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Qwen 3 Coder 30B-A3B vs DeepSeek R1 Distill 14B for Local Coding (2026)

Head-to-head for local coding: Qwen 3 Coder 30B-A3B (MoE, 17GB Q4) vs DeepSeek R1 Distill Qwen 14B (dense, 8GB Q4). HumanEval, speed, reasoning, and which to run on 12GB/24GB GPUs.

When you have a 24 GB GPU (or Mac Studio) and want the best local coding model in early 2026, the two main options are Qwen 3 Coder 30B-A3B (MoE coding specialist from Alibaba) and DeepSeek R1 Distill Qwen 14B (reasoning-tuned distill from DeepSeek). Different architectures, different strengths. Here's the breakdown.

Quick answer

  • Best raw coding quality: Qwen 3 Coder 30B-A3B.
  • Best reasoning / debug workflow: DeepSeek R1 Distill Qwen 14B (chain-of-thought is strong).
  • Fits on 12 GB: Only DeepSeek R1 Distill 14B. Qwen 3 Coder needs 24 GB+.
  • Fastest tok/s per GB of VRAM: Qwen 3 Coder 30B-A3B (MoE benefit).
  • Agentic coding (multi-turn with tools): Qwen 3 Coder, designed for it.

Specs

SpecQwen 3 Coder 30B-A3BDeepSeek R1 Distill Qwen 14B
Total parameters30B (MoE)14B (dense)
Active parameters per token3B14B
ArchitectureMixture of ExpertsDense transformer (distilled from R1)
Context262K → extensible to 1M128K
Training focusAgentic coding + long-horizonGeneral reasoning (distilled from DeepSeek R1)
LicenseApache 2.0MIT
Best runtimellama.cpp, vLLM, MLXllama.cpp, vLLM

VRAM

QuantQwen 3 Coder 30B-A3BDeepSeek R1 Distill 14B
Q4_K_M~17 GB~8 GB
Q5_K_M~20 GB~10 GB
Q6_K~23 GB~11 GB
Q8_0~30 GB~15 GB
FP16~56 GB~28 GB

MoE note: Qwen 3 Coder has 30B total parameters but only 3B activate per token. All 30B must still reside in VRAM (routing requires access to every expert), but compute cost scales with the active 3B — hence the speed advantage.

Hardware fit

12 GB GPUs (RTX 4070, 3060 12GB, 3080)

  • Qwen 3 Coder 30B-A3B: ❌ Won't fit even at Q4.
  • DeepSeek R1 Distill 14B: ✅ Q4 fits comfortably, Q6 tight.

16 GB GPUs (RTX 4060 Ti 16GB, RTX 5080)

  • Qwen 3 Coder 30B-A3B: ⚠️ Marginal at Q4 (17 GB weights + 1-2 GB KV cache overflows).
  • DeepSeek R1 Distill 14B: ✅ Q8 fits, near-lossless.

24 GB GPUs (RTX 4090, 3090, 5080, 7900 XTX)

  • Qwen 3 Coder 30B-A3B: ✅ Q4 with ~5 GB headroom.
  • DeepSeek R1 Distill 14B: ✅ Q8 very comfortable.

32 GB GPUs (RTX 5090)

  • Qwen 3 Coder 30B-A3B: ✅ Q5 or Q6 comfortable.
  • DeepSeek R1 Distill 14B: ✅ FP16 with ~4 GB headroom.

Apple Silicon

  • Qwen 3 Coder 30B-A3B: Needs 36 GB+ unified memory. M4 Max 36GB/64GB great picks.
  • DeepSeek R1 Distill 14B: Fits on 16 GB Macs at Q4. M4 16GB tight but usable.

Real-world speed (tok/s at Q4_K_M)

GPUQwen 3 Coder 30B-A3BDeepSeek R1 Distill 14B
RTX 4070 12GB❌~35 tok/s
RTX 3090 24GB~65 tok/s~50 tok/s
RTX 4090 24GB~85 tok/s~60 tok/s
RTX 5090 32GB~135 tok/s~100 tok/s
M4 Max 36GB~40 tok/s~30 tok/s
M4 Max 64GB~48 tok/s~32 tok/s

Qwen 3 Coder's MoE lets it beat DeepSeek R1 Distill 14B by 30-40% in throughput while being a more capable model on average. Sonewhat counter-intuitive but consistent with the MoE story.

Quality benchmarks (coding-focused)

BenchmarkQwen 3 Coder 30B-A3BDeepSeek R1 Distill 14B
HumanEval (Python Pass@1)~90~80
MBPP (basic Python)~85~78
LiveCodeBench (contest)~55~45
SWE-Bench Verified~25~18
MMLU (general knowledge)~78~75
MATH (hard math)~70~85 ← DeepSeek wins
GPQA Diamond (PhD reasoning)~50~60 ← DeepSeek wins
AIME 2024 (competition math)~55~75 ← DeepSeek wins

Takeaway: Qwen 3 Coder is stronger on pure coding. DeepSeek R1 Distill is stronger on math-heavy reasoning (inherited from R1's reasoning training). For a coding-first workflow → Qwen. For science / math / multi-step proofs → DeepSeek.

When reasoning matters more than coding

DeepSeek R1 Distill Qwen 14B is a reasoning model: it was distilled from DeepSeek R1, which was trained to produce long chain-of-thought before answering. For:

  • Debugging tricky concurrency bugs
  • Understanding unfamiliar codebases
  • Math-heavy scientific code
  • Multi-step algorithm design

It beats Qwen 3 Coder meaningfully because it explicitly reasons through problems in its output.

For straight code-completion tasks (function bodies, test generation, refactors) Qwen 3 Coder wins on both quality and speed.

Agentic coding

If you use tools like Aider, Cline, Continue.dev, or custom agent scaffolds that chain tool calls with the model:

  • Qwen 3 Coder was specifically trained for this — long horizons, tool calling, multi-file context. Expect smooth performance.
  • DeepSeek R1 Distill 14B works but you may see the model "think out loud" inside tool call payloads, which can confuse some agent frameworks. Mitigate with a stop sequence on <tool_call> or use the R1-distill tool-use variant.

Setup commands

Qwen 3 Coder 30B-A3B

# Ollama
ollama run qwen3-coder:30b-a3b

# llama.cpp
huggingface-cli download unsloth/Qwen3-Coder-30B-A3B-GGUF \
  Qwen3-Coder-30B-A3B-Q4_K_M.gguf --local-dir models/
./llama-cli -m models/Qwen3-Coder-30B-A3B-Q4_K_M.gguf -n 512 -cnv

# MLX on Mac
mlx_lm.generate --model mlx-community/Qwen3-Coder-30B-A3B-MLX-4bit \
  --prompt "Implement a thread-safe LRU cache in Rust."

DeepSeek R1 Distill Qwen 14B

# Ollama
ollama run deepseek-r1:14b

# llama.cpp with R1 chain-of-thought thinking tags
./llama-cli -m models/DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf \
  -n 2048 -cnv --reverse-prompt "</think>"

Note for DeepSeek: enable a longer max_tokens (~2048+) and be prepared for <think>...</think> preamble in outputs — the model reasons before answering.

Bottom line

PriorityPick
Best local coding model (with 24 GB+)Qwen 3 Coder 30B-A3B
Best reasoning / math model (with 12 GB)DeepSeek R1 Distill 14B
Tight VRAM budgetDeepSeek R1 Distill 14B
Fastest tokens/secQwen 3 Coder 30B-A3B (MoE)
Long-codebase contextQwen 3 Coder 30B-A3B (262K→1M)
Multi-step algorithm designDeepSeek R1 Distill 14B (R1 reasoning)
Agentic workflows with toolsQwen 3 Coder 30B-A3B
Mac 16GBDeepSeek R1 Distill 14B
Mac Studio 36GB+Qwen 3 Coder 30B-A3B

If you have a 24 GB GPU and primarily code: run both, they complement each other. Qwen 3 Coder for day-to-day code completion + agent loops. DeepSeek R1 Distill for debugging / algorithm design / math-heavy problems.

Related guides

Frequently Asked Questions

Qwen 3 Coder 30B-A3B vs DeepSeek R1 Distill 14B — which is better for coding?

Qwen 3 Coder 30B-A3B is stronger on raw coding benchmarks (HumanEval ~90% vs ~80% for R1 Distill 14B) and much faster per token because only 3B parameters activate. DeepSeek R1 Distill 14B wins on multi-step reasoning / debug tasks where chain-of-thought matters.

VRAM difference?

Qwen 3 Coder 30B-A3B needs ~17 GB at Q4_K_M (MoE, 30B total / 3B active). DeepSeek R1 Distill 14B needs ~8 GB at Q4_K_M (dense 14B). Roughly double the VRAM for Qwen 3 Coder, but you get a 2x+ quality bump on coding benchmarks.

Which runs faster?

On RTX 4090, Qwen 3 Coder 30B-A3B runs at ~85 tok/s despite being 2× the params — because only 3B activate per token. DeepSeek R1 Distill 14B runs at ~60 tok/s (dense, all 14B active). Counter-intuitively, the bigger MoE model is faster per token than the smaller dense model.

Can I run either on 12 GB VRAM?

Only DeepSeek R1 Distill 14B. At Q4_K_M it needs ~8 GB, fits comfortably on an RTX 3060 12GB or RTX 4070 12GB. Qwen 3 Coder 30B-A3B needs 24 GB+ for Q4 — try Qwen 3.5 9B or a Qwen 2.5 Coder variant if your GPU is smaller.

Which is better for agentic / tool-use coding?

Qwen 3 Coder was specifically trained for agentic coding with tool use and long horizons. DeepSeek R1 Distill 14B is general reasoning — it handles tool-use prompts but isn't as naturally tuned for multi-turn coding agents.

Which has longer context?

Qwen 3 Coder 30B-A3B natively supports 262K tokens, extensible to ~1M. DeepSeek R1 Distill Qwen 14B supports 128K. For full-codebase analysis, Qwen 3 Coder wins hands-down.