Qwen2.5-VL 72B Instruct vs Aya Expanse 32B vs Command R+
Too close to call on our weighted score (Aya Expanse 32B 33, Qwen2.5-VL 72B Instruct 30, Command R+ 22). The right pick depends on what you value most.
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
- Cohere
Command R+
22/100- ECI—
- Price$2.50 / $10.00
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Aya Expanse 32B 33/100, Qwen2.5-VL 72B Instruct 30/100, Command R+ 22/100), so choose by what matters most for your work: Aya Expanse 32B on price and Qwen2.5-VL 72B Instruct for long inputs. 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 priceAya Expanse 32BAya Expanse 32B $0.75 · Qwen2.5-VL 72B Instruct $4.20 · Command R+ $4.38 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Aya Expanse 32B 128,000 · Command R+ 128,000 tokens
- Widest inputsQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct: Text, Images · Aya Expanse 32B: Text · Command R+: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-VL 72B Instruct | Aya Expanse 32B | Command R+ |
|---|---|---|---|---|
| Price | 50% | 20 | 56 | 19 |
| Inputs & features | 30% | 50 | 0 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 30/100 | 33/100 | 22/100 |
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-VL 72B InstructAlibaba (Qwen) | Aya Expanse 32BCohere | Command R+Cohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $2.80 | $0.50 (best) | $2.50 |
| Output | $8.40 | $1.50 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $4.20 | $0.75 (best) | $4.38 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 1 providers | Official Cohere API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 8,192 tokens (best) | 4,000 tokens | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenCC-BY-NC-4.0 | Open |
| API model ID | qwen2-5-vl-72b-instruct | c4ai-aya-expanse-32b | command-r-plus-08-2024 |
| API providers | 1 | 2 | 7 (best) |
| Released | Sep 2024 | Oct 24, 2024 | Aug 30, 2024 |
| Knowledge cutoff | Apr 2024 | — | Jun 1, 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-VL 72B Instruct$44.80
- Aya Expanse 32B$8.00
- Command R+$45.00
Which should you choose?
Which is better: Qwen2.5-VL 72B Instruct, Aya Expanse 32B or Command R+?
It is close. Our weighted score puts them within 3 points (Aya Expanse 32B 33/100, Qwen2.5-VL 72B Instruct 30/100, Command R+ 22/100), so choose by what matters most for your work: Aya Expanse 32B on price and Qwen2.5-VL 72B Instruct for long inputs. 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-VL 72B Instruct, Aya Expanse 32B or Command R+?
Aya Expanse 32B is cheaper at $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); Command R+ costs $2.50 input / $10.00 output per million tokens (official Cohere API price). At a typical mix of three input tokens to one output token, that is $0.75 per million tokens for Aya Expanse 32B versus $4.20 for Qwen2.5-VL 72B Instruct (5.6× as much) and $4.38 for Command R+ (5.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5-VL 72B Instruct has not been scored yet, Aya Expanse 32B has not been scored yet and Command R+ has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-VL 72B Instruct, Aya Expanse 32B and Command R+ 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-VL 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 128,000 for Command R+. Maximum output per response: Qwen2.5-VL 72B Instruct up to 8,192, Aya Expanse 32B up to 4,000, Command R+ up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-VL 72B Instruct accepts text and images; Aya Expanse 32B accepts text; Command R+ accepts text. Qwen2.5-VL 72B 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-VL 72B Instruct came out Sep 2024; Command R+ came out Aug 30, 2024. Knowledge cutoff: Qwen2.5-VL 72B Instruct Apr 2024, Command R+ Jun 1, 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.
