Aya Expanse 32B vs Qwen-VL OCR vs Qwen2.5-Coder-0.5B
Qwen2.5-Coder-0.5B comes out ahead, 49 to 36 and 33 on our weighted score, and it is the cheaper option too.
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
- Our pickAlibaba (Qwen)
Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Qwen2.5-Coder-0.5B is our pick
Qwen2.5-Coder-0.5B is the better all-round choice, scoring 49/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. Aya Expanse 32B wins on context window. Qwen-VL OCR wins 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 priceQwen2.5-Coder-0.5BQwen2.5-Coder-0.5B $0.10 · Qwen-VL OCR $0.72 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextAya Expanse 32BAya Expanse 32B 128,000 · Qwen-VL OCR 34,096 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsQwen-VL OCRAya Expanse 32B: Text · Qwen-VL OCR: Text, Images · Qwen2.5-Coder-0.5B: Text
- Self-hostingAya Expanse 32B and Qwen2.5-Coder-0.5BPublishes downloadable weights (CC-BY-NC-4.0 and Apache 2.0)
| Measure | Weight | Aya Expanse 32B | Qwen-VL OCR | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 56 | 57 | 97 |
| Inputs & features | 30% | 0 | 25 | 0 |
| Context window | 20% | 24 | 1 | 0 |
| Overall | 100% | 33/100 | 36/100 | 49/100 |
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 | Aya Expanse 32BCohere | Qwen-VL OCRAlibaba (Qwen) | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 88.2 |
| ECI rank | — | — | #148 of 148 |
| Price per million tokens | |||
| Input | $0.50 | $0.72 | $0.10 (best) |
| Output | $1.50 | $0.72 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.72 | $0.10 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens (best) | 34,096 tokens | 32,768 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | OpenApache 2.0 |
| API model ID | c4ai-aya-expanse-32b | qwen-vl-ocr | — |
| API providers | 2 (best) | 1 | 1 |
| Released | Oct 24, 2024 | Oct 28, 2024 | Nov 12, 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.
- Aya Expanse 32B$8.00
- Qwen-VL OCR$8.64
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Aya Expanse 32B, Qwen-VL OCR or Qwen2.5-Coder-0.5B?
Qwen2.5-Coder-0.5B is the better all-round choice, scoring 49/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. Aya Expanse 32B wins on context window. Qwen-VL OCR wins 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, Aya Expanse 32B, Qwen-VL OCR or Qwen2.5-Coder-0.5B?
Qwen2.5-Coder-0.5B is cheaper at $0.10 input / $0.10 output per million tokens (median across 1 API provider). Qwen-VL OCR costs $0.72 input / $0.72 output per million tokens (official Alibaba API price); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.10 per million tokens for Qwen2.5-Coder-0.5B versus $0.72 for Qwen-VL OCR (7.2× as much) and $0.75 for Aya Expanse 32B (7.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Aya Expanse 32B has not been scored yet, Qwen-VL OCR has not been scored yet and Qwen2.5-Coder-0.5B has an ECI of 88.2.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, Qwen-VL OCR and Qwen2.5-Coder-0.5B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B, Qwen-VL OCR and Qwen2.5-Coder-0.5B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Aya Expanse 32B has the largest context window at 128,000 tokens, against 34,096 for Qwen-VL OCR and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Aya Expanse 32B up to 4,000, Qwen-VL OCR up to 4,096, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Qwen-VL OCR accepts text and images; Qwen2.5-Coder-0.5B accepts text. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Aya Expanse 32B and Qwen2.5-Coder-0.5B publishes its weights (CC-BY-NC-4.0 and Apache 2.0) and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Qwen-VL OCR came out Oct 28, 2024; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: Qwen-VL OCR 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 Oct 4, 2026.
