What is the best local LLM in April 2026?

Qwen 3.6 27B (released April 22, 2026) is the best local LLM for most users. It is a dense 27B model that scores 77.2 on SWE-Bench Verified, outperforming the 397B Qwen 3.5 MoE on agentic coding. At Q4_K_M it uses 16.8GB and runs at about 25.6 tokens per second on Apple M-series. For OpenClaw specifically, gpt-oss 20B has cleaner tool-call JSON output and is the safer production pick.

Why not Llama 3.3 70B anymore?

Llama 3.3 70B was the headline open-weight pick in 2025, but Qwen 3.5 / 3.6 and gpt-oss have shipped models that match or beat it on agentic benchmarks while running at smaller sizes. Llama 3.3 70B is still solid, but it is no longer the default choice in April 2026. Pick a Qwen 3.6 27B or gpt-oss 20B for new deployments unless you specifically need the 70B reasoning depth.

Should I use Qwen 3.5 27B or Qwen 3.6 27B?

Qwen 3.6 27B (released April 22, 2026). The Qwen 3.5 27B has a known tool-calling bug in Ollama (GitHub issue #14493) where tool calls return malformed output, which breaks OpenClaw and other agent frameworks. Qwen 3.6 fixes this and also scores higher on agentic benchmarks. Use 3.6.

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Hardware April 26, 2026

Best Local LLM by RAM (April 2026): 8GB to 128GB Hardware Picks

Your RAM is the single biggest constraint on which local LLM you can run. The April 2026 landscape moved fast: Qwen 3.6 27B (released April 22) now outperforms 397B-parameter MoE models on agentic coding benchmarks, gpt-oss has the cleanest tool-call output for OpenClaw, and Llama 3.3 70B is no longer a headline pick. This hub maps every common RAM tier (8GB through 128GB) to the best model that actually fits today.

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Pick Your RAM Tier (April 2026)

Your RAM	Best Pick	Best For OpenClaw	Detailed Guide
8 GB	Qwen 3.5 4B (Q5_K_M)	Not recommended — use cloud	8GB guide →
16 GB	Qwen 3.5 9B (Q5_K_M)	gpt-oss 20B (Q4)	16GB guide →
24 GB	Qwen 3.6 27B (Q4_K_M) ← NEW	gpt-oss 20B (Q5)	24GB guide →
32 GB	Qwen 3.6 27B (Q6_K)	Qwen 3.6 27B / gpt-oss 20B (Q8)	32GB guide →
48 GB	Qwen 3.6 35B-A3B (Q5)	Qwen 3.6 27B (Q8)	48GB guide →
64 GB	gpt-oss 120B (Q4_K_M)	gpt-oss 120B / Mistral Small 4 (119B-A6B)	64GB guide →
96 GB	Qwen 3.5 122B-A10B (Q4_K_M)	gpt-oss 120B (Q5)	96GB guide →
128 GB	gpt-oss 120B (Q6_K)	gpt-oss 120B (Q8)	128GB guide →

If you came from a Reddit-style model search, use the short versions first: Best local LLM Reddit picks for OpenClaw, Reddit’s favorite local LLM for OpenClaw, Best OpenClaw model Reddit users recommend, 32GB Reddit shortlist, 64GB Reddit shortlist, or 128GB Reddit shortlist. Those pages answer the “what does Reddit recommend?” phrasing directly, then route back here for the full RAM table.

What Changed in April 2026

The local LLM landscape shifted hard between February and April 2026:

Qwen 3.6 27B (April 22) — Dense 27B that outperforms the 397B Qwen 3.5 MoE on agentic coding (77.2 vs 76.x on SWE-Bench Verified). The new default for 24-48GB tiers.
DeepSeek V4 / V4 Pro (April 24) — Cloud-class, not realistic for local hosts at any consumer RAM tier.
GLM-5.1 (April 7) — 744B MoE from Z.ai. Cloud-only. (Earlier guides citing “GLM-5.1 32B” were referring to the older GLM-4 line, not 5.1.)
Mistral Small 4 (March 16) — 119B-A6B MoE that fits at Q4 in about 60GB. Replaces Mistral Large 123B.
Qwen 3.5 small series (March 2) — 0.8B / 2B / 4B / 9B variants. The 9B is the new 16GB tier pick.
Qwen 3.5 medium (February 24) — 27B dense, 35B-A3B MoE, 122B-A10B MoE. The 35B-A3B MoE is excellent at 48GB.
Llama 3.3 70B — Still works, no longer the default. The Qwen and gpt-oss families have caught up at smaller sizes.

How to Use This Guide

Step 1: Find your usable RAM, not your installed RAM. On Mac, the OS reserves 4-6GB. On Windows or Linux with an NVIDIA GPU, the relevant number is VRAM (the GPU’s onboard memory), not system RAM.

Step 2: Subtract context overhead. A 32K context window costs roughly 4-6GB. A 128K window costs 16-24GB. Model weights are not the only thing that has to fit.

Step 3: Pick the highest-quality quant that leaves headroom. Q5_K_M is the sweet spot. Q4_K_M is the standard. Below Q3 starts to hurt tool calling, which kills agent runs.

OpenClaw Tool-Calling Reality Check (April 2026)

Most local LLM guides talk about benchmark scores. For OpenClaw, only one metric matters: does the model emit valid JSON when asked to call a tool, hundreds of times in a row, without drift?

Models that pass this filter today:

gpt-oss 20B — cleanest tool-call JSON in production, this is the safe default
gpt-oss 120B — same family, scaled up
Qwen 3.6 27B — fixed the tool-calling regressions from 3.5
Qwen 3.6 35B-A3B (MoE) — fast inference with reliable tools
Llama 3.3 70B — still fine for tool calls
Mistral Small 4 (119B-A6B) — works, but heavier than gpt-oss

Models to avoid for OpenClaw right now:

Qwen 3.5 27B — known broken tool-calling in Ollama (GitHub issue #14493)
Anything under 7B — too unreliable for autonomous loops
Most fine-tunes of base models

Quantization Cheat Sheet

Quant	Bits/weight	Quality	When to use
Q8_0	8	Near-FP16	When you have 2x the model size in RAM
Q5_K_M	~5.5	Indistinguishable from Q8	Best quality-to-size ratio
Q4_K_M	~4.5	Loses 1-3% on benchmarks	Standard pick when RAM is tight
IQ3_XS	~3.3	Noticeable degradation, MoE-friendly	Squeeze a bigger model into too-little RAM
Q2_K	~2.6	Significantly degraded	Last resort, breaks tool calling