Sonar vs Qwen2.5-VL 7B Instruct vs Qwen Plus Character (Japanese)
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 36 and 30 on our weighted score, and it is the cheaper option too.
PerplexitySonar
30/100- ECI—
- Price$1.00 / $1.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
- Alibaba (Qwen)
Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen Plus Character (Japanese) (36) and Sonar (30). It leads on price and inputs & features. 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 priceQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct $0.525 · Qwen Plus Character (Japanese) $0.725 · Sonar $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct 131,072 · Sonar 128,000 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsQwen2.5-VL 7B InstructSonar: Text · Qwen2.5-VL 7B Instruct: Text, Images · Qwen Plus Character (Japanese): Text
- Self-hostingQwen2.5-VL 7B InstructPublishes downloadable weights
| Measure | Weight | Sonar | Qwen2.5-VL 7B Instruct | Qwen Plus Character (Japanese) |
|---|---|---|---|---|
| Price | 50% | 50 | 63 | 57 |
| Inputs & features | 30% | 0 | 50 | 25 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 30/100 | 51/100 | 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 | Qwen2.5-VL 7B InstructAlibaba (Qwen) | Qwen Plus Character (Japanese)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.35 (best) | $0.50 |
| Output | $1.00 (best) | $1.05 | $1.40 |
| Cached input | — | — | — |
| Blended (3:1) | $1.00 | $0.525 (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 | 131,072 tokens (best) | 8,192 tokens |
| Max output | 4,096 tokens | 8,192 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | sonar | qwen2-5-vl-7b-instruct | qwen-plus-character-ja |
| API providers | 6 (best) | 1 | 1 |
| Released | Jan 1, 2024 | Sep 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
- Qwen2.5-VL 7B Instruct$5.60
- Qwen Plus Character (Japanese)$7.80
Which should you choose?
Which is better: Sonar, Qwen2.5-VL 7B Instruct or Qwen Plus Character (Japanese)?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen Plus Character (Japanese) (36) and Sonar (30). It leads on price and inputs & features. 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, Qwen2.5-VL 7B Instruct or Qwen Plus Character (Japanese)?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 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 $0.525 per million tokens for Qwen2.5-VL 7B Instruct versus $0.725 for Qwen Plus Character (Japanese) (1.4× as much) and $1.00 for Sonar (1.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Sonar has not been scored yet, Qwen2.5-VL 7B Instruct 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, Qwen2.5-VL 7B Instruct 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 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-VL 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Sonar and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: Sonar up to 4,096, Qwen2.5-VL 7B Instruct up to 8,192, Qwen Plus Character (Japanese) up to 512 tokens.
Which can read images, PDFs, audio or video?
Sonar accepts text; Qwen2.5-VL 7B Instruct accepts text and images; Qwen Plus Character (Japanese) accepts text. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 7B Instruct publishes its weights and can be self-hosted; Sonar and Qwen Plus Character (Japanese) is proprietary.
Which is newer?
Qwen2.5-VL 7B Instruct is the newest, released Sep 2024. Sonar came out Jan 1, 2024; Qwen Plus Character (Japanese) came out Jan 2024. Knowledge cutoff: Sonar Sep 1, 2025, Qwen2.5-VL 7B Instruct 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.
