Best Local LLM Coding Models 2026 — Qwen3 Beats Muse Glimmer

TL;DR

• Qwen3-Coder 30B is the default install — 77–79% SWE-bench on a single 24 GB GPU, lowest risk for production code
• Muse Glimmer scores below the human baseline on GDPval-AA v2 (953 Elo vs. 1,000) and hallucinates 82% of the time on AA-Omniscience — Meta’s own benchmarks omit this
• VRAM tier matters more than model hype — we rank by what fits your GPU, not by press releases
• Kimi K3 is frontier-class but requires ~594 GB native weights — local in theory, datacenter in practice

Meta dropped Muse Glimmer on August 10 with the usual fanfare: Apache 2.0 weights, Ollama day-0 support, and internal benchmarks showing it leading every agentic eval. Three days later, Artificial Analysis posted independent numbers. Glimmer scored 953 Elo on GDPval-AA v2 — below the 1,000 human baseline — and hallucinated on 82% of AA-Omniscience queries. If you installed Glimmer based on Meta’s numbers alone, you’re shipping code you can’t trust.

The pattern is now predictable: a lab drops new open weights, publishes cherry-picked benchmarks where the model leads, and the community treats those numbers as gospel for two to four weeks until independent evaluators catch up. Muse Glimmer is the clearest example yet — Meta’s MCP Atlas score of 75.5 looks strong until you compare Glimmer’s 82% hallucination rate against Qwen3.6 27B’s 49%. That gap is the difference between an agent that writes working code and one that invents API endpoints that don’t exist.

So I ranked seven local coding models by the only metric that actually matters for most developers: what fits your GPU and produces reliable output.

Methodology: 7 models evaluated across 3 consumer VRAM tiers plus one cluster-class tier. Selection criteria: independent benchmark performance (SWE-bench Verified, GDPval-AA v2 as of August 13 2026, Terminal-Bench 2.0, AA-Omniscience hallucination rate), VRAM footprint at Q4_K_M quantization unless otherwise noted, license permissiveness, and runner ecosystem support. Rank 1 means: the model I’d install first on a single consumer GPU for production coding work. Not considered: closed-source API-only models, models requiring multi-node clusters for basic inference (except Kimi K3, ranked for completeness).

The 7 Best Local LLM Coding Models

1. Qwen3-Coder 30B

Best for: The developer who needs one model that handles everything from inline completions to multi-file edits on a single 24 GB GPU.

Qwen3-Coder 30B is a 30B-parameter MoE model with 3.3B activated parameters and a 19 GB artifact size at Q4_K_M. It scores 77–79% on SWE-bench on consumer hardware, putting it within striking distance of frontier API models at zero marginal cost per token. The MoE architecture keeps inference fast despite the total parameter count, and single-GPU friendliness makes it the obvious first ollama pull for anyone with an RTX 4090 or M-series Mac with 24+ GB unified memory.

What makes it the default over Qwen3.6 27B (which scores comparably) is the MoE efficiency: you get similar benchmark numbers with lower per-token compute. For agentic workflows where the model runs hundreds of completions per session, that difference compounds into real time savings.

Strengths:

• 77–79% SWE-bench on single consumer GPU
• 19 GB artifact (Q4_K_M) fits comfortably in 24 GB VRAM
• MoE architecture (3.3B active) keeps inference fast
• Apache 2.0 — no commercial restrictions

Weaknesses:

• MoE models can be unpredictable on out-of-distribution tasks
• Less battle-tested than Qwen3.6 27B in community tooling

Score: 9.0 Pricing: Free (Apache 2.0, open weights)

2. Qwen3.6 27B

Best for: Developers who prioritize low hallucination rates over inference speed — especially in agentic pipelines where a single hallucinated function call poisons the chain.

Qwen3.6 27B is the dense counterpart to Qwen3-Coder. At 77.2% SWE-bench Verified and 1141 Elo on GDPval-AA v2, it matches or beats models twice its size. The number that matters most: 49% hallucination rate on AA-Omniscience. That’s not great in absolute terms, but it’s the lowest in the 24 GB VRAM class, and it’s nearly half of Muse Glimmer’s 82%. On Terminal-Bench 2.0, it scores 59.3%, beating Glimmer’s 52% and Gemini 3.5 Flash-Lite’s 54%.

The dense architecture means every parameter activates on every forward pass, which makes it slower token-for-token than MoE alternatives. But dense models produce more consistent outputs — and consistency matters when you’re wiring a model into CI pipelines where retry loops cost time.

Strengths:

• 49% hallucination rate — lowest in VRAM class
• 1141 Elo GDPval-AA v2 — above human baseline
• Dense architecture for consistent outputs
• 59.3% Terminal-Bench 2.0

Weaknesses:

• Slower inference than MoE alternatives at same parameter count
• Requires quantization to fit 24 GB VRAM

Score: 8.7 Pricing: Free (Apache 2.0, open weights)

3. Devstral Small 24B

Best for: Headless CI/CD pipelines and agentic workflows that run without a developer watching.

Devstral Small 24B was designed for unattended operation. Where Qwen models are general-purpose, Devstral is opinionated about its use case: it expects to be wired into tool-calling pipelines, consume structured inputs, and produce structured outputs. If you’re building agents with OpenCode, Hermes Agent, or Cline and want a model that doesn’t need prompt engineering to follow tool schemas, Devstral is the specialist.

Strengths:

• Purpose-built for agentic and CI workflows
• Strong structured output compliance
• Fits 24 GB VRAM class at Q4_K_M
• Apache 2.0

Weaknesses:

• Narrower general coding ability than Qwen3-Coder
• Smaller community and fewer benchmark comparisons available
• Less effective for interactive, conversational coding

Score: 8.3 Pricing: Free (Apache 2.0, open weights)

4. Gemma 4 26B MoE

Best for: Tight VRAM budgets where you need multimodal input (images, screenshots) alongside code.

Google’s Gemma 4 26B MoE activates only 3.8B parameters per forward pass, meaning it runs on a single 24 GB GPU at Q4 quantization while pulling from 26B total parameters. It ranks #6 on the Arena AI leaderboard, supports 256K context, and handles text plus images natively. For agents that need to interpret UI mockups or screenshots alongside code, it’s the only model in this list that does multimodal without bolting on a separate vision model.

The catch: coding-specific benchmarks lag behind Qwen. If you don’t need multimodal, Qwen3-Coder is strictly better for code tasks.

Strengths:

• 3.8B active params — extremely efficient on VRAM
• Native multimodal (text + images)
• 256K context window
• Apache 2.0, day-0 support across Ollama, LM Studio, llama.cpp, vLLM

Weaknesses:

• Coding benchmarks below Qwen3-Coder and Qwen3.6
• Very low active param count can miss complex multi-file reasoning
• Fewer coding-specific fine-tunes available

Score: 7.9 Pricing: Free (Apache 2.0, open weights)

5. Muse Glimmer 30B

Best for: Narrow structured tool-calling tasks where Tau3-Banking compliance matters more than general reliability.

I need to be direct. Meta’s MCP Atlas score of 75.5 makes Glimmer look competitive. Artificial Analysis tells a different story: 953 Elo on GDPval-AA v2 (below the 1,000 human baseline), 52% on Terminal-Bench 2.0, and an 82% hallucination rate on AA-Omniscience. That hallucination number means four out of five factual claims the model makes about code behavior are wrong.

The one legitimate edge: Glimmer’s Tau3-Banking tool-use score of 24% beats Gemini 3.5 Flash-Lite (18%) and Qwen3.6 27B (17%). The 1.8B ViT-G/14 perception encoder also gives it genuine multimodal reasoning for agents interpreting screenshots and UI mockups. If your workload is specifically “call these three banking APIs with structured parameters based on a screenshot,” Glimmer is actually your best local option. For everything else, install Qwen3-Coder instead.

Strengths:

• 24% Tau3-Banking tool-use — best in class
• Multimodal via 1.8B perception encoder
• 16.76 GB Q4_K_M fits 24 GB VRAM
• Apache 2.0, US-origin weights

Weaknesses:

• 82% hallucination rate on AA-Omniscience — dangerous for production
• 953 Elo GDPval-AA v2, below human baseline
• 52% Terminal-Bench 2.0, behind Qwen3.6 (59.3%) and Gemini Flash-Lite (54%)

Score: 6.8 Pricing: Free (Apache 2.0, open weights)

6. Phi-4 14B

Best for: The 8 GB VRAM tier — laptops, older GPUs, constrained environments.

When 24 GB isn’t available, your options shrink fast. Phi-4 14B is the best coding model that runs on 8 GB VRAM (MacBook Air M3, RTX 3060). It handles inline completions and single-file editing competently but can’t sustain multi-file agentic workflows. Think of it as a better autocomplete, not a coding agent.

Strengths:

• Runs on 8 GB VRAM
• Solid inline completion quality
• MIT license

Weaknesses:

• Can’t handle multi-file agentic tasks
• Noticeably weaker on complex reasoning vs. 24 GB tier models
• Limited context window compared to larger models

Score: 7.5 Pricing: Free (MIT license)

7. Kimi K3

Best for: Organizations with GPU cluster infrastructure that want frontier-tier open-weight coding performance.

Kimi K3 is the largest open-weight model ever released: 2.8 trillion parameters with 104B active, landing July 27 under Modified MIT license. Its coding benchmarks are frontier-class. The problem is physical: the native MXFP4 download is approximately 594 GB. Even aggressive 2-bit quantization only reduces this to roughly 325 GB, which means you need multi-GPU server infrastructure — not a workstation, not a Mac Studio, a server rack.

I’m ranking it 7th not because it’s bad — it’s excellent — but because “local” means “runs on hardware I own” for most readers, and most readers don’t own a multi-GPU H200 cluster. If you do have that infrastructure, K3’s coding performance justifies the deployment. For reference, Kimi K2.7-Code (the June 2026 coding-focused refresh of K2.6, same 1T-parameter MoE with 32B active) is the same brand at “smaller” scale — but even K2.7-Code requires roughly 325 GB at 2-bit quantization with a 384 GB DDR5 CPU build or 4× RTX 3090 + 256 GB RAM rig. Both are firmly enterprise cluster territory.

Strengths:

• Frontier-tier coding benchmarks
• 104B active parameters from 2.8T total
• Modified MIT license — commercial use permitted
• Largest open-weight release in history

Weaknesses:

• ~594 GB native weights — requires multi-GPU server
• Even at 2-bit quantization (~325 GB), needs enterprise hardware
• Deployment complexity far exceeds consumer-class models

Score: 8.5 Pricing: Free (Modified MIT license)

Comparison Table

Name	Score	Ideal For	VRAM Tier (Q4_K_M)	Pricing	Open Source
Qwen3-Coder 30B	9.0	Default local coding model	24 GB	Free (Apache 2.0)	Yes
Qwen3.6 27B	8.7	Low-hallucination agentic pipelines	24 GB (quantized)	Free (Apache 2.0)	Yes
Kimi K3	8.5	Frontier perf with cluster hardware	~594 GB native / ~325 GB 2-bit	Free (Modified MIT)	Yes
Devstral Small 24B	8.3	Headless CI / agent pipelines	24 GB	Free (Apache 2.0)	Yes
Gemma 4 26B MoE	7.9	Multimodal + code on tight VRAM	24 GB	Free (Apache 2.0)	Yes
Phi-4 14B	7.5	8 GB VRAM / laptop use	8 GB	Free (MIT)	Yes
Muse Glimmer 30B	6.8	Structured tool-calling only	24 GB (16.76 GB artifact)	Free (Apache 2.0)	Yes

Runner Pairing Guide

The model is half the decision. The runner determines how it integrates into your workflow.

Ollama with MLX engine is the default for Apple Silicon. Pull Qwen3-Coder with ollama pull qwen3-coder:30b and wire it into OpenCode or Cline via --model ollama/qwen3-coder:30b with OLLAMA_HOST=http://localhost:11434. LM Studio works best for developers who want a GUI for model management and quick experimentation — useful for evaluating models before committing one to a pipeline. llama.cpp / vLLM is the choice for headless CI: run llama-server -m qwen3-coder-30b.gguf --port 8080 and point your agent at http://localhost:8080/v1/chat/completions.

All three implement the OpenAI-compatible /v1/chat/completions endpoint. Externalize your base_url as an environment variable from day one — switching runners should never require a code change.

Benchmark reproducibility note: All GDPval-AA v2 and AA-Omniscience scores cited are from Artificial Analysis independent evaluations published August 13, 2026. SWE-bench numbers refer to SWE-bench Verified. Terminal-Bench scores refer to version 2.0. VRAM figures assume Q4_K_M quantization via GGUF unless explicitly noted otherwise (Kimi K3 uses MXFP4 native and 2-bit Unsloth Dynamic quantization). If you’re comparing against other sources, confirm they use the same benchmark version and quantization format — numbers shift meaningfully between configurations.

Conclusion

Install Qwen3-Coder 30B if you have a 24 GB GPU and want a single model that handles inline completions through multi-file edits. Switch to Qwen3.6 27B if your workflow is agentic and hallucination rate matters more than inference speed. Use Devstral Small 24B if you’re building headless CI agents that need structured output compliance without prompt engineering. If 8 GB VRAM is all you have, Phi-4 14B keeps you productive for single-file work. And if you’re considering Muse Glimmer — limit it strictly to structured tool-calling tasks where its Tau3-Banking edge applies. For anything else, its 82% hallucination rate makes it a liability, not an asset.

Stop trusting lab-published benchmarks at face value. Wait for Artificial Analysis or equivalent independent verification before deploying any new model to production. The two-week lag between a model’s release and credible independent numbers is cheap insurance against shipping code generated by a model that invents endpoints wholesale.