Open-Weight Code Generation Model Qwen3 Coder DeepSeek CodeLlama Official Doc

The official docs for open-weight code generation models Qwen3-Coder, DeepSeek Coder, and Code Llama are their maintainers’ GitHub repositories and model cards—not third-party benchmark roundups. If you are choosing one today, start with Qwen3-Coder for a current coding-agent-oriented family, use DeepSeek Coder when you specifically need its older completion and fill-in-the-middle models or DeepSeek-Coder-V2, and treat Code Llama as a well-documented older baseline with a custom Meta license rather than the default choice for a new build.

What are the official docs for Qwen3-Coder, DeepSeek Coder, and Code Llama?

For a model you may run locally, “official docs” should mean the publisher-controlled repository, model card, license, and deployment examples. Those documents answer questions that a leaderboard cannot: which tokenizer and chat template to use, whether a model is base or instruction-tuned, how tool calls are formatted, what its real context window is, and whether your planned commercial use is permitted.

• Qwen3-Coder: use the QwenLM/Qwen3-Coder repository as the family-level reference, then use the Qwen-published Hugging Face model card for the exact checkpoint you deploy. The 30B-A3B Instruct card includes Transformers, vLLM, SGLang, Docker, local-app, sampling, and tool-use guidance.
• DeepSeek Coder: use deepseek-ai/DeepSeek-Coder for the original 1B through 33B family. Its README describes the training mix, 16K context, supported languages, completion, and fill-in-the-blank behavior. For the successor architecture, consult deepseek-ai/DeepSeek-Coder-V2.
• Code Llama: use Meta’s meta-llama/codellama repository and MODEL_CARD.md. The model card is especially important because it identifies the three variants, supported context behavior, intended use, limitations, and custom commercial license.

Do not confuse a model repository with every downstream quantization, GGUF conversion, Ollama tag, or hosted endpoint that happens to share a name. Those can be useful distributions, but they may change precision, templates, context settings, or licensing metadata. Start at the first-party page, pin a specific revision, then choose a runtime and distribution format.

Is Qwen3-Coder open weight, and which version should I use?

Yes: Qwen publishes downloadable Qwen3-Coder checkpoints, and the Qwen3-Coder-30B-A3B-Instruct model card lists the license as Apache 2.0. That makes it comparatively straightforward to evaluate, modify your serving stack around, and use commercially—but still read the model card and the license attached to the exact checkpoint, rather than assuming every member of a family has identical terms.

The practical distinction in the current family is not simply parameter count. Qwen3-Coder-30B-A3B-Instruct has 30.5B total parameters but activates 3.3B per token through mixture-of-experts routing. It has a native 256K-token context window and was documented for coding-agent workflows and function calling. Qwen’s family repository also lists larger 480B-A35B and newer “Next” variants, including base, instruct, FP8, and GGUF releases.

For most engineers, choose an Instruct checkpoint when the input is a task in natural language: explain a failure, make a patch, add tests, or use tools in a loop. Choose a Base checkpoint only when you intend to continue training, perform completion-style inference with your own prompt format, or build a specialized post-training pipeline. The most common self-hosting mistake is using an Instruct model as raw completion without its chat template, then concluding that the model is weak.

How do I run Qwen3-Coder locally?

The official model card supplies examples for both direct Transformers inference and OpenAI-compatible serving through vLLM or SGLang. The shortest useful mental model is: the model server loads weights and exposes an HTTP API; your editor integration, script, or agent sends chat messages to that API. That separation lets you swap the client while keeping model serving local.

# Serve the published Qwen3-Coder checkpoint locally
pip install vllm
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct

# The server exposes an OpenAI-compatible endpoint on localhost:8000.

Hardware planning should begin with the artifact you actually intend to load, not the headline parameter count. BF16 checkpoints, FP8 releases, and community quantizations have radically different VRAM, RAM, latency, and quality trade-offs. Also budget for context: a model may advertise 256K tokens, but serving long repositories and many concurrent requests consumes memory through the KV cache. Validate your target workload with representative repositories, tool schemas, and output lengths before standardizing on a model.

Is DeepSeek Coder still a good choice for code generation?

DeepSeek Coder remains useful when you need its particular older model family: compact sizes from 1B to 33B, project-level code completion, and fill-in-the-blank infilling. The original official README says it was trained on 2T tokens, 87% code and 13% English-and-Chinese natural language, with a 16K window and an extra fill-in-the-blank objective. Those details make it more than a generic chat model adapted to code: its training was explicitly shaped around code completion workflows.

But “DeepSeek Coder” is not one current checkpoint. DeepSeek-Coder-V2 is a separate, newer Mixture-of-Experts family with 16B-total/2.4B-active and 236B-total/21B-active releases, plus a 128K context window. If a tutorial merely says “use DeepSeek Coder,” stop and identify the repository and model ID. The expected prompt format, memory profile, and capabilities differ enough that a vague name is operationally unsafe.

For a code-completion feature, the original DeepSeek Coder is still attractive because infilling directly expresses the editing task: preserve the prefix, generate the missing span, and respect the suffix. For an agent that must read a repository, call tools, plan a change, execute tests, and repair failures, a newer agent-oriented model family is usually a more natural starting point. That is a workload judgment, not a claim that one benchmark permanently settles the comparison.

What is Code Llama, and is it truly open source?

Code Llama is Meta’s code-specialized Llama 2-era family, released in 7B, 13B, 34B, and 70B sizes. The official model card separates it into a general base model, a Python-focused variant, and an Instruct variant. It also documents infilling support for the 7B, 13B, and 70B models, while describing up to 100K-token inference support for most variants.

It is more accurate to call Code Llama open weight than open source. Meta publishes the weights under a custom commercial license, not a standard permissive software license such as Apache 2.0. That does not make it unusable—Meta explicitly identifies commercial and research use as intended use cases—but it does mean legal review should read the actual License and Acceptable Use Policy before shipping it in a product or redistributing it.

Code Llama is still worth understanding because its model card is exemplary source material: it explains variants, expected use, training period, safety limitations, and licensing. For a new deployment, though, its status as a static model trained between January 2023 and January 2024 matters. It is better used as a compatibility target, historical baseline, or constrained deployment option than assumed to be the newest answer to every code-generation problem.

Which open-weight code model should you choose?

• Choose Qwen3-Coder when you want a current open-weight coding model intended for long-context repository work, tool calling, and coding agents; begin with an Instruct checkpoint and the publisher’s exact template.
• Choose original DeepSeek Coder when infilling and lightweight code completion are central, or when its 1B–33B size range fits a constrained local deployment.
• Choose DeepSeek-Coder-V2 when you explicitly want the newer DeepSeek coder architecture and can support its larger Mixture-of-Experts serving requirements.
• Choose Code Llama when you have a reason to use the Meta family, need its documented variants or infilling behavior, and have reviewed its custom license.

Whichever you select, evaluate the full system rather than a single prompt: repository retrieval, prompt template, tool-call parser, sandbox behavior, compiler or test feedback, output token limits, latency, and permission boundaries. A model that produces a convincing function in a chat window may still fail at an engineering task because the surrounding loop gave it stale files, malformed tools, or no way to verify the patch.

Try open-weight models in a coding workflow with Cline

Once you have found the official docs and understand the serving interface, the next problem is testing a model against a real codebase rather than isolated prompts. Cline describes itself as an open-source AI coding agent available as a VS Code extension and CLI, with a secure client-side architecture, multi-root workspaces, and an MCP marketplace. Its pricing page says the open-source offering is free for individual developers; inference is usage-based, and you can bring your own API keys or use Cline’s provider.

That is relevant here because model selection is iterative. Cline says it supports many providers, including DeepSeek, and that users can switch providers or self-host. For an engineer comparing code-generation models, that means you can keep the workflow constant while testing a locally served or provider-hosted model on real tasks—then judge the result by diffs, tests, cost, and review burden rather than by a model name alone.