Which local model is best for Continue.dev or Cline?

Qwen3-Coder 14B on RTX 4060 Ti 16 GB delivers the best balance of speed (14–18 tok/s) and quality. With an RTX 4090, use Qwen3-Coder 32B for multi-file refactoring. Both work natively via Ollama.

What is DeepSeek-V3's API price vs running Qwen locally?

DeepSeek-V3 API: $0.27/1M input tokens, $1.10/1M output tokens — at typical IDE usage, ~$8/month. Running Qwen3-Coder 32B locally costs ~$0.05/day electricity plus hardware amortization. If you own an RTX 4090, local Qwen can be competitive over 3+ years.

Home/Power Local LLM/DeepSeek vs Qwen for Local Coding 2026: Which Wins?

Overview & Reference

DeepSeek vs Qwen for Local Coding 2026: Which Wins?

Last updated: 2026-07-01·14 min read·By Hans Kuepper · Founder of PromptQuorum, multi-model AI dispatch tool · PromptQuorum

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For local coding, Qwen2.5-Coder / Qwen3-Coder 32B wins overall — it leads HumanEval at ~88.4% versus DeepSeek-Coder-V2-Lite's ~83.5%, and fits on one RTX 4090 24 GB at 10–14 tok/s. DeepSeek-Coder is the runner-up: it edges ahead on repo-level and fill-in-the-middle (FIM) autocomplete, but its top model (DeepSeek-V3, 236B MoE) needs API access or a multi-GPU server. Both beat older references CodeLlama and Llama 3, which trail on every current coding benchmark.

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Key Takeaways

Qwen2.5-Coder / Qwen3-Coder 32B leads HumanEval (~88.4% vs DeepSeek-Coder-V2-Lite ~83.5%) and is the best fully local coding LLM — fits on one RTX 4090 24 GB, excels at Rust and C++
DeepSeek-Coder is the runner-up: it edges ahead on repo-level and fill-in-the-middle autocomplete, but its top model DeepSeek-V3 (236B MoE) does not run locally on consumer hardware
CodeLlama and Llama 3 are older references that trail both Qwen and DeepSeek on every current coding benchmark
DeepSeek-R1-Distill-Qwen-32B is a local-runnable distilled version of DeepSeek-R1 reasoning — decent for algorithmic problems but slower than Qwen3-Coder at autocomplete
Budget option: Qwen3-Coder 14B on an RTX 4060 Ti 16 GB delivers 16–18 tok/s at Q4_K_M — faster than the 32B for autocomplete while losing ~3 percentage points on benchmarks
For IDE integration (Continue.dev, Cline, Cursor local mode): Qwen3-Coder works out of the box; DeepSeek-V3 requires API key configuration
Minisforum UM890 Pro + external RTX 4060 Ti 16 GB eGPU: ~$800 total, dedicated coding server running Qwen3-Coder 14B 24/7

📍 In One Sentence

Qwen2.5-Coder / Qwen3-Coder 32B is the best fully local coding LLM in 2026 and leads HumanEval; DeepSeek-Coder is the runner-up, edging ahead on repo-level and fill-in-the-middle autocomplete.

💬 In Plain Terms

If you want a coding AI that runs entirely on your machine without sending code to any cloud: use Qwen2.5-Coder / Qwen3-Coder 32B — it scores highest on the HumanEval coding test. DeepSeek-Coder is a close second and is slightly better at completing code inside an existing file (fill-in-the-middle), but its strongest model needs cloud API access.

Model Overview — What You Are Comparing

DeepSeek and Qwen approach coding assistance differently: DeepSeek optimizes for benchmark scores at scale, while Qwen optimizes for consumer hardware runability. This distinction determines which model is actually usable locally.

Model	Parameters	Architecture	Local-runnable?	Recommended use
DeepSeek-V3	236B MoE (37B active)	Mixture of Experts	No (multi-GPU server only)	Cloud API for best Python/JS
DeepSeek-R1	671B MoE (37B active)	Reasoning MoE	No (data center only)	Cloud API for complex algorithms
DeepSeek-R1-Distill-Qwen-32B	32B dense	Dense (distilled from R1)	Yes — RTX 4090 24 GB	Algorithmic reasoning, competitive programming
Qwen3-Coder 7B	7B dense	Dense	Yes — RTX 3060 12 GB	Budget autocomplete, quick completions
Qwen3-Coder 14B	14B dense	Dense	Yes — RTX 4060 Ti 16 GB	Mid-tier autocomplete, solid all-rounder
Qwen3-Coder 32B	32B dense	Dense	Yes — RTX 4090 24 GB	Best local coding LLM: refactoring, Rust, C++

Benchmark Results — HumanEval, LiveCodeBench, and SWE-bench

HumanEval measures single-function Python code generation. LiveCodeBench measures coding contest problems with 2023–2026 test cases. SWE-bench measures real GitHub issue resolution. All scores are pass@1 (single attempt).

Model	HumanEval	LiveCodeBench	SWE-bench Lite	Best at
Qwen2.5-Coder / Qwen3-Coder 32B (local)	88.4%	43.6%	42.5%	HumanEval, Rust, C++, refactoring
DeepSeek-V3 (API)	82.4%	43.8%	42.0%	Repo-level, scale
DeepSeek-Coder-V2-Lite (local)	83.5%	40.1%	39.6%	Fill-in-the-middle autocomplete
DeepSeek-R1 (API)	79.8%	47.3%	49.2%	Algorithmic reasoning
DeepSeek-R1-Distill-Qwen-32B (local)	72.6%	39.4%	36.8%	Local reasoning tasks
Qwen3-Coder 14B (local)	80.2%	33.6%	28.4%	Autocomplete, budget
Qwen3-Coder 7B (local)	68.9%	26.8%	21.2%	Ultra-budget single-line
CodeLlama 34B (local, reference)	48.8%	19.4%	14.2%	Legacy baseline only

DeepSeek-V3/R1 and Qwen2.5-Coder scores are official reported figures; Qwen2.5-Coder 32B leads HumanEval at ~88.4%. CodeLlama and Llama 3 are older references that trail current coding models on every benchmark. Local scores measured on our RTX 4090 test bench with Q4_K_M quantization via Ollama 0.7.0 on CUDA 12.4.

VRAM and Hardware Requirements

The key difference between DeepSeek and Qwen for local use is not benchmark scores — it is hardware runability. DeepSeek-V3 is a 236B MoE model. Even at INT4 quantization, it requires ~140 GB total VRAM — far beyond any consumer setup.

Model	VRAM (Q4_K_M)	Minimum GPU	Price estimate (July 2026)
Qwen3-Coder 7B	5.2 GB	RTX 3060 12 GB	$150–350 used
Qwen3-Coder 14B	9.4 GB	RTX 4060 Ti 16 GB	$424 new
Qwen3-Coder 32B / DeepSeek-R1-Distill-Qwen-32B	20.1 GB	RTX 4090 24 GB	$1,900 new (2026 surge)
DeepSeek-V3 (local)	~140 GB	6× A100 80 GB minimum	$300,000+ hardware

Buy RTX 4060 Ti 16 GB on Amazon → (runs Qwen3-Coder 14B)product link · disclosedBuy Minisforum UM890 Pro → (dedicated coding server)product link · disclosed

Inference Speed — Tokens per Second by Hardware

Speed matters more for coding autocomplete than for chat — a model generating 15 tok/s feels fast enough for document summarization but sluggish for inline code completion. Target 20+ tok/s for a good autocomplete experience.

Model	RTX 4060 Ti 16 GB	RTX 4090 24 GB	A100 40 GB (cloud)	Usable for autocomplete?
Qwen3-Coder 7B (Q4_K_M)	28–35 tok/s	45–55 tok/s	80–100 tok/s	Yes — excellent
Qwen3-Coder 14B (Q4_K_M)	14–18 tok/s	25–32 tok/s	50–65 tok/s	Acceptable on RTX 4060 Ti, excellent on 4090
Qwen3-Coder 32B (Q4_K_M)	OOM	10–14 tok/s	22–30 tok/s	Marginal on 4090, good on cloud
DeepSeek-R1-Distill-Qwen-32B (Q4_K_M)	OOM	8–12 tok/s	18–25 tok/s	Slow for autocomplete; better for file-level generation
DeepSeek-V3 (API)	N/A	N/A	~40–60 tok/s (API)	Yes, but requires internet

Winner by Programming Language

No single model wins every language. Testing with real coding tasks (not synthetic benchmarks) reveals consistent patterns across language types.

Python: DeepSeek-V3 (API) wins for library-heavy tasks (NumPy, pandas, FastAPI). Qwen3-Coder 32B is the local winner — generates syntactically correct Python 87% of the time on first attempt versus Qwen 14B at 79%. Qwen models are particularly strong with type annotations.
JavaScript / TypeScript: DeepSeek-V3 generates cleaner modern JS (ES2024 patterns, proper async/await chaining). Qwen3-Coder 32B is the local winner and matches DeepSeek-V3 on TypeScript interface generation — the gap is smaller than in Python.
Rust: Qwen3-Coder 32B wins decisively locally. It generates correct borrow-checker-compliant code significantly more often than DeepSeek-R1-Distill-Qwen-32B (which was not specifically trained on Rust). Neither DeepSeek local variant handles Rust lifetimes as consistently as Qwen-Coder.
C++ (modern, C++20): Qwen3-Coder 32B wins for modern C++20 features — concepts, ranges, coroutines. DeepSeek-V3 via API is competitive but Qwen3-Coder shows better understanding of RAII patterns and template metaprogramming.
SQL: Both models perform similarly. DeepSeek-V3 slightly better for complex analytical queries; Qwen3-Coder slightly better for ORM-adjacent code generation.
Algorithmic / competitive programming: DeepSeek-R1-Distill-Qwen-32B wins locally — its reasoning chains (visible in output) help debug complex algorithms. This is the only case where the distilled DeepSeek is the better local pick.

IDE Integration: Continue.dev, Cline, and Cursor Local Mode

Both DeepSeek and Qwen work with Continue.dev, Cline, and Cursor's local model mode via the Ollama OpenAI-compatible API. Qwen works out of the box; DeepSeek-V3 requires API key setup with their cloud endpoint.

1
Install Ollama and pull your Qwen model: ollama pull qwen2.5-coder:32b
Why it matters: Ollama handles the GPU inference and exposes the API on port 11434.
2
In Continue.dev config.json, set provider to "ollama" and model to "qwen2.5-coder:32b"
Why it matters: This points Continue.dev at your local Ollama instance instead of cloud APIs.
3
For Cline: set baseUrl to http://localhost:11434/v1 and apiKey to "ollama"
Why it matters: Cline uses the OpenAI SDK format; any string works as apiKey for Ollama.
4
For DeepSeek-V3 via API: use api.deepseek.com with your DeepSeek API key
Why it matters: DeepSeek's API is OpenAI-compatible, so the same integrations work with a different base URL.
5
Test with a complex refactoring task to compare response quality before committing
Why it matters: Autocomplete quality varies significantly between models on your specific codebase patterns.

Verdict Matrix: DeepSeek vs Qwen by Use Case

Use the matrix below to choose — your primary constraint is whether code can leave your machine, not which model scores higher on benchmarks.

DeepSeek vs Qwen Coding Decision

Use a local LLM if:

•Code must stay on your machine (proprietary, confidential, regulated) → Qwen3-Coder 32B on RTX 4090
•You write mostly Rust or C++ → Qwen3-Coder 32B wins locally on these languages
•You need < 80 ms autocomplete latency without internet dependency → Qwen3-Coder 14B on RTX 4060 Ti
•Budget under $500 for GPU → Qwen3-Coder 7B on RTX 3060 12 GB

Use a cloud model if:

•Python or JavaScript is your primary language AND code can leave your machine → DeepSeek-V3 API
•Complex algorithmic problems or competitive programming → DeepSeek-R1 API
•No GPU available locally → DeepSeek API or Qwen API (Alibaba Cloud DashScope)
•You want the highest benchmark scores for a CI code-review pipeline → DeepSeek-R1 API

Quick decision:

→Best fully local: Qwen3-Coder 32B (RTX 4090)
→Best budget local: Qwen3-Coder 14B (RTX 4060 Ti 16 GB)
→Best API (Python/JS): DeepSeek-V3
→Best API (algorithms): DeepSeek-R1

Related Guides

Qwen production deployment guide: /power-local-llm/qwen-local-deployment-complete-guide-2026
Continue.dev vs Cline vs Aider comparison: /power-local-llm/continue-dev-vs-cline-vs-aider-local
Replace GitHub Copilot with local LLM: /power-local-llm/replace-github-copilot-with-local-llm
Best local coding models 2026: /power-local-llm/best-local-coding-models-2026
Best local reasoning model 2026 — for reasoning (not coding) distills, this is the guide: /local-llms/best-local-reasoning-model-deepseek-r1-2026
Best IDE Plugins for Local LLMs in 2026 (VS Code & JetBrains) -- VS Code and JetBrains plugins for connecting local coding models
Qwen Local Deployment: Complete Production Guide 2026 -- deploy the Qwen coding model as a persistent local server

Frequently Asked Questions

Can I run DeepSeek-V3 locally on my GPU?

No, not on consumer hardware. DeepSeek-V3 is a 236B Mixture of Experts model. Even at INT4 quantization, it requires approximately 140 GB of combined VRAM — equivalent to 6 NVIDIA A100 80 GB cards. The locally runnable alternatives are DeepSeek-R1-Distill-Qwen-32B (fits on RTX 4090 24 GB) or smaller distillations (DeepSeek-R1-Distill-Llama-8B on RTX 3060 12 GB).

Is DeepSeek-R1-Distill-Qwen-32B better than Qwen3-Coder 32B for coding?

Depends on the task. DeepSeek-R1-Distill-Qwen-32B is better for algorithmic reasoning — mathematical problems, competitive programming, complex debugging with visible reasoning chains. Qwen3-Coder 32B is better for practical coding: autocomplete, refactoring, idiomatic Rust/C++, and type-safe TypeScript. For everyday IDE use, Qwen3-Coder is the better choice; it is also 10–20% faster for autocomplete tasks.

Which local model is best for a Continue.dev or Cline integration?

Qwen3-Coder 14B on an RTX 4060 Ti 16 GB delivers the best balance of speed (14–18 tok/s) and quality for IDE autocomplete. If you have an RTX 4090, use Qwen3-Coder 32B for significantly better multi-file refactoring. Both work natively with Continue.dev, Cline, and Cursor local mode via Ollama.

What is DeepSeek-V3's API price compared to running Qwen locally?

DeepSeek-V3 API pricing (as of July 2026): $0.27 per 1M input tokens, $1.10 per 1M output tokens. At typical IDE usage (200K tokens/day), that is $0.27/day or ~$8/month. Running Qwen3-Coder 32B locally on an RTX 4090 costs ~$0.05/day in electricity plus hardware amortization of ~$1.70/day over 3 years — making self-hosted Qwen more expensive than the DeepSeek API unless you already own an RTX 4090.

Does Qwen3-Coder support function calling for agentic coding tasks?

Yes. Qwen3-Coder 14B and 32B support function calling and structured JSON output, which are required for agentic coding tools like Cline and Aider. Qwen3-Coder 7B also supports function calling but with lower reliability on complex multi-step workflows. DeepSeek-R1-Distill-Qwen-32B was not specifically optimized for function calling — Qwen3-Coder is the better choice for agentic tools.

Update Log

2026-05-26: Initial publication. Benchmark data: HumanEval/LiveCodeBench from official model releases; SWE-bench from SWE-bench.com leaderboard. Speed benchmarks measured on RTX 4090 + RTX 4060 Ti 16 GB test machines.
2026-07-01: Corrected HumanEval standings — Qwen2.5-Coder / Qwen3-Coder 32B leads at ~88.4% vs DeepSeek-Coder-V2-Lite ~83.5%. Clarified DeepSeek-Coder as runner-up (repo-level / fill-in-the-middle edge). Added CodeLlama and Llama 3 as legacy reference points.
Next review scheduled: 2026-11-26

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