Qwen3.5 9B vs Qwen3 14B vs GPT-5 Nano
Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, Qwen3 14B 54). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 9B
72/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
- Alibaba (Qwen)
Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
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, Qwen3 14B 54/100), 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 · Qwen3 14B 138.2
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · Qwen3 14B 131,072 tokens
- Widest inputsQwen3.5 9BQwen3.5 9B: Text, Images, Video · Qwen3 14B: Text · GPT-5 Nano: Text, Images
- Self-hostingQwen3.5 9B and Qwen3 14BPublishes downloadable weights
| Measure | Weight | Qwen3.5 9B | Qwen3 14B | GPT-5 Nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 63 | 65 |
| Price | 25% | 95 | 60 | 91 |
| Inputs & features | 15% | 80 | 35 | 70 |
| Context window | 10% | 37 | 24 | 44 |
| Overall | 100% | 72/100 | 54/100 | 70/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.5 9BAlibaba (Qwen) | Qwen3 14BAlibaba (Qwen) | GPT-5 NanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.5 (best) | 138.2 | 139.4 |
| ECI rank | #101 of 148 (best) | #107 of 148 | #102 of 148 |
| GPQA DiamondGraduate-level science questions | 79.0% (best) | 63.8% | 69.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 20.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 61.7% | 66.4% | 81.1% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.10 | $0.35 | $0.05 (best) |
| Output | $0.15 (best) | $1.40 | $0.40 |
| Cached input | — | — | $0.005 |
| Blended (3:1) | $0.113 (best) | $0.613 | $0.138 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 14 providers | Official Alibaba API | Official OpenAI API |
| Limits | |||
| Context window | 262,144 tokens | 131,072 tokens | 400,000 tokens (best) |
| Max output | 65,536 tokens | 8,192 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yesminimal · low · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | qwen3-14b | gpt-5-nano |
| API providers | 15 | 1 | 21 (best) |
| Released | Feb 23, 2026 | Apr 29, 2025 | Aug 7, 2025 |
| Knowledge cutoff | — | Apr 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.
- Qwen3.5 9B$1.30
- Qwen3 14B$6.30
- GPT-5 Nano$1.30
Which should you choose?
Which is better: Qwen3.5 9B, Qwen3 14B or GPT-5 Nano?
It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, Qwen3 14B 54/100), 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, Qwen3.5 9B, Qwen3 14B or GPT-5 Nano?
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 14B costs $0.35 input / $1.40 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 $0.613 for Qwen3 14B (5.4× 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 Qwen3 14B 138.2 (#107 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%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, Qwen3 14B 66.4%, Qwen3.5 9B 61.7%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 9B, Qwen3 14B and GPT-5 Nano 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 Qwen3 14B. Maximum output per response: Qwen3.5 9B up to 65,536, Qwen3 14B up to 8,192, GPT-5 Nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 9B accepts text, images and video; Qwen3 14B accepts text; GPT-5 Nano accepts text and images. Qwen3.5 9B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 9B and Qwen3 14B 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; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Qwen3 14B Apr 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.
