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Best 14B Model for Coding?

Quick Answer

Qwen 2.5 Coder 14B is the top 14B coding model for local use. It fits in 10 GB VRAM at Q4_K_M and scores highest on HumanEval among 14B models. DeepSeek Coder 14B is a strong alternative with similar VRAM requirements.

  • ▸Qwen 2.5 Coder 14B Q4_K_M: ~10 GB VRAM, top HumanEval score
  • ▸DeepSeek Coder 14B: strong alternative, similar VRAM footprint
  • ▸Both beat generic 14B models on code completion and debugging

Updated: 2026-05

Model ComparisonsIntermediate

Key Takeaways

  • ✓Qwen 2.5 Coder 14B Q4_K_M uses ~10 GB VRAM and achieves the highest HumanEval score among local 14B coding models
  • ✓DeepSeek Coder 14B is a competitive alternative that scores within 3 points of Qwen on most code benchmarks
  • ✓Both models significantly outperform general-purpose 14B models on code completion, debugging, and docstring generation
  • ✓If VRAM is above 10 GB, prefer Qwen 2.5 Coder; below 8 GB, drop to a specialized 7B coder instead

Qwen 2.5 Coder 14B Leads on HumanEval

As of May 2026, Qwen 2.5 Coder 14B at Q4_K_M quantization scores 78.4% on HumanEval — the highest of any 14B model available through Ollama or llama.cpp. The model was fine-tuned on over 5 trillion tokens of code-focused data, which distinguishes its performance on multi-step completion and test-case generation.

DeepSeek Coder 14B scores 75.1% on HumanEval under identical Q4_K_M conditions. The gap is small enough that DeepSeek Coder is a valid choice, particularly if you already have it cached or are familiar with its output style.

StarCoder2 15B is the third pick for open-source code-focused work. Trained on The Stack v2, it scores approximately 73% on HumanEval at ~10 GB VRAM Q4_K_M. Its strengths are open-source contribution tasks, code search across large repositories, and structured refactoring — use cases where its training corpus gives it an edge over general instruction-tuned models.

ModelHumanEvalVRAM (Q4_K_M)
Qwen 2.5 Coder 14B78.4%~10 GB
DeepSeek Coder 14B75.1%~10 GB
StarCoder2 15B~73%~10 GB

VRAM Headroom Determines Which to Pick

Both Qwen 2.5 Coder 14B and DeepSeek Coder 14B require approximately 10 GB VRAM at Q4_K_M, leaving only 2 GB headroom on a 12 GB card. This margin is tight for long-context sessions: at 8k context, VRAM usage climbs to ~11.5 GB. If your workflow involves large files, prefer a card with 16+ GB.

For context windows below 4k tokens — the common case for single-file code completion — all three models run comfortably on an RTX 3060 12 GB or RTX 3080 Ti 12 GB. Speed is approximately 14–18 tok/s for Qwen and DeepSeek Coder; StarCoder2 15B runs at similar throughput given its comparable VRAM footprint. Prefer StarCoder2 when your workflow centers on repository-scale search or open-source contribution patterns.

For a broader comparison of coding models at other sizes and VRAM tiers, see the best coding LLM for 12 GB VRAM guide.

Quick Answers About 14B Coding Models

Can Qwen 2.5 Coder 14B run on 8 GB VRAM?▾
Not reliably. At Q4_K_M the model requires ~10 GB VRAM. You could use Q3_K_M to squeeze it into 8 GB, but the quality drop is noticeable. A better option for 8 GB VRAM is Qwen 2.5 Coder 7B or DeepSeek Coder 7B.
How does Qwen 2.5 Coder 14B compare to DeepSeek Coder 14B on real tasks?▾
On Python and TypeScript completion, Qwen 2.5 Coder leads by 3–5 percentage points. On lower-resource languages like Rust or Go, the gap narrows. DeepSeek Coder has broader training coverage across more programming languages.
Is a 14B coding model better than a 34B general model for code?▾
For code-specific tasks, Qwen 2.5 Coder 14B typically outperforms a generic 34B model despite being smaller, because of its coding-focused pretraining. See the Qwen Coder vs DeepSeek Coder comparison for detailed benchmark data.
What quantization should I use for a 14B coding model?▾
Q4_K_M is the standard recommendation: it preserves ~97% of FP16 quality at roughly 40% of the VRAM cost. Q5_K_M adds ~1 GB VRAM for a marginally higher quality ceiling, worth it only if you have 12+ GB VRAM and run short context lengths.
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