Qwen2.5 14B Instruct vs Qwen2.5-VL 7B Instruct vs Aya Expanse 32B
Qwen2.5-VL 7B Instruct comes out ahead, 51 to 42 and 33 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen2.5 14B Instruct
42/100- ECI—
- Price$0.35 / $1.40
- Context131K
- Our pickAlibaba (Qwen)
Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
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 14B Instruct (42) 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 · Qwen2.5 14B Instruct $0.613 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 14B Instruct and Qwen2.5-VL 7B InstructQwen2.5 14B Instruct 131,072 · Qwen2.5-VL 7B Instruct 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsQwen2.5-VL 7B InstructQwen2.5 14B Instruct: Text · Qwen2.5-VL 7B Instruct: Text, Images · Aya Expanse 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5 14B Instruct | Qwen2.5-VL 7B Instruct | Aya Expanse 32B |
|---|---|---|---|---|
| Price | 50% | 60 | 63 | 56 |
| Inputs & features | 30% | 25 | 50 | 0 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 42/100 | 51/100 | 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 | Qwen2.5 14B InstructAlibaba (Qwen) | Qwen2.5-VL 7B InstructAlibaba (Qwen) | Aya Expanse 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.35 (best) | $0.35 (best) | $0.50 |
| Output | $1.40 | $1.05 (best) | $1.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.613 | $0.525 (best) | $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 | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens (best) | 8,192 tokens (best) | 4,000 tokens |
| 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 | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenCC-BY-NC-4.0 |
| API model ID | qwen2-5-14b-instruct | qwen2-5-vl-7b-instruct | c4ai-aya-expanse-32b |
| API providers | 1 | 1 | 2 (best) |
| Released | Sep 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.
- Qwen2.5 14B Instruct$6.30
- Qwen2.5-VL 7B Instruct$5.60
- Aya Expanse 32B$8.00
Which should you choose?
Which is better: Qwen2.5 14B Instruct, Qwen2.5-VL 7B Instruct or Aya Expanse 32B?
Qwen2.5-VL 7B Instruct is the better all-round choice, scoring 51/100 against Qwen2.5 14B Instruct (42) 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, Qwen2.5 14B Instruct, Qwen2.5-VL 7B Instruct or Aya Expanse 32B?
Qwen2.5-VL 7B Instruct is cheaper at $0.35 input / $1.05 output per million tokens (official Alibaba API price). Qwen2.5 14B Instruct costs $0.35 input / $1.40 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 $0.613 for Qwen2.5 14B Instruct (1.2× 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. Qwen2.5 14B Instruct has not been scored yet, Qwen2.5-VL 7B 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 Qwen2.5 14B Instruct, Qwen2.5-VL 7B 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 Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen2.5 14B 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: Qwen2.5 14B Instruct up to 8,192, Qwen2.5-VL 7B Instruct up to 8,192, Aya Expanse 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 14B Instruct accepts text; Qwen2.5-VL 7B Instruct accepts text and images; Aya Expanse 32B accepts text. 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?
Aya Expanse 32B is the newest, released Oct 24, 2024. Qwen2.5 14B Instruct came out Sep 2024; Qwen2.5-VL 7B Instruct came out Sep 2024. Knowledge cutoff: Qwen2.5 14B Instruct 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.
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