Aya Expanse 32B vs Qwen-VL OCR vs Qwen2.5-VL 7B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 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-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
Qwen2.5-VL 7B Instruct is our pick
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price and 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-VL 7B InstructQwen2.5-VL 7B Instruct $0.525 · Qwen-VL OCR $0.72 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct 131,072 · Aya Expanse 32B 128,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsQwen-VL OCR and Qwen2.5-VL 7B InstructAya Expanse 32B: Text · Qwen-VL OCR: Text, Images · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingAya Expanse 32B and Qwen2.5-VL 7B InstructPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Aya Expanse 32B | Qwen-VL OCR | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 56 | 57 | 63 |
| Inputs & features | 30% | 0 | 25 | 50 |
| Context window | 20% | 24 | 1 | 24 |
| Overall | 100% | 33/100 | 36/100 | 51/100 |
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-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.50 | $0.72 | $0.35 (best) |
| Output | $1.50 | $0.72 (best) | $1.05 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.72 | $0.525 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 34,096 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Proprietary | Open |
| API model ID | c4ai-aya-expanse-32b | qwen-vl-ocr | qwen2-5-vl-7b-instruct |
| API providers | 2 (best) | 1 | 1 |
| Released | Oct 24, 2024 | Oct 28, 2024 | Sep 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.
- Aya Expanse 32B$8.00
- Qwen-VL OCR$8.64
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Aya Expanse 32B, Qwen-VL OCR or Qwen2.5-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price and 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-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 output per million tokens (official Alibaba API price). 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 $0.525 per million tokens for Qwen2.5-VL 7B Instruct versus $0.72 for Qwen-VL OCR (1.4× as much) and $0.75 for Aya Expanse 32B (1.4× 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-VL 7B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 32B, Qwen-VL OCR and Qwen2.5-VL 7B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B and Qwen-VL OCR does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-VL 7B 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: Aya Expanse 32B up to 4,000, Qwen-VL OCR up to 4,096, Qwen2.5-VL 7B Instruct 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-VL 7B Instruct accepts text and images. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Aya Expanse 32B and Qwen2.5-VL 7B Instruct 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 7B Instruct came out Sep 2024. Knowledge cutoff: Qwen-VL OCR Apr 2024, Qwen2.5-VL 7B 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.
