Aya Expanse 8B vs Vision Large
Vision Large comes out ahead, 84 to 0 on our weighted score.
CohereAya Expanse 8B
0/100- ECI—
- Price—
- Context8K
- Our pick
VisparkVision Large
84/100- ECI—
- Price—
- Context1M
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Make it a three-way comparison.
Vision Large is our pick
Vision Large is the better all-round choice, scoring 84/100 against Aya Expanse 8B (0). It leads on inputs & features and context window. 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 contextVision LargeVision Large 1,000,000 · Aya Expanse 8B 8,000 tokens
- Widest inputsVision LargeAya Expanse 8B: Text · Vision Large: Text, Images, PDFs, Audio, Video
- Self-hostingAya Expanse 8BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Aya Expanse 8B | Vision Large |
|---|---|---|---|
| Inputs & features | 60% | 0 | 100 |
| Context window | 40% | 0 | 60 |
| Overall | 100% | 0/100 | 84/100 |
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 | Aya Expanse 8BCohere | Vision LargeVispark |
|---|---|---|
| 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 | 8,000 tokens | 1,000,000 tokens (best) |
| Max output | 4,000 tokens | 65,536 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | Yes |
| Audio | No | Yes |
| Video | No | Yes |
| Reasoning | No | Yes |
| Tool calling | No | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | OpenCC-BY-NC-4.0 | Proprietary |
| API model ID | — | — |
| API providers | — | — |
| Released | Oct 24, 2024 | May 15, 2024 |
| Knowledge cutoff | — | — |
What would a month cost?
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- Aya Expanse 8B—
- Vision Large—
Which should you choose?
Which is better: Aya Expanse 8B or Vision Large?
Vision Large is the better all-round choice, scoring 84/100 against Aya Expanse 8B (0). It leads on inputs & features and context window. 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, Aya Expanse 8B or Vision Large?
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. Aya Expanse 8B has not been scored yet and Vision Large has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Aya Expanse 8B and Vision Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 8B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Vision Large has the largest context window at 1,000,000 tokens, against 8,000 for Aya Expanse 8B. Maximum output per response: Aya Expanse 8B up to 4,000, Vision Large up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Aya Expanse 8B accepts text; Vision Large accepts text, images, PDFs, audio and video. Vision Large handles the widest range of inputs.
Are any of these open source?
Aya Expanse 8B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Vision Large is proprietary.
Which is newer?
Aya Expanse 8B is the newest, released Oct 24, 2024. Vision Large came out May 15, 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.
