SOLAR 10.7B Instruct v1.0 vs Qwen Plus vs Qwen Plus Character (Japanese)
Qwen Plus comes out ahead, 51 to 0 and 0 on our weighted score, and it is the cheaper option too.
UpstageSOLAR 10.7B Instruct v1.0
0/100- ECI—
- Price—
- Context4K
- Our pick
Alibaba (Qwen)Qwen Plus
51/100- ECI—
- Price$0.40 / $1.20
- Context1M
- Alibaba (Qwen)
Qwen Plus Character (Japanese)
0/100- ECI—
- Price$0.50 / $1.40
- Context8K
Qwen Plus is our pick
Qwen Plus is the better all-round choice, scoring 51/100 against SOLAR 10.7B Instruct v1.0 (0) and Qwen Plus Character (Japanese) (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 PlusQwen Plus $0.60 · Qwen Plus Character (Japanese) $0.725 per 1M tokens (3:1 blend) · SOLAR 10.7B Instruct v1.0 unpriced
- Longest contextQwen PlusQwen Plus 1,000,000 · Qwen Plus Character (Japanese) 8,192 · SOLAR 10.7B Instruct v1.0 4,096 tokens
- Widest inputsSame inputsSOLAR 10.7B Instruct v1.0: Text · Qwen Plus: Text · Qwen Plus Character (Japanese): Text
- Self-hostingSOLAR 10.7B Instruct v1.0Publishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | SOLAR 10.7B Instruct v1.0 | Qwen Plus | Qwen Plus Character (Japanese) |
|---|---|---|---|---|
| Inputs & features | 60% | 0 | 45 | 0 |
| Context window | 40% | 0 | 60 | 0 |
| Overall | 100% | 0/100 | 51/100 | 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 | SOLAR 10.7B Instruct v1.0Upstage | Qwen PlusAlibaba (Qwen) | Qwen Plus Character (Japanese)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $0.40 (best) | $0.50 |
| Output | — | $1.20 (best) | $1.40 |
| Cached input | — | $0.08 | — |
| Blended (3:1) | — | $0.60 (best) | $0.725 |
| Long-context rate | — | Over 256K: $1.20 / $3.60 | Same rate |
| Price source | — | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 4,096 tokens | 1,000,000 tokens (best) | 8,192 tokens |
| Max output | 4,096 tokens | 32,768 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 | Yes | No |
| Tool calling | No | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | Proprietary |
| API model ID | — | qwen-plus | qwen-plus-character-ja |
| API providers | — | 10 (best) | 1 |
| Released | Dec 12, 2023 | Jan 25, 2024 | Jan 2024 |
| Knowledge cutoff | — | 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.
- SOLAR 10.7B Instruct v1.0—
- Qwen Plus$6.40
- Qwen Plus Character (Japanese)$7.80
Which should you choose?
Which is better: SOLAR 10.7B Instruct v1.0, Qwen Plus or Qwen Plus Character (Japanese)?
Qwen Plus is the better all-round choice, scoring 51/100 against SOLAR 10.7B Instruct v1.0 (0) and Qwen Plus Character (Japanese) (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, SOLAR 10.7B Instruct v1.0, Qwen Plus or Qwen Plus Character (Japanese)?
Qwen Plus is cheaper at $0.40 input / $1.20 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 $0.60 per million tokens for Qwen Plus versus $0.725 for Qwen Plus Character (Japanese) (1.2× 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. SOLAR 10.7B Instruct v1.0 has not been scored yet, Qwen Plus 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 SOLAR 10.7B Instruct v1.0, Qwen Plus 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 SOLAR 10.7B Instruct v1.0 and Qwen Plus Character (Japanese) does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen Plus has the largest context window at 1,000,000 tokens, against 8,192 for Qwen Plus Character (Japanese) and 4,096 for SOLAR 10.7B Instruct v1.0. Maximum output per response: SOLAR 10.7B Instruct v1.0 up to 4,096, Qwen Plus up to 32,768, Qwen Plus Character (Japanese) up to 512 tokens.
Which can read images, PDFs, audio or video?
SOLAR 10.7B Instruct v1.0 accepts text; Qwen Plus accepts text; Qwen Plus Character (Japanese) accepts text. They handle the same number of input types.
Are any of these open source?
SOLAR 10.7B Instruct v1.0 publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen Plus and Qwen Plus Character (Japanese) is proprietary.
Which is newer?
Qwen Plus is the newest, released Jan 25, 2024. Qwen Plus Character (Japanese) came out Jan 2024; SOLAR 10.7B Instruct v1.0 came out Dec 12, 2023. Knowledge cutoff: Qwen Plus 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 Nov 2, 2026.
