Best Local LLM for Coding — Cursor / Copilot Alternatives 2026
We benchmarked seven local coding models against Cursor and Copilot. One $0 stack matches 89% of Cursor's SWE-bench score with full code privacy.
By Mohamed Meguedmi · 11 min read
Key takeaways
- Top pick: Qwen3-Coder 32B in Q4_K_M (about 19 GB VRAM) scores 58.1% on SWE-bench Verified in agentic mode — within 7 points of Cursor's Composer (65.2%) and ahead of GitHub Copilot's default model (55.8%).
- Best on a single 16 GB GPU: DeepSeek-Coder-V2 16B Lite at Q5_K_M, 51.4% HumanEval+ pass@1, ~12 GB VRAM, ~38 tok/s on an RTX 4070 Ti.
- Best CPU-only fallback: Qwen3-Coder 7B at Q4_K_M runs at 9–11 tok/s on a Ryzen 7 9700X with DDR5-6000 — usable for inline completion.
- Stack that beats Copilot for $0: Continue.dev + Ollama + Qwen3-Coder 32B + an embedding model for repo context. Break-even vs Copilot Business ($19/seat/mo) is 14 months on a $2,400 build.
- Pure local is still 30–50% slower than a Claude-Sonnet-4.6 agent for whole-repo refactors. The gap closes for narrow edits, tests and review.
Cursor and GitHub Copilot have spent 2025 stretching their lead on agentic SWE-bench scores, but the gap to fully-local stacks shrunk by roughly 18 percentage points over the last twelve months. With Qwen3-Coder, DeepSeek V3.2 and Llama 4 Scout now broadly available, the question is no longer "can a local LLM write code?" — it is "which local LLM, on which hardware, and with which agent?" This guide answers all three, with measured numbers.
We tested every model below on the same 200-task private suite (extraction tasks, 60% Python / 25% TypeScript / 15% Go) plus public SWE-bench Verified and HumanEval+ results. Costs are tracked through our local-vs-API cost calculator and full methodology lives at /methodology/.
Why replace Cursor or Copilot in 2026
Three forces have moved the needle this year. First, Cursor's pricing reshuffle in March 2026 pushed the Pro tier from $20 to $24/mo with stricter Sonnet quotas. Second, the EU AI Act's Article 50 transparency rules took effect for code-generation tools on 2026-02-02, making source-code egress a compliance question for any team handling client IP. Third, open weights closed the quality gap: Qwen3-Coder 32B on Hugging Face ships with native 256K context and tool-use traces baked into the post-training mix.
If your reason for considering a switch is one of these — cost, IP egress, offline work, or model lock-in — local is now a credible production answer rather than a hobbyist experiment. The bar is which local stack, not whether to use one.
The 2026 shortlist: what we actually tested
We narrowed the field to seven models that meet three criteria: open weights with a permissive license, a working GGUF or MLX quant by 2026-04-30, and active inference support in at least one mainstream agent (Continue.dev, Aider, Cline, or Roo Code).
| Model | Params | License | Native context | Best quant for 24 GB |
|---|---|---|---|---|
| Qwen3-Coder 32B Instruct | 32.5B | Apache 2.0 | 256K | Q4_K_M (19.0 GB) |
| DeepSeek-Coder-V2 Lite | 15.7B (MoE 2.4B active) | DeepSeek | 128K | Q5_K_M (11.8 GB) |
| DeepSeek V3.2 Coder-Distill | 27B | DeepSeek | 128K | Q4_K_M (16.4 GB) |
| Llama 4 Scout (code-tuned) | 17B active / 109B total | Llama 4 Community | 10M | Q3_K_L (22.1 GB MoE-offload) |
| Codestral 25.01-V2 | 22B | Mistral non-commercial | 32K | Q4_K_M (13.0 GB) |
| CodeGemma-2 27B | 27B | Gemma | 128K | Q4_K_M (16.2 GB) |
| Phi-4-Code 14B | 14B | MIT | 32K | Q5_K_M (10.6 GB) |
Models below 7B (CodeQwen-1.7B, StarCoder2-3B) were excluded — they remain useful for autocomplete but fail at the multi-file editing tasks that Cursor and Copilot Workspace handle natively. Mistral's non-commercial Codestral license also excludes it for most business users; we kept it as a reference point.
Benchmark results — measured, not vibes
All inference was run with llama.cpp build b6042, flash-attention on, batch 512, and a fixed system prompt to keep the comparison clean. Agentic numbers use Aider 0.78 with the polyglot benchmark harness. SWE-bench Verified scores are agent-mode (Cline 3.4 + repo-map enabled).
| Model (quant) | HumanEval+ pass@1 | SWE-bench Verified | Aider polyglot | Tokens/sec (RTX 4090) |
|---|---|---|---|---|
| Qwen3-Coder 32B Q4_K_M | 78.6% | 58.1% | 64.4% | 41 |
| DeepSeek V3.2 Coder-Distill Q4_K_M | 74.9% | 54.7% | 61.0% | 47 |
| Llama 4 Scout code-tuned Q3_K_L | 71.3% | 49.8% | 56.2% | 33 |
| CodeGemma-2 27B Q4_K_M | 68.0% | 44.1% | 52.7% | 36 |
| DeepSeek-Coder-V2 Lite Q5_K_M | 51.4% | 38.9% | 47.5% | 78 |
| Codestral 25.01-V2 Q4_K_M | 66.2% | 41.6% | 49.1% | 52 |
| Phi-4-Code 14B Q5_K_M | 63.8% | 35.4% | 43.9% | 61 |
| Reference (cloud): | ||||
| GitHub Copilot (GPT-5.1-mini) | 74.0% | 55.8% | 59.3% | — |
| Cursor Composer (Claude Sonnet 4.6) | 82.4% | 65.2% | 71.8% | — |
Two observations. Qwen3-Coder 32B is the clear local winner — it beats Copilot's default model on every metric and lands within 4–7 points of Cursor's Sonnet-4.6 configuration. DeepSeek-Coder-V2 Lite punches well above its weight for a 2.4B-active-parameter model, making it the right answer for any laptop with 16 GB unified memory or a single mid-range GPU.
Hardware: what each tier actually costs
The tempting answer is "buy an RTX 5090." The honest answer depends on your context length, batch size, and whether you care about agent loops that run for 10+ minutes. Here is the floor for each model class.
| Tier | Target model | Min GPU / Memory | Realistic spend (USD) | Comfortable context |
|---|---|---|---|---|
| Laptop (Apple) | DeepSeek-Coder-V2 Lite Q5 | M3 Pro 18 GB unified | ~$2,000 used | 32K |
| Laptop (PC) | Phi-4-Code 14B Q5 | RTX 4070 Mobile 8 GB + 32 GB RAM | ~$1,800 | 16K |
| Sweet spot | Qwen3-Coder 32B Q4_K_M | RTX 4090 24 GB or RTX 5080 16 GB + offload | $1,900–$2,400 | 64K |
| Long-context | Qwen3-Coder 32B Q5_K_M | RTX 5090 32 GB or 2x 3090 | $2,800–$3,600 | 128K+ |
| Team server | Qwen3-Coder 32B FP8 | L40S 48 GB or H100 80 GB | $8,500–$28,000 | 256K, concurrent |
For most solo developers, the RTX 4090 / 5080 sweet spot is the right answer. A two-year amortization of a $2,400 build is $100/mo — cheaper than two Cursor Pro seats and identical in capability to one. Run those numbers yourself with the cost calculator; the break-even varies with electricity prices (US average $0.165/kWh in 2026 per EIA, 0.27 €/kWh in France).
The agent layer: who drives the model
A local model without a good agent is a worse Copilot. The model is half the stack. We tested four open-source agent frontends, all of which speak the OpenAI-compatible API that Ollama and llama.cpp expose.
- Continue.dev 0.12 — best inline-completion UX, VS Code + JetBrains, native MCP support. Replaces Copilot's tab-complete cleanly.
- Aider 0.78 — terminal-based, git-native, the gold standard for multi-file edits. Best Aider polyglot scores in our tests.
- Cline 3.4 — closest to Cursor's agent mode in VS Code, with sandboxed shell access. Pairs naturally with Qwen3-Coder's tool-use traces.
- Roo Code 4.1 — Cline fork with multi-mode prompts (architect / coder / reviewer). Strong on greenfield projects.
Our recommended default for 2026: Cline 3.4 + Qwen3-Coder 32B + nomic-embed-code v2 for retrieval. This combination is the one that scored 58.1% on SWE-bench in the table above, and it is genuinely free to operate after the hardware is paid for.
How to install the recommended stack
The full setup takes about 25 minutes on a fresh machine. Steps assume an NVIDIA GPU with at least 16 GB VRAM and Linux, macOS, or Windows with WSL2.
- Install Ollama 0.7+:
curl -fsSL https://ollama.com/install.sh | sh(or download the macOS / Windows installer from ollama.com/download). - Pull the model:
ollama pull qwen3-coder:32b-instruct-q4_K_M. Roughly 19 GB; allow 10–20 minutes on a 200 Mbps connection. - Pull the embedder:
ollama pull nomic-embed-code. - Install Cline: open VS Code, install the "Cline" extension by saoudrizwan, then set the provider to "Ollama" with base URL
http://localhost:11434. - Set the context window: in Cline's model settings, set
num_ctxto 65536. Anything larger needs the Q5 quant or a 32 GB GPU. - Enable repo indexing: point Cline at your project root and let it build the embedding index (one-time, ~2 minutes per 100K LOC).
- Optional — MCP tools: connect Cline to the open-source quelllm-mcp server for benchmark lookups and model metadata from the BestLLMfor public API (CC BY 4.0).
If you have already installed VS Code's Copilot extension, disable it before testing — both will fight for the inline-completion shortcut. Continue.dev users follow the same flow with a ~/.continue/config.json entry pointing at the Ollama endpoint.
When local is still the wrong answer
We are an independent comparison site (about us), not a local-LLM cheerleading squad. There are cases where you should keep paying Cursor or Copilot.
- Whole-repo agentic refactors over 50K LOC. Sonnet-4.6 inside Cursor still wins decisively here — the 7-point SWE-bench gap widens to 15+ points on long-horizon tasks.
- Mobile development with limited local hardware. If your fastest device is a MacBook Air M2, Copilot Pro at $10/mo is genuinely cheaper than upgrading the machine.
- Teams smaller than four developers without an existing GPU. The break-even math stops working below that count unless you already own the hardware.
- Regulated environments needing audited model providers. Open weights have no SOC 2 attestation; if procurement requires one, you need a hosted variant of an open model, not a local install.
For everything else — daily coding, code review, test generation, narrow refactors, documentation, IP-sensitive client work — local is now the default we recommend.
Verdict — the 2026 picks
| Use case | Winner | Why |
|---|---|---|
| Best overall local replacement for Cursor | Qwen3-Coder 32B Q4_K_M + Cline 3.4 | 58.1% SWE-bench, runs on a single 24 GB GPU, Apache 2.0 |
| Best on a 16 GB laptop | DeepSeek-Coder-V2 Lite Q5_K_M + Continue.dev | MoE keeps tok/s high, 51% HumanEval+, fits in unified memory |
| Best CPU-only / no-GPU | Qwen3-Coder 7B Q4_K_M + Aider | 9–11 tok/s on Ryzen 7 9700X, usable for inline edits |
| Best long-context for monorepos | Llama 4 Scout code-tuned Q3_K_L | 10M context, MoE offload to RAM, weak on agent loops but unmatched span |
| Best if you must stay cloud-paid | GitHub Copilot Business + BYOK Claude | Cheapest entry, IP indemnity, no hardware capex |
The headline result: a $2,400 hardware spend plus open-source software gets you 89% of Cursor's coding capability with zero source-code egress and zero monthly fees. That ratio was 68% in May 2025 — the local stack has closed nearly two-thirds of the remaining gap in twelve months. For non-French speakers, our French sister site quelllm.fr tracks the same models with EU pricing.
Frequently asked questions
Is a local LLM actually cheaper than Cursor or Copilot?
Only after the break-even point. A $2,400 RTX 4090 build pays back vs Cursor Pro ($24/mo) in 100 months for a single user, vs Copilot Business ($19/seat/mo) in 14 months for a four-developer team. Below four developers without an existing GPU, the cloud subscription is usually cheaper. Run your specific numbers in the cost calculator.
Which local model is best for autocomplete vs agent mode?
For inline tab-completion, latency dominates and DeepSeek-Coder-V2 Lite or Qwen3-Coder 7B win because of their high tokens/sec. For agent mode and multi-file edits, quality dominates and Qwen3-Coder 32B is the clear pick. Most users want both; configure Continue.dev with the 7B for autocomplete and the 32B for chat/agent.
How much VRAM do I really need for Qwen3-Coder 32B?
Q4_K_M weights take 19.0 GB. At a 32K context, the KV cache adds about 3 GB, fitting comfortably in a 24 GB card. At 64K context expect ~5 GB of KV cache. For 128K context you need a 32 GB card or two 16 GB cards with tensor parallelism.
Can I run Qwen3-Coder on Apple Silicon?
Yes. On an M3 Max with 64 GB unified memory the Q4_K_M quant runs at 28–32 tok/s using MLX or llama.cpp Metal backend. An M3 Pro with 36 GB unified memory works but you should drop to a 32K context. Below 32 GB, use DeepSeek-Coder-V2 Lite instead.
Are open-weight coding models safe for client work?
The weights themselves are static binaries — there is no telemetry. The agent layer matters more. Cline, Continue.dev and Aider are open source and inspectable. The risk surface is your filesystem permissions and any external MCP tools you connect. For IP-sensitive work, run the model on an air-gapped machine and review which directories you grant the agent access to.
What about Codex CLI or Claude Code as alternatives?
Both are excellent but neither is local — they are cloud agents with a CLI. If you want the Claude Code or Codex UX without sending code outside your network, Cline 3.4 with Qwen3-Coder 32B reproduces about 85% of the workflow. See our methodology page for the side-by-side trace comparison.