whatmodel.sh / Aya Expanse 8B
Aya Expanse 8B
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Aya Expanse 8B is an open-weights language model from Cohere, released October 2024. Its published size is 8.03B parameters and a 8k-token context window. Licence: cc-by-nc-4.0.
Catalogue identity notes
Arena entry 'c4ai-aya-expanse-8b' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'c4ai-aya-expanse-8b' as 'Aya-Expanse-8B' (Cohere, CC-BY-NC-4.0), linking https://huggingface.co/CohereForAI/aya-expanse-8b. Maker repo CohereLabs/aya-expanse-8b @5062468: licence tag cc-by-nc-4.0. 8,028,033,024 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-10: month the maker's Hugging Face repo was created (createdAt 2024-10-23). Cohere deprecations: c4ai-aya-expanse-8b retired April 4th, 2026.
$ how it compares
The LLMs ranked either side of Aya Expanse 8B, out of 828 with a score, then more from Cohere.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 481 |  Gemma 4 E2B | 47±1 | api $0.050/M +local | ||
| 482 |  Magicoder-S-CL-7B | 47±1 | local | ||
| 483 | Aya Expanse 8B | 47±2 | local | ||
| 484 |  Phind CodeLlama 34B v2 | 47±1 | local | ||
| 485 |  CogVLM2 Llama3 Chinese Chat 19B | 47±7 | comparison only | ||
| more from Cohere | |||||
| 76 | North Small Translate 1.0 | 71±6 | api price unknown | ||
| 175 | Command A+ (05-2026) | 64±1 | api price unknown +local | ||
| 277 | Command A (03-2025) | 57±1 | api $4.38/M +local | ||
$ what it's good at
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #289 of 393 * · 67% of its score · 2 tests
Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1223.0 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 78/379 | 36% |
| Arabic IFEval prompt-level (Inception) | 48.5% ✎ | ▁·▁▁▂▁▄▄▆▄█▄ beats 25/38 | 30% |
other tests5 not in a task · 31% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Open Persian LLM Leaderboard v2 average (21 tasks) | 53.6% ✎ | ▂▄▂▃█▄▃▆▃▅▂▂ beats 39/81 | 16% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 70.7% ✎ | ▃▁▁▁▁▁▁▁▂█▅▁ beats 171/276 | 8.2% |
| EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy | 76.7% ✎ | ▁▁▁▁▁▁▁▂▄█▄▃ beats 55/146 | 4.7% |
| Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate) | 87.8% ✎ | ▁▁▂▃▄▄█▆▃▂▂▁ beats 16/140 | 2.6% |
| Vectara hallucination leaderboard (HHEM-2.3, factual consistency rate) | 90.5% ✎ | ▁▂▁▂▅█▂▃▄▃▁▁ beats 50/98 | 0.0% |
$ where to run it
llama.cpp · recommended
version
- Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.macOS / Linux
curl -LsSf https://llama.app/install.sh | shWindowsirm https://llama.app/install.ps1 | iex - Run Aya Expanse 8B · needs ~5.1 GB · from huggingface.co/lmstudio-communitychat in the terminal
llama cli -hf lmstudio-community/aya-expanse-8b-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf lmstudio-community/aya-expanse-8b-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Aya Expanse 8B · needs ~5.1 GB · from huggingface.co/lmstudio-community
- Or download it in the terminal
lms get https://huggingface.co/lmstudio-community/aya-expanse-8b-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
version
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run Aya Expanse 8B · needs ~5.32 GB · from ollama.comchat in the terminal
ollama run aya-expanse:8b-q4_K_M
MLX (Mac)
version
- Install MLX (Mac) · Apple silicon Macs only, text only.with uv
uv tool install mlx-lmorpip install mlx-lm - Run Aya Expanse 8B · needs ~4.5 GB, Mac only · from huggingface.co/mlx-communitychat in the terminal
mlx_lm.chat --model mlx-community/aya-expanse-8b-4bit - Or start an OpenAI-compatible server at http://localhost:8080/v1
mlx_lm.server --model mlx-community/aya-expanse-8b-4bit
Weights and documentation: huggingface.co/CohereLabs ↗
$ how the score was worked out · LMArena leaderboard dataset 36%, Inception Arabic/Hindi leader… 30%, Open Persian LLM Leaderboard… 16% and 3 more
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 LMArena leaderboard dataset | 1 | 36% |
 Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval) | 1 | 30% |
| Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab) | 1 | 16% |
| Open Portuguese LLM Leaderboard (archived) | 1 | 8% |
 EuroEval leaderboards (results collection), English generative leaderboard | 1 | 5% |
| Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository) | 2 | 3% |
| prior · pull toward the middle | 2% | |
| weighted average | = 46.6 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json