GPT-5 Nano vs Gemma 3 27B IT vs Qwen3.5 9B
Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, Gemma 3 27B IT 60). The right pick depends on what you value most.
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
GoogleGemma 3 27B IT
60/100- ECI130.0
- Price$0.08 / $0.20
- Context131K
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 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, Gemma 3 27B IT 60/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability, Gemma 3 27B IT on price 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 · Gemma 3 27B IT 130.0
- Lowest priceGemma 3 27B ITGemma 3 27B IT $0.11 · Qwen3.5 9B $0.113 · GPT-5 Nano $0.138 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · Gemma 3 27B IT 131,072 tokens
- Widest inputsQwen3.5 9BGPT-5 Nano: Text, Images · Gemma 3 27B IT: Text, Images · Qwen3.5 9B: Text, Images, Video
- Self-hostingGemma 3 27B IT and Qwen3.5 9BPublishes downloadable weights
| Measure | Weight | GPT-5 Nano | Gemma 3 27B IT | Qwen3.5 9B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 53 | 65 |
| Price | 25% | 91 | 95 | 95 |
| Inputs & features | 15% | 70 | 50 | 80 |
| Context window | 10% | 44 | 24 | 37 |
| Overall | 100% | 70/100 | 60/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5 NanoOpenAI | Gemma 3 27B ITGoogle | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 | 130.0 | 139.5 (best) |
| ECI rank | #102 of 148 | #125 of 148 | #101 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 69.4% | 47.7% | 79.0% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% (best) | 22.5% | 61.7% |
| SimpleQA VerifiedShort factual questions | 11.7% | — | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.08 | $0.10 |
| Output | $0.40 | $0.20 | $0.15 (best) |
| Cached input | $0.005 | — | — |
| Blended (3:1) | $0.138 | $0.11 (best) | $0.113 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 9 providers | Median of 14 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 131,072 tokens | 262,144 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yesminimal · low · medium · high | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-5-nano | — | — |
| API providers | 21 (best) | 10 | 15 |
| Released | Aug 7, 2025 | Mar 12, 2025 | Feb 23, 2026 |
| Knowledge cutoff | May 30, 2024 | Aug 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
- Gemma 3 27B IT$1.20
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: GPT-5 Nano, Gemma 3 27B IT or Qwen3.5 9B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, Gemma 3 27B IT 60/100), so choose by what matters most for your work: Qwen3.5 9B for raw capability, Gemma 3 27B IT on price 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, Gemma 3 27B IT or Qwen3.5 9B?
Gemma 3 27B IT is cheaper at $0.08 input / $0.20 output per million tokens (median across 9 API providers). Qwen3.5 9B costs $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). At a typical mix of three input tokens to one output token, that is $0.11 per million tokens for Gemma 3 27B IT versus $0.113 for Qwen3.5 9B (1× as much) and $0.138 for GPT-5 Nano (1.3× 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 Gemma 3 27B IT 130.0 (#125 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%, Gemma 3 27B IT 47.7%; OTIS Mock AIME 2024–2025 — GPT-5 Nano 81.1%, Qwen3.5 9B 61.7%, Gemma 3 27B IT 22.5%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Nano, Gemma 3 27B IT and Qwen3.5 9B 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 Gemma 3 27B IT. Maximum output per response: GPT-5 Nano up to 128,000, Gemma 3 27B IT up to 131,072, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Nano accepts text and images; Gemma 3 27B IT accepts text and images; Qwen3.5 9B accepts text, images and video. Qwen3.5 9B handles the widest range of inputs.
Are any of these open source?
Gemma 3 27B IT and Qwen3.5 9B 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; Gemma 3 27B IT came out Mar 12, 2025. Knowledge cutoff: GPT-5 Nano May 30, 2024, Gemma 3 27B IT Aug 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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