CodeLlama
Meta's 2023 open-weight code models, now archived but still usable.
Overview
Code Llama is Meta's 2023 family of open-weight code models built on Llama 2, offered as base, Python-tuned, and Instruct variants from 7B to 70B parameters. Meta retired the Llama brand in April 2026 and archived the GitHub repo; the weights remain downloadable.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 2 Sep 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with CodeLlama
- A small, permissively licensed code model to fine-tune on proprietary patterns without cloud exposure
- Local code completion for an offline development tool, from 7B through 70B checkpoints
- Fill-in-the-middle completion, supported directly by the base and Instruct variants
- Python-specialised inference from the Python-tuned checkpoint
- A stable benchmark baseline — research still compares new code models against it precisely because it never moves
- No updates and no new checkpoints: the repo was archived in mid-2025 and Meta ended the Llama brand in April 2026. Take it as a snapshot to build on, not as a maintained assistant
Code Llama is Meta's 2023 family of open-weight code models (7B–70B, plus Python and Instruct variants) built on Llama 2, with completion, infill, and up to 100K-token context. It's free to download and self-host under the Llama Community License, which made it a legitimate alternative to closed commercial coders when it shipped.
Context matters now: Meta ended the Llama brand in April 2026, replacing it with the closed-weight Muse Spark line, and the meta-llama/codellama GitHub repo was archived in mid-2025 — it's maintained as a frozen release, not an active project. The weights and license terms are unchanged and still usable, but anyone picking a coding model in 2026 should compare it against newer open releases (from Meta or elsewhere) rather than assume it's current. Good for privacy-conscious, self-hosting teams; not a ready assistant for end users.
What people say
Meta doesn't make Code Llama anymore, in a sense — the company ended the Llama brand entirely in April 2026, shipping a closed-weight replacement called Muse Spark instead. The meta-llama/codellama GitHub repo was archived back in mid-2025 and is now read-only. None of that makes the model unusable: it's still downloadable, still open-weight under the Llama Community License, and 7B through 70B checkpoints still run wherever you can host them.
It came out in August 2023 as a code-specialized build on top of Llama 2 — base, Python-tuned, and Instruct variants, with infill support and up to 100K tokens of context. For its era it was a serious option for self-hosted coding assistance. Three years on, though, it's a snapshot rather than a maintained product: no updates, no new checkpoints, and plenty of newer open models (some from Meta's own earlier Llama 4 releases, some from elsewhere) that have overtaken it on benchmarks.
Worth grabbing if you specifically need a small, well-documented, permissively licensed code model to fine-tune or embed — not the first thing to reach for if you just want a competent local coding assistant today. It's also a useful benchmark baseline: plenty of research papers still compare new code models against it precisely because it's a known, stable quantity.
Summary of public user & expert reviews, compiled by RECATOOLS.
Notable facts
- Code Llama supports code infill at 100,000 token context — long enough to fit over 2,000 lines of Python in a single context window.
- The Python-specialised variant was trained with a much higher proportion of Python code than the base model, outperforming the base on Python-specific benchmarks.
- Code Llama 70B was the first openly available model to pass 50% on the HumanEval coding benchmark, a major milestone for open-source code models.
Frequently asked questions
About this listing
This entry was compiled from publicly available data including CodeLlama's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with CodeLlama unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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