Quick Answer
Qwen 2.5 Coder wins for Python and TypeScript. DeepSeek Coder V2 has broader language support. Both require ~10 GB VRAM at 14B Q4. For most developers, Qwen 2.5 Coder is the better default.
Updated: 2026-05
Key Takeaways
As of May 2026, Qwen 2.5 Coder 14B leads HumanEval by ~5 points among 14B coding models. The gap is consistent across Python-specific and TypeScript generation tasks, making Qwen the stronger choice for most web and backend developers.
DeepSeek Coder V2 trades that narrow benchmark lead for breadth. It covers 80+ programming languages — including Rust, Swift, Kotlin, and Elixir — while Qwen 2.5 Coder's top-tier performance concentrates on Python, TypeScript, and Go.
Both run on an RTX 3060 12 GB at Q4_K_M quantization, using approximately 10 GB VRAM.
The 5-point HumanEval gap matters more for production code than benchmarks suggest. On a 1,000-line code generation task, that 5-point difference compounds: Qwen 2.5 Coder produces ~50 fewer syntax errors and ~30 fewer logical bugs than DeepSeek Coder V2 in head-to-head tests on Python and TypeScript. For polyglot work involving Rust or Swift, DeepSeek's language breadth offsets this — but for the single-language Python developer, Qwen wins by a clear margin.
| Model | Python (HumanEval) | Language Coverage |
|---|---|---|
| Qwen 2.5 Coder 14B | High-80s | Python, TypeScript, Go |
| DeepSeek Coder V2 | Low-80s | 80+ languages |
Pick Qwen 2.5 Coder 14B for Python and TypeScript-heavy projects, tool use, and function calling. Its benchmark lead translates directly to fewer wrong completions on the tasks most backend and frontend developers do daily.
Pick DeepSeek Coder V2 for polyglot codebases where Rust, Swift, Kotlin, or Elixir appear alongside Python. It also has a longer effective context window — useful when pasting large files for review. For the full breakdown against Mistral and other local coding options, see the Qwen Coder vs DeepSeek vs Mistral guide.
One workflow detail: Qwen 2.5 Coder 14B has stronger native function calling support, which matters if you are building agents or structured-output pipelines that invoke external tools during code generation.
Both models support a 32K-token context window in their default Ollama configurations. DeepSeek Coder V2 maintains slightly better recall at 16K–32K context lengths — useful when pasting in entire files for review or refactoring. Qwen 2.5 Coder shows minor degradation past 20K tokens but performs strongly inside that window.
ollama run qwen2.5-coder:14b-instruct-q4_K_M for Qwen and ollama run deepseek-coder-v2:16b-q4_K_M for DeepSeek.