GPT-5 Nano vs Qwen3.5 27B vs Qwen3.5 9B
Qwen3.5 9B comes out ahead, 79 to 75 and 61 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5 Nano
75/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
Alibaba (Qwen)Qwen3.5 27B
61/100- ECI—
- Price$0.30 / $2.40
- Context262K
- Our pickAlibaba (Qwen)
Qwen3.5 9B
79/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
Qwen3.5 9B is our pick
Qwen3.5 9B is the better all-round choice, scoring 79/100 against GPT-5 Nano (75) and Qwen3.5 27B (61). It leads on price. GPT-5 Nano wins on context window. Qwen3.5 27B wins on inputs & features. 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 priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · Qwen3.5 27B $0.825 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 27B 262,144 · Qwen3.5 9B 262,144 tokens
- Widest inputsQwen3.5 27BGPT-5 Nano: Text, Images · Qwen3.5 27B: Text, Images, Audio, Video · Qwen3.5 9B: Text, Images, Video
- Self-hostingQwen3.5 27B and Qwen3.5 9BPublishes downloadable weights
| Measure | Weight | GPT-5 Nano | Qwen3.5 27B | Qwen3.5 9B |
|---|---|---|---|---|
| Price | 50% | 91 | 54 | 95 |
| Inputs & features | 30% | 70 | 90 | 80 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 75/100 | 61/100 | 79/100 |
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 | GPT-5 NanoOpenAI | Qwen3.5 27BAlibaba (Qwen) | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | — | 139.5 (best) |
| ECI rank | #102 of 148 | — | #101 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 69.4% | — | 79.0% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% (best) | — | 61.7% |
| SimpleQA VerifiedShort factual questions | 11.7% | — | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.30 | $0.10 |
| Output | $0.40 | $2.40 | $0.15 (best) |
| Cached input | $0.005 | — | — |
| Blended (3:1) | $0.138 | $0.825 | $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 (best) | 262,144 tokens | 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 | Yes | 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 | Open | Open |
| API model ID | gpt-5-nano | qwen3.5-27b | — |
| API providers | 21 (best) | 16 | 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 27B$7.80
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: GPT-5 Nano, Qwen3.5 27B or Qwen3.5 9B?
Qwen3.5 9B is the better all-round choice, scoring 79/100 against GPT-5 Nano (75) and Qwen3.5 27B (61). It leads on price. GPT-5 Nano wins on context window. Qwen3.5 27B wins on inputs & features. 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, GPT-5 Nano, Qwen3.5 27B 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 27B costs $0.30 input / $2.40 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 $0.825 for Qwen3.5 27B (7.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5 Nano has an ECI of 139.4, Qwen3.5 27B has not been scored yet and Qwen3.5 9B has an ECI of 139.5.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Nano, Qwen3.5 27B and Qwen3.5 9B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 27B and 262,144 for Qwen3.5 9B. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3.5 27B 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 27B accepts text, images, audio and video; Qwen3.5 9B accepts text, images and video. Qwen3.5 27B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 27B and Qwen3.5 9B publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
Qwen3.5 27B 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.
