Qwen-VL OCR vs Qwen2.5-VL 72B Instruct vs Aya Expanse 32B
Qwen-VL OCR comes out ahead, 36 to 33 and 30 on our weighted score, and it is the cheaper option too.
- Our pick
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
- Alibaba (Qwen)
Qwen2.5-VL 72B Instruct
30/100- ECI—
- Price$2.80 / $8.40
- Context131K
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Qwen-VL OCR is our pick
Qwen-VL OCR is the better all-round choice, scoring 36/100 against Aya Expanse 32B (33) and Qwen2.5-VL 72B Instruct (30). Qwen2.5-VL 72B Instruct 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 priceQwen-VL OCRQwen-VL OCR $0.72 · Aya Expanse 32B $0.75 · Qwen2.5-VL 72B Instruct $4.20 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Aya Expanse 32B 128,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsQwen-VL OCR and Qwen2.5-VL 72B InstructQwen-VL OCR: Text, Images · Qwen2.5-VL 72B Instruct: Text, Images · Aya Expanse 32B: Text
- Self-hostingQwen2.5-VL 72B Instruct and Aya Expanse 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Qwen-VL OCR | Qwen2.5-VL 72B Instruct | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 57 | 20 | 56 |
| Inputs & features | 30% | 25 | 50 | 0 |
| Context window | 20% | 1 | 24 | 24 |
| Overall | 100% | 36/100 | 30/100 | 33/100 |
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 | Qwen-VL OCRAlibaba (Qwen) | Qwen2.5-VL 72B InstructAlibaba (Qwen) | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.72 | $2.80 | $0.50 (best) |
| Output | $0.72 (best) | $8.40 | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.72 (best) | $4.20 | $0.75 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 34,096 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenCC-BY-NC-4.0 |
| API model ID | qwen-vl-ocr | qwen2-5-vl-72b-instruct | c4ai-aya-expanse-32b |
| API providers | 1 | 1 | 2 (best) |
| Released | Oct 28, 2024 | Sep 2024 | Oct 24, 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.
- Qwen-VL OCR$8.64
- Qwen2.5-VL 72B Instruct$44.80
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen-VL OCR, Qwen2.5-VL 72B Instruct or Aya Expanse 32B?
Qwen-VL OCR is the better all-round choice, scoring 36/100 against Aya Expanse 32B (33) and Qwen2.5-VL 72B Instruct (30). Qwen2.5-VL 72B Instruct 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, Qwen-VL OCR, Qwen2.5-VL 72B Instruct or Aya Expanse 32B?
Qwen-VL OCR is cheaper at $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); Qwen2.5-VL 72B Instruct costs $2.80 input / $8.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.72 per million tokens for Qwen-VL OCR versus $0.75 for Aya Expanse 32B (1× as much) and $4.20 for Qwen2.5-VL 72B Instruct (5.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen-VL OCR has not been scored yet, Qwen2.5-VL 72B Instruct 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 Qwen-VL OCR, Qwen2.5-VL 72B Instruct 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-VL OCR and Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-VL 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 34,096 for Qwen-VL OCR. Maximum output per response: Qwen-VL OCR up to 4,096, Qwen2.5-VL 72B Instruct up to 8,192, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen-VL OCR accepts text and images; Qwen2.5-VL 72B Instruct accepts text and images; Aya Expanse 32B accepts text. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 72B Instruct and Aya Expanse 32B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Qwen-VL OCR is the newest, released Oct 28, 2024. Aya Expanse 32B came out Oct 24, 2024; Qwen2.5-VL 72B Instruct came out Sep 2024. Knowledge cutoff: Qwen-VL OCR Apr 2024, Qwen2.5-VL 72B Instruct 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.
