whatmodel.sh / Yi-Coder 9B Chat

Yi-Coder 9B Chat

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Yi-Coder 9B Chat is an open-weights language model from 01.AI, released August 2024. Its published size is 8.83B parameters and a 128k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo 01-ai/Yi-Coder-9B-Chat @356a1f8: licence tag apache-2.0. 8,829,407,232 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2024-08: month the maker's Hugging Face repo was created (createdAt 2024-08-21). Ollama tags yi-coder:9b-chat-q4_K_M, yi-coder:9b-chat-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

48.6±1.4#370 of 719 LLMsfrom 8 benchmark results across 5 sources

$ how it compares

The LLMs ranked either side of Yi-Coder 9B Chat, out of 828 with a score, then more from 01.AI.

#	model	score	where	RAM	released
441	AceReason-Nemotron-1.1-7B NVIDIA	49±2	local	~6GB	2025-06
442	Mixtral 8x22B Instruct v0.1 Mistral	49±1	local	~56GB	2024-04
443	Yi-Coder 9B Chat 01.AI	49±1	local	~6.8GB	2024-08
444	Qwen2-Math 7B Instruct Alibaba	49±2	local	~6GB	2024-08
445	Magicoder-S-DS 6.7B UIUC (ise-uiuc)	49±1	local	~5.8GB	2023-12
	more from 01.AI
308	Yi-Lightning 01.AI	55±1	api price unknown		2024-10
373	Yi-Large Preview 01.AI	52±1	comparison only		2024-05
396	Yi-Large 01.AI	51±1	comparison only		2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #286 of 458 · 83% of its score · 6 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Yi 1.5 9B Chat here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
LiveCodeBench official (Jan 1–Nov 1, 2024)	23.5% ✎	▁▁·▁▂▄▆█▅▂·▂ beats 8/31	hard · avg 26%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%	sharp
Aider code editing (133 Exercism Python exercises, two attempts)	54.1% ✎	▁·▁▂▄▁▆▄█▆▂▂ beats 20/62	medium · avg 54%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%	at its level
CodeReviewQA: change localisation (hard), invariant accuracy	3.9% ✎	█▂▁▁▃▂▂▃▅▅▅▇ beats 13/47	medium · avg 36%	▇▇ 5.4%	sharp · at its level · noisy
CodeReviewQA: solution identification (hard), invariant accuracy	33.0% ✎	▃··▁▂▂▃▃█▃▄▂ beats 18/47	medium · avg 54%	▇▇ 5.0%	sharp · at its level · noisy
CodeReviewQA: automated code refinement, exact match rate	32.4% ✎	▁▁▁▁▁▁▂▂▃█▅▄ beats 27/47	hard · avg 33%	▇ 3.7%	at its level
CodeReviewQA: change type recognition, invariant accuracy	52.1% ✎	▁▁▁······▃▃█ beats 6/47	easy · avg 67%	▇ 0.1%	blunt · at its level · noisy

other tests2 not in a task · 16% of its score

test	result	vs the others in its run	difficulty	counts	why
EvalPerf correctness (118 tasks, 20240328)	78.8% ✎	▂·▂▂▂▆▅▆▆▆▃█ beats 15/29	easy · avg 76%	▇▇▇▇▇ 12%	at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)	61.9% ✎	▃▁▁▁▁▁▁▁▃█▅▁ beats 94/276	medium · avg 73%	▇▇ 3.9%	at its level · noisy

$ 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 | sh
Windows
irm https://llama.app/install.ps1 | iex
other ways to install ↗
Run Yi-Coder 9B Chat · needs ~5.3 GB · from huggingface.co/lmstudio-community
chat in the terminal
llama cli -hf lmstudio-community/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf lmstudio-community/Yi-Coder-9B-Chat-GGUF:Q4_K_M

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run Yi-Coder 9B Chat · needs ~5.3 GB · from huggingface.co/lmstudio-community
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/lmstudio-community/Yi-Coder-9B-Chat-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) ↗
Linux
curl -fsSL https://ollama.com/install.sh | sh
Run Yi-Coder 9B Chat · needs ~5.8 GB · from ollama.com
chat in the terminal
ollama run yi-coder:9b-chat-q4_K_M

MLX (Mac)

version

Install MLX (Mac) · Apple silicon Macs only, text only.
with uv
uv tool install mlx-lm
or
pip install mlx-lm
Run Yi-Coder 9B Chat · needs ~5 GB, Mac only · from huggingface.co/mlx-community
chat in the terminal
mlx_lm.chat --model mlx-community/Yi-Coder-9B-Chat-4bit
Or start an OpenAI-compatible server at http://localhost:8080/v1
mlx_lm.server --model mlx-community/Yi-Coder-9B-Chat-4bit

Official weights and documentation: huggingface.co/01-ai ↗

$ how the score was worked out · LiveCodeBench official websit… 35%, Aider code editing benchmark… 34%, CodeReviewQA: The Code Review… 14% and 2 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	licence
LiveCodeBench official website results (2024-12-26)	1	▇▇▇▇▇▇▇▇▇▇▇ 35%	CC-BY-SA-4.0
Aider code editing benchmark runs (old edit leaderboard)	1	▇▇▇▇▇▇▇▇▇▇ 34%	Apache-2.0
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models (arXiv:2503.16167v2)	4	▇▇▇▇ 14%	CC-BY-4.0
EvalPlus / EvalPerf published evaluation runs	1	▇▇▇▇ 12%	Apache-2.0
Open Portuguese LLM Leaderboard (archived)	1	▇ 4%	Apache-2.0
prior · pull toward the middle		▇ 1%
weighted average		= 48.6

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

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 2026-10-11 02:20 UTC · sources and licences · json