Code Llama (local via Ollama) is Meta's original code model, still shipped by Ollama at 7B, 13B, 34B, and 70B. It is a fill-in-the-middle specialist and a useful baseline, free to self-host, but the Qwen and Devstral lines have overtaken it. Inside Atlas, Code Llama's 16,384 token context is far too small for agentic work, and it predates the agentic tool-calling era, so it does not reliably produce well-formed tool calls.
What is Code Llama actually good at in 2026?
Code Llama is good at fill-in-the-middle infilling, the completion behavior it was explicitly trained for by Meta. Code Llama ships in four sizes (7b, 13b, 34b, and 70b) under one Ollama tag, plus dedicated instruct and python-tuned variants, all free to self-host.
Fill-in-the-middle is a real capability and Code Llama remains competent at it. Given a prefix and a suffix, Code Llama produces the span between them, which is the classic inline-completion shape rather than the agent shape. If your use for a local model is filling gaps in code you are already writing, Code Llama at `ollama pull codellama:13b` still does that job. What it is not is a model that will drive Atlas through a plan, edit, and approval loop.
Why is a 16K context window too small for Atlas?
Code Llama's 16,384 token context is far too small for agentic work: Atlas's codebase-search results alone can exceed it. Because Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, a single retrieval pass can fill a 16K budget before the agent has done anything.
Context is the hard wall with Code Llama. Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so retrieval returns dense, complete declarations, and enough of them will not fit in 16,384 tokens. Add a plan, a tool-call history, and a diff, and the budget is gone. Register Code Llama in the ollama provider block in atlas.json with "limit": { "context": 16384 } so Atlas at least trims deliberately, and accept that this ceiling rules out multi-file work entirely.
Does Code Llama work with Atlas tool calling?
Code Llama does not reliably produce well-formed tool calls for Atlas, because Code Llama predates the agentic tool-calling era. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, and malformed calls surface as rejected turns rather than progress. The 16,384 token context compounds the problem.
The training-era problem is more fundamental than the context problem, because no configuration fixes it. Code Llama was built when a code model's job was to continue text, not to emit a structured call to an edit tool and wait for approval. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, and a model that cannot express the edit as a proper call never gets that far. Do not expect Code Llama to survive an Atlas build session.
Why does Code Llama still have mature tooling support?
Code Llama has mature quantizations and broad tooling support after years in the ecosystem, which is a genuine and underrated advantage. Four sizes, 7b, 13b, 34b, and 70b, are all well quantized, so Code Llama runs predictably on hardware where a newer model's quant might misbehave.
Ecosystem maturity is why Code Llama remains a good baseline. When a new local model behaves strangely, running the same prompt through Code Llama tells you whether the problem is the model or your setup. The instruct and python-tuned variants add further baselines. Pull `ollama pull codellama:13b` (or 7b, 34b, 70b), register it, and select it from /models. Use it as a control, not as the model you ship work with.
When should you pick a different model than Code Llama?
Pick a different model than Code Llama for any real Atlas agent work. The documented guidance is direct: treat Code Llama as a completion baseline, and for real Atlas agent work prefer `ollama pull devstral` or `ollama pull qwen3-coder:30b` instead.
Code Llama is a fill-in-the-middle specialist and a useful baseline, but the Qwen and Devstral lines have overtaken it, and the gap is not close on agentic work. Devstral was trained agent-first, and Qwen3-Coder 30B brings a far larger window. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keeping Code Llama configured alongside a real agent model costs nothing. Just do not put Code Llama in the seat that writes the diffs.
Setup
- 01Run `ollama pull codellama:13b` (or 7b, 34b, 70b depending on your hardware).
- 02Register it in the ollama provider block in atlas.json with "limit": { "context": 16384 }.
- 03Select Code Llama from /models in the Atlas TUI.
- 04Treat it as a completion baseline: for real Atlas agent work, prefer `ollama pull devstral` or `ollama pull qwen3-coder:30b`.
- 05Keep the stronger agent model in the main slot and use Code Llama only for infilling comparisons.
Frequently asked questions
- is code llama still worth using in 2026
- Code Llama is still a useful fill-in-the-middle baseline with mature quantizations, but the Qwen and Devstral lines have overtaken it. For real Atlas agent work, prefer `ollama pull devstral` or `ollama pull qwen3-coder:30b`.
- what is the context window of code llama
- Code Llama has a 16,384 token context window. That is far too small for agentic work, since Atlas's codebase-search results alone can exceed it.
- what sizes does code llama come in on ollama
- Code Llama ships at 7b, 13b, 34b, and 70b under one Ollama tag, plus dedicated instruct and python-tuned variants. Pull one with `ollama pull codellama:13b`.
- why does code llama fail at tool calling
- Code Llama predates the agentic tool-calling era, so it does not reliably produce well-formed tool calls for Atlas's permission gate. Malformed calls surface as rejected turns rather than progress.
- code llama vs qwen3-coder for local coding
- Qwen3-Coder 30B is the better choice for Atlas. Code Llama's 16,384 token context and pre-agentic training make it unsuitable as a build-agent model, while it remains competent at fill-in-the-middle infilling.
- how do i add code llama to atlas.json
- Run `ollama pull codellama:13b`, then register it in the ollama provider block in atlas.json with "limit": { "context": 16384 }, and select it from /models.
- what is fill in the middle infilling
- Fill-in-the-middle is the completion behavior Code Llama was explicitly trained for: given a prefix and a suffix, the model produces the span between them. It is a completion pattern, not an agent pattern.
Try Atlas in your terminal
The terminal-native AI coding agent. Free core, single binary.
Install AtlasRelated guides
Run the Test Suite and Triage the Failures with Atlas in 2026
How to triage a failing test suite with Atlas in 2026: bash truncates at 2000 lines or 50 KB and saves the full log, then grep groups failures by root cause.
Atlas for Three.js: Terminal-Native AI Coding for Scenes, Materials, and Disposal in 2026
Atlas is a terminal-native AI coding agent for Three.js in 2026, where geometry, material, and texture disposal is the difference between a demo and a leak.
Atlas vs Windsurf: Terminal AI Coding Agents in 2026
Atlas and Windsurf offer distinct AI coding experiences in 2026. Atlas provides a terminal-native TUI with permission-gated tools, while Windsurf features an in-editor live preview and a dedicated GUI.
Review a Pull Request with Atlas (2026 Workflow)
How to review a pull request with Atlas in 2026: bash produces the raw patch, read pulls whole files, the lsp tool's findReferences checks callers the diff never shows.
Atlas for F#: A Terminal-Native AI Coding Agent for .fsproj Solutions in 2026
Atlas is a terminal-native AI coding agent for F# in 2026. It respects .fsproj file order, maps discriminated unions, runs dotnet test behind a prompt, and runs Fantomas.
Atlas for Ruby on Rails in 2026
Atlas is a terminal-native AI coding agent for Ruby on Rails in 2026. Run it in a Rails app with a config/application.rb and review every diff before it lands.
Extract a Shared Helper from Duplicated Code with Atlas (2026 Workflow)
How to extract a shared helper from duplicated code with Atlas in 2026: codebase_search finds the copies by meaning, write creates the module, apply_patch swaps each call.
Atlas vs CodeGPT in 2026: A Developer's Guide to Terminal and IDE AI Agents
Comparing Atlas and CodeGPT in 2026 for developers. Atlas offers terminal-native TUI and permission-gated tools, while CodeGPT provides IDE integration and a full repo Knowledge Graph.