Qwen-VL Plus vs Qwen Plus Character (Japanese) vs Mistral 7B
Qwen-VL Plus comes out ahead, 57 to 47 and 36 on our weighted score, though Mistral 7B is 21% cheaper per token.
- Our pick
Alibaba (Qwen)Qwen-VL Plus
57/100- ECI—
- Price$0.21 / $0.63
- Context131K
- Alibaba (Qwen)
Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
Mistral AIMistral 7B
47/100- ECI—
- Price$0.25 / $0.25
- Context8K
Qwen-VL Plus is our pick
Qwen-VL Plus is the better all-round choice, scoring 57/100 against Mistral 7B (47) and Qwen Plus Character (Japanese) (36). It leads on inputs & features and context window. Mistral 7B wins on price. 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-VL Plus $0.315 · Qwen Plus Character (Japanese) $0.725 per 1M tokens (3:1 blend)
- Longest contextQwen-VL PlusQwen-VL Plus 131,072 · Qwen Plus Character (Japanese) 8,192 · Mistral 7B 8,000 tokens
- Widest inputsQwen-VL PlusQwen-VL Plus: Text, Images · Qwen Plus Character (Japanese): Text · Mistral 7B: Text
- Self-hostingMistral 7BPublishes downloadable weights
| Measure | Weight | Qwen-VL Plus | Qwen Plus Character (Japanese) | Mistral 7B |
|---|---|---|---|---|
| Price | 50% | 74 | 57 | 78 |
| Inputs & features | 30% | 50 | 25 | 25 |
| Context window | 20% | 24 | 0 | 0 |
| Overall | 100% | 57/100 | 36/100 | 47/100 |
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 | Qwen-VL PlusAlibaba (Qwen) | Qwen Plus Character (Japanese)Alibaba (Qwen) | Mistral 7BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.21 (best) | $0.50 | $0.25 |
| Output | $0.63 | $1.40 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.315 | $0.725 | $0.25 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Alibaba API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens (best) | 8,192 tokens | 8,000 tokens |
| Max output | 8,192 tokens (best) | 512 tokens | 8,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | qwen-vl-plus | qwen-plus-character-ja | open-mistral-7b |
| API providers | 2 (best) | 1 | 1 |
| Released | Jan 25, 2024 | Jan 2024 | Sep 27, 2023 |
| Knowledge cutoff | Apr 2024 | Apr 2024 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen-VL Plus$3.36
- Qwen Plus Character (Japanese)$7.80
- Mistral 7B$3.00
Which should you choose?
Which is better: Qwen-VL Plus, Qwen Plus Character (Japanese) or Mistral 7B?
Qwen-VL Plus is the better all-round choice, scoring 57/100 against Mistral 7B (47) and Qwen Plus Character (Japanese) (36). It leads on inputs & features and context window. Mistral 7B wins on price. 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, Qwen-VL Plus, Qwen Plus Character (Japanese) or Mistral 7B?
Mistral 7B is cheaper at $0.25 input / $0.25 output per million tokens (official Mistral API price). Qwen-VL Plus costs $0.21 input / $0.63 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). At a typical mix of three input tokens to one output token, that is $0.25 per million tokens for Mistral 7B versus $0.315 for Qwen-VL Plus (1.3× as much) and $0.725 for Qwen Plus Character (Japanese) (2.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen-VL Plus has not been scored yet, Qwen Plus Character (Japanese) has not been scored yet and Mistral 7B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen-VL Plus, Qwen Plus Character (Japanese) and Mistral 7B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen-VL Plus has the largest context window at 131,072 tokens, against 8,192 for Qwen Plus Character (Japanese) and 8,000 for Mistral 7B. Maximum output per response: Qwen-VL Plus up to 8,192, Qwen Plus Character (Japanese) up to 512, Mistral 7B up to 8,000 tokens.
Which can read images, PDFs, audio or video?
Qwen-VL Plus accepts text and images; Qwen Plus Character (Japanese) accepts text; Mistral 7B accepts text. Qwen-VL Plus handles the widest range of inputs.
Are any of these open source?
Mistral 7B publishes its weights and can be self-hosted; Qwen-VL Plus and Qwen Plus Character (Japanese) is proprietary.
Which is newer?
Qwen-VL Plus is the newest, released Jan 25, 2024. Qwen Plus Character (Japanese) came out Jan 2024; Mistral 7B came out Sep 27, 2023. Knowledge cutoff: Qwen-VL Plus Apr 2024, Qwen Plus Character (Japanese) Apr 2024, Mistral 7B Dec 2023.
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.
