DeepSeek OCR 2 vs GPT-5 Nano vs Qwen3.5 9B
Qwen3.5 9B comes out ahead, 79 to 75 and 32 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek OCR 2
32/100- ECI—
- Price$0.89 / $1.47
- Context8K
OpenAIGPT-5 Nano
75/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
- Our pick
Alibaba (Qwen)Qwen3.5 9B
79/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
Qwen3.5 9B is our pick
Qwen3.5 9B is the better all-round choice, scoring 79/100 against GPT-5 Nano (75) and DeepSeek OCR 2 (32). It leads on price and inputs & features. GPT-5 Nano wins on context window. 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 priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · DeepSeek OCR 2 $1.03 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · DeepSeek OCR 2 8,192 tokens
- Widest inputsQwen3.5 9BDeepSeek OCR 2: Text, Images · GPT-5 Nano: Text, Images · Qwen3.5 9B: Text, Images, Video
- Self-hostingDeepSeek OCR 2 and Qwen3.5 9BPublishes downloadable weights
| Measure | Weight | DeepSeek OCR 2 | GPT-5 Nano | Qwen3.5 9B |
|---|---|---|---|---|
| Price | 50% | 49 | 91 | 95 |
| Inputs & features | 30% | 25 | 70 | 80 |
| Context window | 20% | 0 | 44 | 37 |
| Overall | 100% | 32/100 | 75/100 | 79/100 |
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 | DeepSeek OCR 2DeepSeek | GPT-5 NanoOpenAI | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 139.4 | 139.5 (best) |
| ECI rank | — | #102 of 148 | #101 of 148 (best) |
| 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 | $0.89 | $0.05 (best) | $0.10 |
| Output | $1.47 | $0.40 | $0.15 (best) |
| Cached input | — | $0.005 | — |
| Blended (3:1) | $1.03 | $0.138 | $0.113 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official OpenAI API | Median of 14 providers |
| Limits | |||
| Context window | 8,192 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 | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | No | Yesminimal · low · medium · high | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | gpt-5-nano | — |
| API providers | 2 | 21 (best) | 15 |
| Released | Jan 27, 2026 | 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 OCR 2$11.83
- GPT-5 Nano$1.30
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: DeepSeek OCR 2, GPT-5 Nano or Qwen3.5 9B?
Qwen3.5 9B is the better all-round choice, scoring 79/100 against GPT-5 Nano (75) and DeepSeek OCR 2 (32). It leads on price and inputs & features. GPT-5 Nano wins on context window. 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, DeepSeek OCR 2, 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 OCR 2 costs $0.89 input / $1.47 output per million tokens (median across 1 API provider). 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 $1.03 for DeepSeek OCR 2 (9.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek OCR 2 has not been scored yet, GPT-5 Nano has an ECI of 139.4 and Qwen3.5 9B has an ECI of 139.5.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek OCR 2, GPT-5 Nano and Qwen3.5 9B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that DeepSeek OCR 2 does not support tool calling, which most coding agents need.
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 8,192 for DeepSeek OCR 2. Maximum output per response: DeepSeek OCR 2 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 OCR 2 accepts text and images; 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 OCR 2 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. DeepSeek OCR 2 came out Jan 27, 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.
