Sonar vs Mixtral 8x7B vs Qwen Plus Character (Japanese)
Too close to call on our weighted score (Mixtral 8x7B 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
Mistral AIMixtral 8x7B
36/100- ECI118.5
- Price$0.70 / $0.70
- Context32K
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 (Mixtral 8x7B 36/100, Qwen Plus Character (Japanese) 36/100, Sonar 30/100), so choose by what matters most for your work: Mixtral 8x7B 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 priceMixtral 8x7BMixtral 8x7B $0.70 · Qwen Plus Character (Japanese) $0.725 · Sonar $1.00 per 1M tokens (3:1 blend)
- Longest contextSonarSonar 128,000 · Mixtral 8x7B 32,000 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsSame inputsSonar: Text · Mixtral 8x7B: Text · Qwen Plus Character (Japanese): Text
- Self-hostingMixtral 8x7BPublishes downloadable weights
| Measure | Weight | Sonar | Mixtral 8x7B | Qwen Plus Character (Japanese) |
|---|---|---|---|---|
| Price | 50% | 50 | 57 | 57 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 0 | 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 | Mixtral 8x7BMistral AI | Qwen Plus Character (Japanese)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 118.5 | — |
| ECI rank | — | #142 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 30.6% | — |
| Price per million tokens | |||
| Input | $1.00 | $0.70 | $0.50 (best) |
| Output | $1.00 | $0.70 (best) | $1.40 |
| Cached input | — | — | — |
| Blended (3:1) | $1.00 | $0.70 (best) | $0.725 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Perplexity API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens (best) | 32,000 tokens | 8,192 tokens |
| Max output | 4,096 tokens | 32,000 tokens (best) | 512 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 | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | sonar | open-mixtral-8x7b | qwen-plus-character-ja |
| API providers | 6 (best) | 1 | 1 |
| Released | Jan 1, 2024 | Dec 11, 2023 | Jan 2024 |
| Knowledge cutoff | Sep 1, 2025 | Jan 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
- Mixtral 8x7B$8.40
- Qwen Plus Character (Japanese)$7.80
Which should you choose?
Which is better: Sonar, Mixtral 8x7B or Qwen Plus Character (Japanese)?
It is close. Our weighted score puts them within a point (Mixtral 8x7B 36/100, Qwen Plus Character (Japanese) 36/100, Sonar 30/100), so choose by what matters most for your work: Mixtral 8x7B 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, Mixtral 8x7B or Qwen Plus Character (Japanese)?
Mixtral 8x7B is cheaper at $0.70 input / $0.70 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 $0.70 per million tokens for Mixtral 8x7B 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, Mixtral 8x7B has an ECI of 118.5 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, Mixtral 8x7B 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?
Sonar has the largest context window at 128,000 tokens, against 32,000 for Mixtral 8x7B and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: Sonar up to 4,096, Mixtral 8x7B up to 32,000, Qwen Plus Character (Japanese) up to 512 tokens.
Which can read images, PDFs, audio or video?
Sonar accepts text; Mixtral 8x7B accepts text; Qwen Plus Character (Japanese) accepts text. They handle the same number of input types.
Are any of these open source?
Mixtral 8x7B publishes its weights and can be self-hosted; Sonar and Qwen Plus Character (Japanese) is proprietary.
Which is newer?
Sonar is the newest, released Jan 1, 2024. Qwen Plus Character (Japanese) came out Jan 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Sonar Sep 1, 2025, Mixtral 8x7B Jan 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.
