Aya Expanse 32B vs Qwen2.5-Coder-32B-Instruct vs Qwen2.5-VL 7B Instruct
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 45 and 33 on our weighted score, though Qwen2.5-Coder-32B-Instruct is 10% cheaper per token.
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
Alibaba (Qwen)Qwen2.5-Coder-32B-Instruct
45/100- ECI—
- Price$0.43 / $0.60
- Context131K
- 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 Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on inputs & features. Qwen2.5-Coder-32B-Instruct wins on price. 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 $0.473 · Qwen2.5-VL 7B Instruct $0.525 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-Coder-32B-Instruct and Qwen2.5-VL 7B InstructQwen2.5-Coder-32B-Instruct 131,072 · Qwen2.5-VL 7B Instruct 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsQwen2.5-VL 7B InstructAya Expanse 32B: Text · Qwen2.5-Coder-32B-Instruct: Text · Qwen2.5-VL 7B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Aya Expanse 32B | Qwen2.5-Coder-32B-Instruct | Qwen2.5-VL 7B Instruct |
|---|---|---|---|---|
| Price | 50% | 56 | 65 | 63 |
| Inputs & features | 30% | 0 | 25 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 33/100 | 45/100 | 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 | Qwen2.5-Coder-32B-InstructAlibaba (Qwen) | Qwen2.5-VL 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.50 | $0.43 | $0.35 (best) |
| Output | $1.50 | $0.60 (best) | $1.05 |
| Cached input | — | — | — |
| Blended (3:1) | $0.75 | $0.473 (best) | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 4 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 4,000 tokens | 8,192 tokens (best) | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenCC-BY-NC-4.0 | Open | Open |
| API model ID | c4ai-aya-expanse-32b | — | qwen2-5-vl-7b-instruct |
| API providers | 2 | 4 (best) | 1 |
| Released | Oct 24, 2024 | Nov 12, 2024 | Sep 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
- Qwen2.5-Coder-32B-Instruct$5.50
- Qwen2.5-VL 7B Instruct$5.60
Which should you choose?
Which is better: Aya Expanse 32B, Qwen2.5-Coder-32B-Instruct or Qwen2.5-VL 7B Instruct?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5-Coder-32B-Instruct (45) and Aya Expanse 32B (33). It leads on inputs & features. Qwen2.5-Coder-32B-Instruct wins on price. 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, Qwen2.5-Coder-32B-Instruct or Qwen2.5-VL 7B Instruct?
Qwen2.5-Coder-32B-Instruct is cheaper at $0.43 input / $0.60 output per million tokens (median across 4 API providers). Qwen2.5-VL 7B Instruct costs $0.35 input / $1.05 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 $0.473 per million tokens for Qwen2.5-Coder-32B-Instruct versus $0.525 for Qwen2.5-VL 7B Instruct (1.1× as much) and $0.75 for Aya Expanse 32B (1.6× 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, Qwen2.5-Coder-32B-Instruct 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, Qwen2.5-Coder-32B-Instruct 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 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5-Coder-32B-Instruct and Qwen2.5-VL 7B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Aya Expanse 32B. Maximum output per response: Aya Expanse 32B up to 4,000, Qwen2.5-Coder-32B-Instruct up to 8,192, Qwen2.5-VL 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 32B accepts text; Qwen2.5-Coder-32B-Instruct accepts text; Qwen2.5-VL 7B Instruct accepts text and images. Qwen2.5-VL 7B Instruct handles the widest range of inputs.
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; Qwen2.5-VL 7B Instruct came out Sep 2024. Knowledge cutoff: 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.
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