Qwen2.5-Coder-32B-Instruct vs Vision Small vs Aya Expanse 32B
Vision Small comes out ahead, 61 to 39 and 32 on our weighted score, though Qwen2.5-Coder-32B-Instruct is 2.1× cheaper per token.
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
39/100- ECI—
- Price$0.66 / $1.00
- Context131K
- Our pick
VisparkVision Small
61/100- ECI—
- Price$1.05 / $3.16
- Context1M
CohereAya Expanse 32B
32/100- ECI—
- Price$0.50 / $1.50
- Context128K
Vision Small is our pick
Vision Small is the better all-round choice, scoring 61/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads on inputs & features and context window. 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 priceQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct $0.745 · Aya Expanse 32B $0.75 · Vision Small $1.58 per 1M tokens (3:1 blend)
- Longest contextVision SmallVision Small 1,000,000 · Qwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsVision SmallQwen2.5-Coder-32B-Instruct: Text · Vision Small: Text, Images, PDFs, Audio, Video · 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 | Vision Small | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 54 | 39 | 53 |
| Inputs & features | 30% | 25 | 100 | 0 |
| Context window | 20% | 25 | 60 | 24 |
| Overall | 100% | 39/100 | 61/100 | 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) | Vision SmallVispark | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.66 | $1.05 | $0.50 (best) |
| Output | $1.00 (best) | $3.16 | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.745 (best) | $1.58 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official Vispark API | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 65,536 tokens (best) | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenCC-BY-NC-4.0 |
| API model ID | — | vispark/vision-small | c4ai-aya-expanse-32b |
| API providers | 7 (best) | 1 | 2 |
| Released | Nov 12, 2024 | May 15, 2024 | 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
- Vision Small$16.82
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5-Coder-32B-Instruct, Vision Small or Aya Expanse 32B?
Vision Small is the better all-round choice, scoring 61/100 against Qwen2.5-Coder-32B-Instruct (39) and Aya Expanse 32B (32). It leads on inputs & features and context window. 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, Vision Small 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); Vision Small costs $1.05 input / $3.16 output per million tokens (official Vispark 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) and $1.58 for Vision Small (2.1× 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, Vision Small 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, Vision Small 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 Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Vision Small has the largest context window at 1,000,000 tokens, against 131,072 for Qwen2.5-Coder-32B-Instruct and 128,000 for Aya Expanse 32B. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Vision Small up to 65,536, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-Coder-32B-Instruct accepts text; Vision Small accepts text, images, PDFs, audio and video; Aya Expanse 32B accepts text. Vision Small handles the widest range of inputs.
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; Vision Small 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; Vision Small came out May 15, 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.
