Sonar vs Qwen-VL OCR vs Qwen Plus Character (Japanese)
Too close to call on our weighted score (Qwen-VL OCR 36, Qwen Plus Character (Japanese) 36, Sonar 30). The right pick depends on what you value most.
PerplexitySonar
30/100- ECI—
- Price$1.00 / $1.00
- Context128K
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
- Alibaba (Qwen)
Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
Too close to call
It is close. Our weighted score puts them within a point (Qwen-VL OCR 36/100, Qwen Plus Character (Japanese) 36/100, Sonar 30/100), so choose by what matters most for your work: Qwen-VL OCR on price and Sonar for long inputs. 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 priceQwen-VL OCRQwen-VL OCR $0.72 · Qwen Plus Character (Japanese) $0.725 · Sonar $1.00 per 1M tokens (3:1 blend)
- Longest contextSonarSonar 128,000 · Qwen-VL OCR 34,096 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsQwen-VL OCRSonar: Text · Qwen-VL OCR: Text, Images · Qwen Plus Character (Japanese): Text
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Sonar | Qwen-VL OCR | Qwen Plus Character (Japanese) |
|---|---|---|---|---|
| Price | 50% | 50 | 57 | 57 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 1 | 0 |
| Overall | 100% | 30/100 | 36/100 | 36/100 |
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 | SonarPerplexity | Qwen-VL OCRAlibaba (Qwen) | Qwen Plus Character (Japanese)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.72 | $0.50 (best) |
| Output | $1.00 | $0.72 (best) | $1.40 |
| Cached input | — | — | — |
| Blended (3:1) | $1.00 | $0.72 (best) | $0.725 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Perplexity API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens (best) | 34,096 tokens | 8,192 tokens |
| Max output | 4,096 tokens (best) | 4,096 tokens (best) | 512 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | sonar | qwen-vl-ocr | qwen-plus-character-ja |
| API providers | 6 (best) | 1 | 1 |
| Released | Jan 1, 2024 | Oct 28, 2024 | Jan 2024 |
| Knowledge cutoff | Sep 1, 2025 | Apr 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Sonar$12.00
- Qwen-VL OCR$8.64
- Qwen Plus Character (Japanese)$7.80
Which should you choose?
Which is better: Sonar, Qwen-VL OCR or Qwen Plus Character (Japanese)?
It is close. Our weighted score puts them within a point (Qwen-VL OCR 36/100, Qwen Plus Character (Japanese) 36/100, Sonar 30/100), so choose by what matters most for your work: Qwen-VL OCR on price and Sonar for long inputs. 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, Sonar, Qwen-VL OCR or Qwen Plus Character (Japanese)?
Qwen-VL OCR is cheaper at $0.72 input / $0.72 output per million tokens (official Alibaba API price). Qwen Plus Character (Japanese) costs $0.50 input / $1.40 output per million tokens (official Alibaba API price); Sonar costs $1.00 input / $1.00 output per million tokens (official Perplexity API price). At a typical mix of three input tokens to one output token, that is $0.72 per million tokens for Qwen-VL OCR versus $0.725 for Qwen Plus Character (Japanese) (1× as much) and $1.00 for Sonar (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Sonar has not been scored yet, Qwen-VL OCR has not been scored yet and Qwen Plus Character (Japanese) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sonar, Qwen-VL OCR and Qwen Plus Character (Japanese) yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sonar and Qwen-VL OCR does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sonar has the largest context window at 128,000 tokens, against 34,096 for Qwen-VL OCR and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: Sonar up to 4,096, Qwen-VL OCR up to 4,096, Qwen Plus Character (Japanese) up to 512 tokens.
Which can read images, PDFs, audio or video?
Sonar accepts text; Qwen-VL OCR accepts text and images; Qwen Plus Character (Japanese) accepts text. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
No. Sonar, Qwen-VL OCR and Qwen Plus Character (Japanese) are proprietary and only available through APIs and apps.
Which is newer?
Qwen-VL OCR is the newest, released Oct 28, 2024. Sonar came out Jan 1, 2024; Qwen Plus Character (Japanese) came out Jan 2024. Knowledge cutoff: Sonar Sep 1, 2025, Qwen-VL OCR Apr 2024, Qwen Plus Character (Japanese) Apr 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.
