Qwen2.5-Coder-32B-Instruct vs SOLAR 10.7B Instruct v1.0 vs Aya Expanse 32B
Qwen2.5-Coder-32B-Instruct comes out ahead, 25 to 10 and 0 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (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
CohereAya Expanse 32B
10/100- 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 25/100 against Aya Expanse 32B (10) and SOLAR 10.7B Instruct v1.0 (0). It leads on inputs & features. 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 priceQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct $0.745 · Aya Expanse 32B $0.75 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 · Aya Expanse 32B 128,000 · SOLAR 10.7B Instruct v1.0 4,096 tokens
- Widest inputsSame inputsQwen2.5-Coder-32B-Instruct: Text · SOLAR 10.7B Instruct v1.0: Text · Aya Expanse 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-Coder-32B-Instruct | SOLAR 10.7B Instruct v1.0 | Aya Expanse 32B |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 0 | 0 |
| Context window | 40% | 25 | 0 | 24 |
| Overall | 100% | 25/100 | 0/100 | 10/100 |
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 | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | SOLAR 10.7B Instruct v1.0Upstage | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.66 | — | $0.50 (best) |
| Output | $1.00 (best) | — | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.745 (best) | — | $0.75 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 7 providers | — | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 4,096 tokens | 128,000 tokens |
| Max output | 8,192 tokens (best) | 4,096 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 | OpenCC-BY-NC-4.0 | OpenCC-BY-NC-4.0 |
| API model ID | — | — | c4ai-aya-expanse-32b |
| API providers | 7 (best) | — | 2 |
| Released | Nov 12, 2024 | Dec 12, 2023 | Oct 24, 2024 |
| Knowledge cutoff | — | — | — |
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
- SOLAR 10.7B Instruct v1.0—
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, SOLAR 10.7B Instruct v1.0 or Aya Expanse 32B?
Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 25/100 against Aya Expanse 32B (10) and SOLAR 10.7B Instruct v1.0 (0). It leads on inputs & features. 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, Qwen2.5-Coder-32B-Instruct, SOLAR 10.7B Instruct v1.0 or Aya Expanse 32B?
Qwen2.5-Coder-32B-Instruct is cheaper at $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 $0.745 per million tokens for Qwen2.5-Coder-32B-Instruct versus $0.75 for Aya Expanse 32B (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. Qwen2.5-Coder-32B-Instruct has not been scored yet, SOLAR 10.7B Instruct v1.0 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, SOLAR 10.7B Instruct v1.0 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 SOLAR 10.7B Instruct v1.0 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 4,096 for SOLAR 10.7B Instruct v1.0. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, SOLAR 10.7B Instruct v1.0 up to 4,096, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; SOLAR 10.7B Instruct v1.0 accepts text; Aya Expanse 32B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (CC-BY-NC-4.0), so you can self-host them.
Which is newer?
Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Aya Expanse 32B came out Oct 24, 2024; SOLAR 10.7B Instruct v1.0 came out Dec 12, 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 Nov 2, 2026.
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