Qwen Plus Character (Japanese) vs Qwen2.5-Coder-32B-Instruct vs SOLAR 10.7B Instruct v1.0
Qwen2.5-Coder-32B-Instruct comes out ahead, 25 to 0 and 0 on our weighted score.
Alibaba (Qwen)Qwen Plus Character (Japanese)
0/100- ECI—
- Price$0.50 / $1.40
- Context8K
- Our pickAlibaba (Qwen)
Qwen2.5-Coder-32B-Instruct
25/100- ECI—
- Price$0.66 / $1.00
- Context131K
UpstageSOLAR 10.7B Instruct v1.0
0/100- ECI—
- Price—
- Context4K
Qwen2.5-Coder-32B-Instruct is our pick
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 25/100 against Qwen Plus Character (Japanese) (0) and SOLAR 10.7B Instruct v1.0 (0). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 Plus Character (Japanese)Qwen Plus Character (Japanese) $0.725 · Qwen2.5-Coder-32B-Instruct $0.745 per 1M tokens (3:1 blend) · SOLAR 10.7B Instruct v1.0 unpriced
- Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Qwen Plus Character (Japanese) 8,192 · SOLAR 10.7B Instruct v1.0 4,096 tokens
- Widest inputsSame inputsQwen Plus Character (Japanese): Text · Qwen2.5-Coder-32B-Instruct: Text · SOLAR 10.7B Instruct v1.0: Text
- Self-hostingQwen2.5-Coder-32B-Instruct and SOLAR 10.7B Instruct v1.0Publishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Qwen Plus Character (Japanese) | Qwen2.5-Coder-32B-Instruct | SOLAR 10.7B Instruct v1.0 |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 25 | 0 |
| Context window | 40% | 0 | 25 | 0 |
| Overall | 100% | 0/100 | 25/100 | 0/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen Plus Character (Japanese)Alibaba (Qwen) | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | SOLAR 10.7B Instruct v1.0Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.50 (best) | $0.66 | — |
| Output | $1.40 | $1.00 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $0.725 (best) | $0.745 | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official Alibaba API | Median of 7 providers | — |
| Limits | |||
| Context window | 8,192 tokens | 131,072 tokens (best) | 4,096 tokens |
| Max output | 512 tokens | 8,192 tokens (best) | 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 | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenCC-BY-NC-4.0 |
| API model ID | qwen-plus-character-ja | — | — |
| API providers | 1 | 7 (best) | — |
| Released | Jan 2024 | Nov 12, 2024 | Dec 12, 2023 |
| Knowledge cutoff | 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.
- Qwen Plus Character (Japanese)$7.80
- Qwen2.5-Coder-32B-Instruct$8.60
- SOLAR 10.7B Instruct v1.0—
Which should you choose?
Which is better: Qwen Plus Character (Japanese), Qwen2.5-Coder-32B-Instruct or SOLAR 10.7B Instruct v1.0?
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 25/100 against Qwen Plus Character (Japanese) (0) and SOLAR 10.7B Instruct v1.0 (0). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Qwen Plus Character (Japanese), Qwen2.5-Coder-32B-Instruct or SOLAR 10.7B Instruct v1.0?
Qwen Plus Character (Japanese) is cheaper at $0.50 input / $1.40 output per million tokens (official Alibaba API price). Qwen2.5-Coder-32B-Instruct costs $0.66 input / $1.00 output per million tokens (median across 7 API providers). At a typical mix of three input tokens to one output token, that is $0.725 per million tokens for Qwen Plus Character (Japanese) versus $0.745 for Qwen2.5-Coder-32B-Instruct (1× as much). SOLAR 10.7B Instruct v1.0 has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen Plus Character (Japanese) has not been scored yet, Qwen2.5-Coder-32B-Instruct has not been scored yet and SOLAR 10.7B Instruct v1.0 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen Plus Character (Japanese), Qwen2.5-Coder-32B-Instruct and SOLAR 10.7B Instruct v1.0 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen Plus Character (Japanese) and SOLAR 10.7B Instruct v1.0 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 8,192 for Qwen Plus Character (Japanese) and 4,096 for SOLAR 10.7B Instruct v1.0. Maximum output per response: Qwen Plus Character (Japanese) up to 512, Qwen2.5-Coder-32B-Instruct up to 8,192, SOLAR 10.7B Instruct v1.0 up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen Plus Character (Japanese) accepts text; Qwen2.5-Coder-32B-Instruct accepts text; SOLAR 10.7B Instruct v1.0 accepts text. They handle the same number of input types.
Are any of these open source?
Qwen2.5-Coder-32B-Instruct and SOLAR 10.7B Instruct v1.0 publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen Plus Character (Japanese) is proprietary.
Which is newer?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Qwen Plus Character (Japanese) came out Jan 2024; SOLAR 10.7B Instruct v1.0 came out Dec 12, 2023. Knowledge cutoff: 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 Nov 2, 2026.
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