DeepSeek-V3.1 vs GPT-5 Nano vs Qwen3.5 9B
Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, DeepSeek-V3.1 55). The right pick depends on what you value most.
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
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, DeepSeek-V3.1 55/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Qwen3.5 9B on price and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · Qwen3.5 9B 139.5 · GPT-5 Nano 139.4
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · DeepSeek-V3.1 $0.601 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · DeepSeek-V3.1 131,072 tokens
- Widest inputsQwen3.5 9BDeepSeek-V3.1: Text · GPT-5 Nano: Text, Images · Qwen3.5 9B: Text, Images, Video
- Self-hostingDeepSeek-V3.1 and Qwen3.5 9BPublishes downloadable weights (MIT License)
| Measure | Weight | DeepSeek-V3.1 | GPT-5 Nano | Qwen3.5 9B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 65 | 65 |
| Price | 25% | 60 | 91 | 95 |
| Inputs & features | 15% | 35 | 70 | 80 |
| Context window | 10% | 24 | 44 | 37 |
| Overall | 100% | 55/100 | 70/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-V3.1DeepSeek | GPT-5 NanoOpenAI | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 (best) | 139.4 | 139.5 |
| ECI rank | #100 of 148 (best) | #102 of 148 | #101 of 148 |
| 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 | $0.385 | $0.05 (best) | $0.10 |
| Output | $1.25 | $0.40 | $0.15 (best) |
| Cached input | — | $0.005 | — |
| Blended (3:1) | $0.601 | $0.138 | $0.113 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 8 providers | Official OpenAI API | Median of 14 providers |
| Limits | |||
| Context window | 131,072 tokens | 400,000 tokens (best) | 262,144 tokens |
| Max output | 8,192 tokens | 128,000 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yesminimal · low · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenMIT License | Proprietary | Open |
| API model ID | — | gpt-5-nano | — |
| API providers | 8 | 21 (best) | 15 |
| Released | Aug 21, 2025 | Aug 7, 2025 | 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.
- DeepSeek-V3.1$6.35
- GPT-5 Nano$1.30
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: DeepSeek-V3.1, GPT-5 Nano 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, DeepSeek-V3.1 55/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Qwen3.5 9B 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, DeepSeek-V3.1, GPT-5 Nano 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); DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 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 $0.601 for DeepSeek-V3.1 (5.3× as much).
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), Qwen3.5 9B 139.5 (#101 of 148) and GPT-5 Nano 139.4 (#102 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 136.5–141.3), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-V3.1, GPT-5 Nano and Qwen3.5 9B yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 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 DeepSeek-V3.1. Maximum output per response: DeepSeek-V3.1 up to 8,192, GPT-5 Nano up to 128,000, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-V3.1 accepts text; GPT-5 Nano 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?
DeepSeek-V3.1 and Qwen3.5 9B publishes its weights (MIT License) and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
Qwen3.5 9B is the newest, released Feb 23, 2026. DeepSeek-V3.1 came out Aug 21, 2025; 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.
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