Qwen3.5 9B vs DeepSeek V4 Flash vs GPT-5 Nano
Too close to call on our weighted score (Qwen3.5 9B 72, DeepSeek V4 Flash 71, GPT-5 Nano 70). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 9B
72/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
DeepSeekDeepSeek V4 Flash
71/100- ECI146.1
- Price$0.14 / $0.28
- Context1M
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 1 points (Qwen3.5 9B 72/100, DeepSeek V4 Flash 71/100, GPT-5 Nano 70/100), so choose by what matters most for your work: DeepSeek V4 Flash for raw capability and Qwen3.5 9B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V4 FlashCapabilities Index (ECI): DeepSeek V4 Flash 146.1 · Qwen3.5 9B 139.5 · GPT-5 Nano 139.4
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · DeepSeek V4 Flash $0.175 per 1M tokens (3:1 blend)
- Longest contextDeepSeek V4 FlashDeepSeek V4 Flash 1,000,000 · GPT-5 Nano 400,000 · Qwen3.5 9B 262,144 tokens
- Widest inputsQwen3.5 9BQwen3.5 9B: Text, Images, Video · DeepSeek V4 Flash: Text · GPT-5 Nano: Text, Images
- Self-hostingQwen3.5 9B and DeepSeek V4 FlashPublishes downloadable weights
| Measure | Weight | Qwen3.5 9B | DeepSeek V4 Flash | GPT-5 Nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 73 | 65 |
| Price | 25% | 95 | 86 | 91 |
| Inputs & features | 15% | 80 | 45 | 70 |
| Context window | 10% | 37 | 60 | 44 |
| Overall | 100% | 72/100 | 71/100 | 70/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.5 9BAlibaba (Qwen) | DeepSeek V4 FlashDeepSeek | GPT-5 NanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.5 | 146.1 (best) | 139.4 |
| ECI rank | #101 of 148 | #71 of 148 (best) | #102 of 148 |
| GPQA DiamondGraduate-level science questions | 79.0% (best) | — | 69.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 20.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 61.7% | — | 81.1% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.10 | $0.14 | $0.05 (best) |
| Output | $0.15 (best) | $0.28 | $0.40 |
| Cached input | — | — | $0.005 |
| Blended (3:1) | $0.113 (best) | $0.175 | $0.138 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 14 providers | Median of 42 providers | Official OpenAI API |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens (best) | 400,000 tokens |
| Max output | 65,536 tokens | 384,000 tokens (best) | 128,000 tokens |
| 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 | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | — | gpt-5-nano |
| API providers | 15 | 48 (best) | 21 |
| Released | Feb 23, 2026 | Apr 24, 2026 | Aug 7, 2025 |
| Knowledge cutoff | — | May 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
- DeepSeek V4 Flash$1.96
- GPT-5 Nano$1.30
Which should you choose?
Which is better: Qwen3.5 9B, DeepSeek V4 Flash or GPT-5 Nano?
It is close. Our weighted score puts them within 1 points (Qwen3.5 9B 72/100, DeepSeek V4 Flash 71/100, GPT-5 Nano 70/100), so choose by what matters most for your work: DeepSeek V4 Flash for raw capability and Qwen3.5 9B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.5 9B, DeepSeek V4 Flash 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); DeepSeek V4 Flash costs $0.14 input / $0.28 output per million tokens (median across 42 API providers). 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 $0.175 for DeepSeek V4 Flash (1.6× as much).
Which scores higher on benchmarks?
DeepSeek V4 Flash scores higher on the Capabilities Index (ECI): DeepSeek V4 Flash 146.1 (#71 of 148), Qwen3.5 9B 139.5 (#101 of 148) and GPT-5 Nano 139.4 (#102 of 148). Their confidence ranges do not overlap (143.6–147.9 vs 136.5–141.3), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 9B, DeepSeek V4 Flash and GPT-5 Nano yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4 Flash 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?
DeepSeek V4 Flash has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5 Nano and 262,144 for Qwen3.5 9B. Maximum output per response: Qwen3.5 9B up to 65,536, DeepSeek V4 Flash up to 384,000, GPT-5 Nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 9B accepts text, images and video; DeepSeek V4 Flash 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 DeepSeek V4 Flash publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
DeepSeek V4 Flash is the newest, released Apr 24, 2026. Qwen3.5 9B came out Feb 23, 2026; GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: DeepSeek V4 Flash May 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.
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