GPT-5 Nano vs Qwen3.5 27B vs Seed 2.0 Code
GPT-5 Nano comes out ahead, 75 to 61 and 57 on our weighted score, and it is the cheaper option too.
- Our pick
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
ByteDance SeedSeed 2.0 Code
57/100- ECI—
- Price$0.475 / $2.37
- Context262K
GPT-5 Nano is our pick
GPT-5 Nano is the better all-round choice, scoring 75/100 against Qwen3.5 27B (61) and Seed 2.0 Code (57). It leads on price and 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 priceGPT-5 NanoGPT-5 Nano $0.138 · Qwen3.5 27B $0.825 · Seed 2.0 Code $0.95 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 27B 262,144 · Seed 2.0 Code 262,144 tokens
- Widest inputsQwen3.5 27BGPT-5 Nano: Text, Images · Qwen3.5 27B: Text, Images, Audio, Video · Seed 2.0 Code: Text, Images, Video
- Self-hostingQwen3.5 27BPublishes downloadable weights
| Measure | Weight | GPT-5 Nano | Qwen3.5 27B | Seed 2.0 Code |
|---|---|---|---|---|
| Price | 50% | 91 | 54 | 51 |
| Inputs & features | 30% | 70 | 90 | 80 |
| Context window | 20% | 44 | 37 | 37 |
| Overall | 100% | 75/100 | 61/100 | 57/100 |
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) | Seed 2.0 CodeByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | — | — |
| ECI rank | #102 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 69.4% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% | — | — |
| SimpleQA VerifiedShort factual questions | 11.7% | — | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.30 | $0.475 |
| Output | $0.40 (best) | $2.40 | $2.37 |
| Cached input | $0.005 (best) | — | $0.095 |
| Blended (3:1) | $0.138 (best) | $0.825 | $0.95 |
| Long-context rate | Same rate | Same rate | Over 32K: $0.712 / $3.56 |
| Price source | Official OpenAI API | Official Alibaba API | Official Volcengine Ark API |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 65,536 tokens | 131,072 tokens (best) |
| 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 | Yesminimal · low · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-5-nano | qwen3.5-27b | doubao-seed-2-0-code-preview-260215 |
| API providers | 21 (best) | 16 | 10 |
| Released | Aug 7, 2025 | Feb 23, 2026 | Feb 14, 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
- Seed 2.0 Code$9.50
Which should you choose?
Which is better: GPT-5 Nano, Qwen3.5 27B or Seed 2.0 Code?
GPT-5 Nano is the better all-round choice, scoring 75/100 against Qwen3.5 27B (61) and Seed 2.0 Code (57). It leads on price and 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 Seed 2.0 Code?
GPT-5 Nano is cheaper at $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); Seed 2.0 Code costs $0.475 input / $2.37 output per million tokens (official Volcengine Ark API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $0.825 for Qwen3.5 27B (6× as much) and $0.95 for Seed 2.0 Code (6.9× 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 Seed 2.0 Code has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Nano, Qwen3.5 27B and Seed 2.0 Code 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 Seed 2.0 Code. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3.5 27B up to 65,536, Seed 2.0 Code up to 131,072 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; Seed 2.0 Code accepts text, images and video. Qwen3.5 27B handles the widest range of inputs.
Are any of these open source?
Qwen3.5 27B publishes its weights and can be self-hosted; GPT-5 Nano and Seed 2.0 Code is proprietary.
Which is newer?
Qwen3.5 27B is the newest, released Feb 23, 2026. Seed 2.0 Code came out Feb 14, 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.
