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Code Llama 70B (base)

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Code Llama 70B (base) is an open-weights language model from Meta, released January 2024. Its published size is 68.98B parameters and a 16k-token context window. Licence: llama2.

Catalogue identity notes

Maker repo codellama/CodeLlama-70b-hf @cc11b0f: licence tag llama2. 68,976,910,336 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-29). Pretrained Code Llama 70B (January 2024 release); not the Python or Instruct variant. Ollama tags codellama:70b-code-q4_K_M, codellama:70b-code-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

43.1±1.1#481 of 719 LLMsfrom 6 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Code Llama 70B (base), out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
568Yi 34B Chat 01.AI43±1local~23GB2023-11
569CogVLM2 Llama3 Chat 19B Zhipu AI (THUDM)43±3local~43GB2024-05
570Code Llama 70B (base) Meta43±1local~28GB2024-01
571Gemma 3n E2B IT Google43±2local~4.7GB2025-06
572Reka Flash 21B (2024-02-26) Reka AI43±1comparison only2024-02
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #343 of 458 · 87% of its score · 5 tests

Beats LFM2.5 2.6B, LFM2.5 8B-A1B, Qwen3-VL 2B Thinking here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), DeepSeek LLM 67B Chat, OpenChat 3.5 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval lab53.0% ✎█▃▃▅·▃▅·▃··▃ beats 8/11easy · avg 76%▇▇▇▇▇▇▇▇▇▇▇▇ 29%sharp · at its level
HumanEvalSynthesize (HumanEvalPack, 6-language average)52.8% ✎▁·▂▁▂▆▅█▄▄▁▂ beats 31/35medium · avg 64%▇▇▇▇▇▇▇▇ 19%at its level
HumanEvalExplain (HumanEvalPack, 6-language average)28.2% ✎▄▇█▃▄▇▄▁▇▃·▁ beats 24/35medium · avg 49%▇▇▇▇▇ 13%at its level
HumanEvalFix (HumanEvalPack, 6-language average)32.8% ✎▁·▁▁▄██▄█▃▅▅ beats 27/35medium · avg 56%▇▇▇▇▇ 13%sharp · at its level
MBPP lab62.4% ✎▃▃▃█··▃▅··▅▃ beats 10/11easy · avg 73%▇▇▇▇▇ 13%at its level
other tests1 not in a task · 12% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Complete)44.3% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 41/58medium · avg 50%▇▇▇▇▇ 12%at its level

$ where to run it

llama.cpp · recommended

version

  1. 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 ↗

  2. Run Code Llama 70B (base) · needs ~41.4 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/CodeLlama-70B-hf-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/CodeLlama-70B-hf-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Code Llama 70B (base) · needs ~41.4 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/CodeLlama-70B-hf-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Code Llama 70B (base) · needs ~41 GB · from ollama.com
    chat in the terminal
    ollama run codellama:70b-code-q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Code Llama 70B (base) · needs ~39.2 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/CodeLlama-70b-hf-4bit-MLX
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/CodeLlama-70b-hf-4bit-MLX

Weights and documentation: huggingface.co/codellama ↗

$ how the score was worked out · Granite Code Models: A Family… 46%, Code Llama: Open Foundation M… 41%, BigCodeBench paper (Zhuo et a… 12%

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

sourceresultsshare of the scorelicence
Granite Code Models: A Family of Open Foundation Models for Code Intelligence — authors' own runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 46%CC BY 4.0
Code Llama: Open Foundation Models for Code — authors' own runs · lab-reported2▇▇▇▇▇▇▇▇▇▇▇▇ 41%CC BY 4.0
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v41▇▇▇▇ 12%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 43.1

$ improve this page

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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