GPT-5 Nano vs Qwen3 235B-A22B Thinking 2507 vs Qwen3 30B-A3B Instruct 2507
GPT-5 Nano comes out ahead, 68 to 59 and 55 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT-5 Nano
68/100- ECI139.3
- Price$0.05 / $0.40
- Context400K
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
55/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
- Alibaba (Qwen)
Qwen3 30B-A3B Instruct 2507
59/100- ECI137.3
- Price$0.10 / $0.30
- Context262K
GPT-5 Nano is our pick
GPT-5 Nano is the better all-round choice, scoring 68/100 against Qwen3 30B-A3B Instruct 2507 (59) and Qwen3 235B-A22B Thinking 2507 (55). It leads on price, inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22B Thinking 2507Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 · GPT-5 Nano 139.3 · Qwen3 30B-A3B Instruct 2507 137.3
- Lowest priceGPT-5 NanoGPT-5 Nano $0.138 · Qwen3 30B-A3B Instruct 2507 $0.15 · Qwen3 235B-A22B Thinking 2507 $0.95 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3 235B-A22B Thinking 2507 262,144 · Qwen3 30B-A3B Instruct 2507 262,144 tokens
- Widest inputsGPT-5 NanoGPT-5 Nano: Text, Images · Qwen3 235B-A22B Thinking 2507: Text · Qwen3 30B-A3B Instruct 2507: Text
- Self-hostingQwen3 235B-A22B Thinking 2507 and Qwen3 30B-A3B Instruct 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GPT-5 Nano | Qwen3 235B-A22B Thinking 2507 | Qwen3 30B-A3B Instruct 2507 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 63 | 69 | 61 |
| Price | 25% | 87 | 49 | 85 |
| Inputs & features | 15% | 70 | 35 | 25 |
| Context window | 10% | 44 | 37 | 37 |
| Overall | 100% | 68/100 | 55/100 | 59/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-5 NanoOpenAI | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) | Qwen3 30B-A3B Instruct 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.3 | 143.8 (best) | 137.3 |
| ECI rank | #116 of 165 | #93 of 165 (best) | #124 of 165 |
| GPQA DiamondGraduate-level science questions | 69.4% | 80.1% (best) | 55.6% |
| FrontierMath Tiers 1–3Research-level mathematics | 20.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.1% | 86.7% (best) | 62.2% |
| SimpleQA VerifiedShort factual questions | 11.7% | 40.4% (best) | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.30 | $0.10 |
| Output | $0.40 | $2.90 | $0.30 (best) |
| Cached input | $0.005 | — | — |
| Blended (3:1) | $0.138 (best) | $0.95 | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 11 providers | Median of 12 providers |
| Limits | |||
| Context window | 400,000 tokens (best) | 262,144 tokens | 262,144 tokens |
| Max output | 128,000 tokens (best) | 81,920 tokens | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yesminimal · low · medium · high | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | gpt-5-nano | — | — |
| API providers | 30 (best) | 12 | 14 |
| Released | Aug 7, 2025 | Jul 25, 2025 | Jul 28, 2025 |
| 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 235B-A22B Thinking 2507$8.80
- Qwen3 30B-A3B Instruct 2507$1.60
Which should you choose?
Which is better: GPT-5 Nano, Qwen3 235B-A22B Thinking 2507 or Qwen3 30B-A3B Instruct 2507?
GPT-5 Nano is the better all-round choice, scoring 68/100 against Qwen3 30B-A3B Instruct 2507 (59) and Qwen3 235B-A22B Thinking 2507 (55). It leads on price, inputs & features and context window. Qwen3 235B-A22B Thinking 2507 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-5 Nano, Qwen3 235B-A22B Thinking 2507 or Qwen3 30B-A3B Instruct 2507?
GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). Qwen3 30B-A3B Instruct 2507 costs $0.10 input / $0.30 output per million tokens (median across 12 API providers); Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $0.15 for Qwen3 30B-A3B Instruct 2507 (1.1× as much) and $0.95 for Qwen3 235B-A22B Thinking 2507 (6.9× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B Thinking 2507 scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B Thinking 2507 143.8 (#93 of 165), GPT-5 Nano 139.3 (#116 of 165) and Qwen3 30B-A3B Instruct 2507 137.3 (#124 of 165). Their confidence ranges do not overlap (141.9–145.2 vs 134.3–141.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3 235B-A22B Thinking 2507 80.1%, GPT-5 Nano 69.4%, Qwen3 30B-A3B Instruct 2507 55.6%; OTIS Mock AIME 2024–2025 — Qwen3 235B-A22B Thinking 2507 86.7%, GPT-5 Nano 81.1%, Qwen3 30B-A3B Instruct 2507 62.2%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Nano, Qwen3 235B-A22B Thinking 2507 and Qwen3 30B-A3B Instruct 2507 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B Thinking 2507 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 235B-A22B Thinking 2507 and 262,144 for Qwen3 30B-A3B Instruct 2507. Maximum output per response: GPT-5 Nano up to 128,000, Qwen3 235B-A22B Thinking 2507 up to 81,920, Qwen3 30B-A3B Instruct 2507 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Nano accepts text and images; Qwen3 235B-A22B Thinking 2507 accepts text; Qwen3 30B-A3B Instruct 2507 accepts text. GPT-5 Nano handles the widest range of inputs.
Are any of these open source?
Qwen3 235B-A22B Thinking 2507 and Qwen3 30B-A3B Instruct 2507 publishes its weights (Apache 2.0) and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
GPT-5 Nano is the newest, released Aug 7, 2025. Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025; Qwen3 235B-A22B Thinking 2507 came out Jul 25, 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 Nov 2, 2026.
Related comparisons
- GPT-5 Nano vs DeepSeek-V3.1DeepSeek · ECI 139.8
- GPT-5 Nano vs Gemma 4 26B A4B ITGoogle · ECI 142.0
- GPT-5 Nano vs Seed-OSS-36B-InstructByteDance Seed
- Qwen3 235B-A22B Thinking 2507 vs GLM-4.7Z.ai (Zhipu) · ECI 143.7
- Qwen3 235B-A22B Thinking 2507 vs GPT-5 MiniOpenAI · ECI 145.4
- Qwen3 235B-A22B Thinking 2507 vs DeepSeek R1 0528DeepSeek · ECI 141.2
