Aya Expanse is Cohere Labs’ open-weight multilingual research model family, released in 8-billion-parameter and 32-billion-parameter variants. Its model cards describe a 23-language, text-in/text-out scope; they also specify a noncommercial Creative Commons license and an evaluation setup that should be read as benchmark evidence, not a guarantee of quality for every language or task.
What is Aya Expanse?
Aya Expanse is a multilingual large language model family developed by Cohere Labs and released with downloadable weights. The two variants contain 8 billion and 32 billion parameters. Cohere Labs describes the release as a research model, with development involving a Command-family pretrained model, multilingual data arbitrage, preference training, safety tuning, and model merging. The Aya Expanse 8B model card and 32B model card are the primary references for each version.
Aya Expanse is one release within the broader Aya research initiative. The initiative includes other models, so its wider language coverage or other terms should not be assumed to apply to Aya Expanse. Cohere Labs’ Aya overview provides that broader context.
Which languages and inputs does Aya Expanse support?
The 8B card specifies text input and text generation, an 8K context length, and these 23 languages:
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- Arabic
- Chinese (simplified and traditional)
- Czech
- Dutch
- English
- French
- German
- Greek
- Hebrew
- Hindi
- Indonesian
- Italian
- Japanese
- Korean
- Persian
- Polish
- Portuguese
- Romanian
- Russian
- Spanish
- Turkish
- Ukrainian
- Vietnamese
The 32B card also describes a 23-language scope. The cited specifications do not establish that performance is equal across those languages, nor do they provide an independent language-by-language quality assessment. The stated text-only scope also means the model cards do not describe image, audio, or video input.
What does the published evaluation show?
The 8B model card reports comparisons with Gemma 2 9B, Llama 3.1 8B, Ministral 8B, and Qwen 2.5 7B. It describes evaluation on the dolly_human_edited subset of the Aya Evaluation Suite and m-ArenaHard, a benchmark based on Arena-Hard-Auto and translated into the supported languages. The card says reported win rates use gpt-4o-2024-08-06 as judge and identifies that as the conservative reported judge configuration. The model card and Cohere Labs’ Aya Expanse release materials document this evaluation approach.
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These results describe performance under that particular model comparison, dataset, translation, and judge setup. They do not show that Aya Expanse is best in every supported language, independently validated by this publication, or necessarily the strongest choice for a specific application. For a meaningful selection, test representative prompts in the languages and task types that matter to you.
How can you try Aya Expanse?
Hosted exploration is the easiest place to start if you do not want to arrange local inference. The model cards point readers to the Cohere playground and a Hugging Face Space. Availability and interface details can change, so consult those pages for current access.
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For local experimentation, the 8B card links to its Hugging Face repository and loading details. Downloaded weights require suitable compute, but the reviewed official materials do not specify a minimum GPU, computer configuration, or inference cost. The 8B and 32B parameter counts alone are not enough to determine whether a particular computer can run a model comfortably; hardware needs depend on the inference setup.
Can you use Aya Expanse commercially?
The model cards identify the license as CC-BY-NC and require compliance with Cohere Labs’ Acceptable Use Policy. Because the license is noncommercial, do not assume the weights can be used for commercial purposes. Read the current license and Cohere Labs’ Acceptable Use Policy before using or distributing the models. This is practical guidance, not legal advice.
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How should you compare Aya Expanse with other multilingual models?
Parameter count and a list of supported languages are only part of the decision. Compare models on the criteria that affect your actual use:
- Language-specific quality: Test the languages, dialects, and tasks you expect to handle rather than treating a language count as a quality score.
- Compute and model size: Aya Expanse’s 8B and 32B variants differ substantially in parameter count, but the cited materials do not provide a matched hardware or latency comparison.
- Context length: The 8B card specifies an 8K context. The cited material does not establish a directly comparable context figure for the 32B variant.
- Evaluation method: Check which datasets, competing models, translations, and judge were used; results from different setups are not automatically comparable.
- Modality: The 8B card describes text input and text generation, not multimodal input or output.
- License and use rules: Check restrictions and policy requirements before building a workflow around downloaded weights.
Aya Expanse is most useful to consider as an open-weight multilingual research option whose specifications and published evaluation can guide further testing. Whether it fits a particular deployment depends on task-specific quality, available compute, and permitted use.
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