Updated · Sources & corrections
In this guide
Qwen2.5-Coder and Llama 3.1 target overlapping but different workloads. This comparison covers those named releases, not the newest models available today. The useful question is which exact checkpoint meets your coding, language, memory and licensing requirements.
Code-focused and general-purpose checkpoints
The Qwen release describes a code-focused family with multiple sizes. Meta’s linked model card describes the Llama 3.1 instruction-tuned models. Choose a base or instruction-tuned checkpoint deliberately; results for one are not interchangeable with results for the other.
What the published evaluations establish
The linked model cards and release reports contain vendor evaluations. Read their prompting, decoding and benchmark settings before comparing scores. An unquantised checkpoint evaluated by its developer is not the same test as a downloaded Q4 conversion running in an editor.
No independent HumanEval score is published here. Earlier approximate score comparisons have been removed because the page did not provide a matched evaluation configuration. For a local decision, use private tasks with tests and inspect the generated changes rather than relying on a single public leaderboard.
Model memory and context
A four-bit weights-only estimate starts at roughly half a byte per parameter, before format overhead, runtime buffers and context cache. Actual memory can be higher and depends on the conversion and context. This arithmetic is a sizing estimate, not a measured GPU compatibility table.
Choose a model that leaves room for the operating system and application. Inspect loading and inference separately. A successful download does not prove that a long-context request will fit on the GPU.
A small evaluation you can reproduce
- Record the full model tag or revision and quantisation.
- Use the same runtime, context, sampling settings and hardware.
- Include completion, debugging and repository-edit tasks from your own workflow.
- Keep test results, failures, elapsed time and manual corrections.
- Repeat runs before claiming a quality or speed difference.
Licensing and editor integration
Read the licence attached to the exact checkpoint and its upstream base model. Availability of weights is not a blanket open-source or commercial-use guarantee. For autocomplete, verify both checkpoint support and the editor’s prompting/adapter configuration.
For installation and diagnostics, see the Ollama setup guide and runtime troubleshooting.
Sources and verification scope
This article distinguishes documented features and vendor-reported results from independent testing. No original head-to-head benchmark dataset is published for this page; hardware-memory figures retained in the guide are estimates, not measured guarantees.
Editorial update, 21 October 2026: source references and verification scope added; identified command, feature or wording errors corrected. Read our sourcing and correction policy.