Mistral 7B vs Qwen Plus Character (Japanese) vs Sonar
Mistral 7B comes out ahead, 47 to 36 and 30 on our weighted score, and it is the cheaper option too.
- Our pick
Mistral AIMistral 7B
47/100- ECI—
- Price$0.25 / $0.25
- Context8K
Alibaba (Qwen)Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
PerplexitySonar
30/100- ECI—
- Price$1.00 / $1.00
- Context128K
Mistral 7B is our pick
Mistral 7B is the better all-round choice, scoring 47/100 against Qwen Plus Character (Japanese) (36) and Sonar (30). It leads on price. Sonar 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 priceMistral 7BMistral 7B $0.25 · Qwen Plus Character (Japanese) $0.725 · Sonar $1.00 per 1M tokens (3:1 blend)
- Longest contextSonarSonar 128,000 · Qwen Plus Character (Japanese) 8,192 · Mistral 7B 8,000 tokens
- Widest inputsSame inputsMistral 7B: Text · Qwen Plus Character (Japanese): Text · Sonar: Text
- Self-hostingMistral 7BPublishes downloadable weights
| Measure | Weight | Mistral 7B | Qwen Plus Character (Japanese) | Sonar |
|---|---|---|---|---|
| Price | 50% | 78 | 57 | 50 |
| Inputs & features | 30% | 25 | 25 | 0 |
| Context window | 20% | 0 | 0 | 24 |
| Overall | 100% | 47/100 | 36/100 | 30/100 |
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 | Mistral 7BMistral AI | Qwen Plus Character (Japanese)Alibaba (Qwen) | SonarPerplexity |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.50 | $1.00 |
| Output | $0.25 (best) | $1.40 | $1.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.25 (best) | $0.725 | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Alibaba API | Official Perplexity API |
| Limits | |||
| Context window | 8,000 tokens | 8,192 tokens | 128,000 tokens (best) |
| Max output | 8,000 tokens (best) | 512 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | open-mistral-7b | qwen-plus-character-ja | sonar |
| API providers | 1 | 1 | 6 (best) |
| Released | Sep 27, 2023 | Jan 2024 | Jan 1, 2024 |
| Knowledge cutoff | Dec 2023 | Apr 2024 | Sep 1, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral 7B$3.00
- Qwen Plus Character (Japanese)$7.80
- Sonar$12.00
Which should you choose?
Which is better: Mistral 7B, Qwen Plus Character (Japanese) or Sonar?
Mistral 7B is the better all-round choice, scoring 47/100 against Qwen Plus Character (Japanese) (36) and Sonar (30). It leads on price. Sonar 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, Mistral 7B, Qwen Plus Character (Japanese) or Sonar?
Mistral 7B is cheaper at $0.25 input / $0.25 output per million tokens (official Mistral 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 $0.25 per million tokens for Mistral 7B versus $0.725 for Qwen Plus Character (Japanese) (2.9× as much) and $1.00 for Sonar (4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Mistral 7B has not been scored yet, Qwen Plus Character (Japanese) has not been scored yet and Sonar has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral 7B, Qwen Plus Character (Japanese) and Sonar 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?
Sonar has the largest context window at 128,000 tokens, against 8,192 for Qwen Plus Character (Japanese) and 8,000 for Mistral 7B. Maximum output per response: Mistral 7B up to 8,000, Qwen Plus Character (Japanese) up to 512, Sonar up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Mistral 7B accepts text; Qwen Plus Character (Japanese) accepts text; Sonar accepts text. They handle the same number of input types.
Are any of these open source?
Mistral 7B publishes its weights and can be self-hosted; Qwen Plus Character (Japanese) and Sonar is proprietary.
Which is newer?
Qwen Plus Character (Japanese) is the newest, released Jan 2024. Sonar came out Jan 1, 2024; Mistral 7B came out Sep 27, 2023. Knowledge cutoff: Mistral 7B Dec 2023, Qwen Plus Character (Japanese) Apr 2024, Sonar Sep 1, 2025.
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.
