Qwen2.5-Coder-32B-Instruct vs Qwen Plus Character (Japanese) vs Aya Expanse 32B
Qwen2.5-Coder-32B-Instruct comes out ahead, 39 to 32 and 27 on our weighted score.
- Our pick
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
- Alibaba (Qwen)
Qwen Plus Character (Japanese)
27/100- ECI—
- Price$0.50 / $1.40
- Context8K
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
Qwen2.5-Coder-32B-Instruct is our pick
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 39/100 against Aya Expanse 32B (32) and Qwen Plus Character (Japanese) (27). It leads on inputs & features. 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 priceQwen Plus Character (Japanese)Qwen Plus Character (Japanese) $0.725 · Qwen2.5-Coder-32B-Instruct $0.745 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsSame inputsQwen2.5-Coder-32B-Instruct: Text · Qwen Plus Character (Japanese): Text · Aya Expanse 32B: Text
- Self-hostingQwen2.5-Coder-32B-Instruct and Aya Expanse 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Qwen2.5-Coder-32B-Instruct | Qwen Plus Character (Japanese) | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 54 | 54 | 53 |
| Inputs & features | 30% | 25 | 0 | 0 |
| Context window | 20% | 25 | 0 | 24 |
| Overall | 100% | 39/100 | 27/100 | 32/100 |
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 | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | Qwen Plus Character (Japanese)Alibaba (Qwen) | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.66 | $0.50 (best) | $0.50 (best) |
| Output | $1.00 (best) | $1.40 | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.745 | $0.725 (best) | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official Alibaba API | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 8,192 tokens | 128,000 tokens |
| Max output | 8,192 tokens (best) | 512 tokens | 4,000 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 | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | — | qwen-plus-character-ja | c4ai-aya-expanse-32b |
| API providers | 7 (best) | 1 | 2 |
| Released | Nov 12, 2024 | Jan 2024 | Oct 24, 2024 |
| 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.
- Qwen2.5-Coder-32B-Instruct$8.60
- Qwen Plus Character (Japanese)$7.80
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Qwen Plus Character (Japanese) or Aya Expanse 32B?
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 39/100 against Aya Expanse 32B (32) and Qwen Plus Character (Japanese) (27). It leads on inputs & features. 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, Qwen2.5-Coder-32B-Instruct, Qwen Plus Character (Japanese) or Aya Expanse 32B?
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); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (official Cohere API price). 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) and $0.75 for Aya Expanse 32B (1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5-Coder-32B-Instruct has not been scored yet, Qwen Plus Character (Japanese) has not been scored yet and Aya Expanse 32B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-Coder-32B-Instruct, Qwen Plus Character (Japanese) and Aya Expanse 32B 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 Aya Expanse 32B 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 128,000 for Aya Expanse 32B and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Qwen Plus Character (Japanese) up to 512, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; Qwen Plus Character (Japanese) accepts text; Aya Expanse 32B accepts text. They handle the same number of input types.
Are any of these open source?
Qwen2.5-Coder-32B-Instruct and Aya Expanse 32B 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. Aya Expanse 32B came out Oct 24, 2024; Qwen Plus Character (Japanese) came out Jan 2024. 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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