ALLaM-2-7b vs Aya Vision 32B
Aya Vision 32B comes out ahead, 15 to 0 on our weighted score.
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
- Our pick
CohereAya Vision 32B
15/100- ECI—
- Price—
- Context16K
Add a model
Make it a three-way comparison.
Aya Vision 32B is our pick
Aya Vision 32B is the better all-round choice, scoring 15/100 against ALLaM-2-7b (0). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. 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
- Longest contextAya Vision 32BAya Vision 32B 16,000 · ALLaM-2-7b 4,096 tokens
- Widest inputsAya Vision 32BALLaM-2-7b: Text · Aya Vision 32B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | ALLaM-2-7b | Aya Vision 32B |
|---|---|---|---|
| Inputs & features | 60% | 0 | 25 |
| Context window | 40% | 0 | 0 |
| Overall | 100% | 0/100 | 15/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ALLaM-2-7bSDAIA | Aya Vision 32BCohere |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 4,096 tokens | 16,000 tokens (best) |
| Max output | 4,096 tokens (best) | 4,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | No | No |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenCC-BY-NC-4.0 |
| API model ID | — | c4ai-aya-vision-32b |
| API providers | 2 (best) | 1 |
| Released | Jan 23, 2025 | Mar 4, 2025 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- ALLaM-2-7b—
- Aya Vision 32B—
Which should you choose?
Which is better: ALLaM-2-7b or Aya Vision 32B?
Aya Vision 32B is the better all-round choice, scoring 15/100 against ALLaM-2-7b (0). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, ALLaM-2-7b or Aya Vision 32B?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. ALLaM-2-7b has not been scored yet and Aya Vision 32B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for ALLaM-2-7b and Aya Vision 32B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ALLaM-2-7b and Aya Vision 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Aya Vision 32B has the largest context window at 16,000 tokens, against 4,096 for ALLaM-2-7b. Maximum output per response: ALLaM-2-7b up to 4,096, Aya Vision 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
ALLaM-2-7b accepts text; Aya Vision 32B accepts text and images. Aya Vision 32B handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (CC-BY-NC-4.0), so you can self-host them.
Which is newer?
Aya Vision 32B is the newest, released Mar 4, 2025. ALLaM-2-7b came out Jan 23, 2025.
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.
