen3.5 9BGPT-5 Nano: Text, Images · Qwen3.5 9B: Text, Images, Video · QwQ 32B: Text
- Self-hostingQwen3.5 9B and QwQ 32BPublishes downloadable weights
| Measure | Weight | GPT-5 Nano | Qwen3.5 9B | QwQ 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 65 | 62 |
| Price | 25% | 91 | 95 | 56 |
| Inputs & features | 15% | 70 | 80 | 35 |
| Context window | 10% | 44 | 37 | 24 |
| Overall | 100% | 70/100 | 72/100 | 53/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5 NanoOpenAI | Qwen3.5 9BAlibaba (Qwen) | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | 139.5 (best) | 137.6 |
| ECI rank | #102 of 148 | #101 of 148 (best) | #109 of 148 |
| GPQA DiamondGraduate-level science questions | 69.4% | 79.0% (best) | 65.3% |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% (best) | 61.7% | 59.2% |
| SimpleQA VerifiedShort factual questions | 11.7% | — | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.10 | $0.66 |
| Output | $0.40 | $0.15 (best) | $1.00 |
| Cached input | $0.005 | — | — |
| Blended (3:1) | $0.138 | $0.113 (best) | $0.745 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 14 providers | Median of 1 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 131,072 tokens |
| Max output | 128,000 tokens (best) | 65,536 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yesminimal · low · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-5-nano | — | — |
| API providers | 21 (best) | 15 | 1 |
| Released | Aug 7, 2025 | Feb 23, 2026 | Mar 5, 2025 |
| Knowledge cutoff | May 30, 2024 | — | Apr 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 9B$1.30
- QwQ 32B$8.60
Which should you choose?
Which is better: GPT-5 Nano, Qwen3.5 9B or QwQ 32B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, QwQ 32B 53/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5 Nano, Qwen3.5 9B or QwQ 32B?
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); QwQ 32B costs $0.66 input / $1.00 output per million tokens (median across 1 API provider). 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 $0.745 for QwQ 32B (6.6× as much).
Which scores higher on benchmarks?
Qwen3.5 9B scores higher on the Capabilities Index (ECI): Qwen3.5 9B 139.5 (#101 of 148), GPT-5 Nano 139.4 (#102 of 148) and QwQ 32B 137.6 (#109 of 148). The confidence ranges of the top two overlap (136.5–141.3 vs 134.9–141.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, GPT-5 Nano 69.4%, QwQ 32B 65.3%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, Qwen3.5 9B 61.7%, QwQ 32B 59.2%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Nano, Qwen3.5 9B and QwQ 32B yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 9B 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 Nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3.5 9B and 131,072 for QwQ 32B. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3.5 9B up to 65,536, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Nano accepts text and images; Qwen3.5 9B accepts text, images and video; QwQ 32B accepts text. Qwen3.5 9B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 9B and QwQ 32B publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
Qwen3.5 9B is the newest, released Feb 23, 2026. GPT-5 Nano came out Aug 7, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: GPT-5 Nano May 30, 2024, QwQ 32B Apr 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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