[whatmodel.sh](../../README.md) / Aya Expanse 32B
# Aya Expanse 32B
Cohere · released 2024-10 · 32.3B params · cc-by-nc-4.0
**score 52.5** ±1.6 · #329 of 732 · from 10 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Aya Expanse 32B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #263 of 402* 50% 2
chat & writing · #263 of 402 · 50% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1267.3 ▁▁▁▂▃▃●▆▆▇█▃ beats 101/385 avg 1260 28% blunt
Arabic IFEval prompt-level (Incep… 56.7% ▁·▁▁▂▁▄▄▆▄●▄ beats 31/38 medium · avg 46% 23% sharp · at its level
other tests (8)
test result vs the others in its run difficulty counts why
KCL-Essay vanilla 20.2% ▁···▁●▂█▂▃▃▂ beats 2/23 hard · avg 17% 14% typical
Open Persian LLM Leaderboard v2 a… 62.9% ▂▄▂▃█▄▃●▃▅▂▂ beats 59/81 medium · avg 57% 13% blunt · at its level
KCL-MCQA vanilla 28.4% ▂···●▂▆▆██▆▂ beats 2/23 very hard · avg 29% 6.3% typical
Open PT-LLM Leaderboard average (… 73.0% ▃▁▁▁▁▁▁▁▂●▅▁ beats 201/276 medium · avg 72% 6.1% at its level · noisy
EuroEval HellaSwag (English), tes… 88.1% ▃·▁▁▂▁▂▅█▇●▁ beats 88/100 medium · avg 67% 5.1% sharp · at its level · noisy
EuroEval Life in the UK (English … 86.7% ▁▁▁▁▁▁▁▂▄●▄▃ beats 104/146 easy · avg 82% 2.9% noisy
Vectara hallucination leaderboard… 91.5% ▁▁▂▃●▄█▆▃▂▂▁ beats 31/140 very easy · avg 94% 1.6% blunt · maxed out · noisy
Vectara hallucination leaderboard… 89.1% ▁▂▁▂▅●▂▃▄▃▁▁ beats 32/98 very easy · avg 90% 0.0% blunt
source results share of the score
LMArena leaderboard dataset 1 28% ▇▇▇
Inception Arabic/Hindi leaderboards 1 23% ▇▇▇
Korean Canonical Legal Benchmark: Towar… 2 20% ▇▇
Open Persian LLM Leaderboard v2 1 13% ▇▇
EuroEval leaderboards 2 8% ▇
Open Portuguese LLM Leaderboard 1 6% ▇
Vectara hallucination leaderboard 2 2% ▇
prior: pull toward the middle 1% ▇
weighted average = 52.5
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs’ Legal Reasoning Capabilities ([CC BY 4.0](../../licenses/cards-final-NOTICE.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (Cohere) · $0.75/M blended · $0.5 in / $1.5 out per 1M tokens · [docs.cohere.com](https://docs.cohere.com/docs/models)
[price source](https://cohere.com/pricing): USD per 1M tokens, Cohere API: the pricing page FAQ states 'Aya Expanse models (8B and 32B) on the API are charged at $0.50/1M tokens for input and $1.50/1M tokens for output'. Cohere's models page lists c4ai-aya-expanse-32b on the Chat endpoint. Trial-key free usage excluded. Currency: USD. Cohere official pricing page, observed 2026-10-07. · [context source](https://huggingface.co/CohereLabs/aya-expanse-32b/blob/b306ea27e360683b50c005d7fcbad6a242317910/README.md): Context length stated on the official model card.
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Aya Expanse 32B (Q4_K_M, needs ~19.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/aya-expanse-32b-GGUF/tree/ed1c028722d48e88385b421c2649a18495b77d07))
```sh
llama cli -hf lmstudio-community/aya-expanse-32b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/aya-expanse-32b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.3 GB) `llama cli -hf lmstudio-community/aya-expanse-32b-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Aya Expanse 32B (Q4_K_M, needs ~19.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/aya-expanse-32b-GGUF/tree/ed1c028722d48e88385b421c2649a18495b77d07))
```sh
lms get https://huggingface.co/lmstudio-community/aya-expanse-32b-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~34.3 GB) `lms get https://huggingface.co/lmstudio-community/aya-expanse-32b-GGUF@Q8_0 -y` · 8bit (~34.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/aya-expanse-32b-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Aya Expanse 32B (Q4_K_M, needs ~19.88 GB, from [ollama.com](https://ollama.com/library/aya-expanse/tags))
```sh
ollama run aya-expanse:32b-q4_K_M
```
Other versions: Q8_0 (~39.26 GB) `ollama run aya-expanse:32b-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Aya Expanse 32B (8bit, needs ~34.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/aya-expanse-32b-8bit/tree/3f42e199f54b88cb749dc0810d32902102f4a2e7))
```sh
mlx_lm.chat --model mlx-community/aya-expanse-32b-8bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/aya-expanse-32b-8bit
```
Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/aya-expanse-32b)
[interactive version](https://whatmodel.sh/model/aya-expanse-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)