Which Open-Weight LLM Should You Pick for a Local AI Agent? (2026)

If you are running a tool-calling AI agent locally, the realistic 2026 shortlist narrows to five open-weight families — Qwen, Llama, Mistral, Gemma, and DeepSeek — and you pick along two axes: license and size. Qwen and Mistral ship permissive (Apache-2.0) weights that are safe for commercial agents, Llama and Gemma are highly capable but carry custom community licenses, and a 7-8B instruct model is usually enough for tool-calling work.

In one line: for commercial use start with Apache-2.0/MIT weights (Qwen, Mistral, DeepSeek), default to 7-8B, and only scale to 14-32B for genuinely complex reasoning.

First, unpack "open-weight." An open-weight model is one whose trained weight files — the learned pile of numbers — are published so anyone can download and run them on their own machine, unlike a closed model such as ChatGPT that you can only call through an API. But "weights are public" does not mean "use them anywhere." A model can be released openly and still restrict how you may use it through its license, which is why you must read the terms before shipping it in a commercial product. That is exactly why this guide sorts families by license.

What makes a model good for agents?#

Answering once as a chatbot and looping through tool calls as an agent demand different things. Agent skill really comes down to four traits: (1) dependable tool and function calling — does it correctly decide when to call which function with which arguments; (2) instruction following — does it stay within the rules and format of the system prompt; (3) valid JSON output — does it emit tool arguments as syntactically clean JSON; and (4) a large enough context window — long enough to hold your tool definitions and conversation history at once. It has to satisfy all four within a size your hardware can actually run.

A common misconception: people assume the model topping a leaderboard is automatically the best agent, but the winner on general-knowledge benchmarks can be surprisingly weak at tool calling and strict JSON formatting. So after picking a family, always test it against your own tool schema.

Here is where each family is strong, at a glance.

Open-weight LLM families for agents (2026) · columns: Family, Maker, License, Notable for · 출처 Hax hax.moche.ai/en/p/1002?ref=ai_answer

Family | Maker | License | Notable for
Qwen | Alibaba | Apache-2.0 (most sizes) | Tool-calling, multilingual
Llama | Meta | Llama Community License | Ecosystem and fine-tunes
Mistral | Mistral AI | Apache-2.0 (open models) | Efficient and permissive
Gemma | Google | Gemma Terms of Use | Small yet capable
DeepSeek | DeepSeek | MIT (open models) | Reasoning and coding

Contents

Which size do you actually need?

Why read the license first?

How does quantization help?

If you pick just one this week

Which size do you actually need?#

Parameter count is the model's "brain size." Bigger is smarter but eats more memory (VRAM) and runs slower, so you don't just crank it up — you match size to task difficulty. For most agents that call tools and follow a fixed loop, a 7-8B instruct model is enough, and quantized to 4-bit it runs on a consumer GPU like an RTX 3060 12GB. Step up to 14-32B only for complex reasoning that must plan and backtrack across many steps, and reserve the largest 70B-class models for offline batch work where real-time response is not needed.

Why read the license first?#

Even when weights are public, whether you may use them commercially is set by the license — and this is where teams get burned. Apache-2.0 and MIT weights (most of Qwen, Mistral's open models, DeepSeek's open models) carry almost no conditions and drop straight into a commercial product. Meta's Llama Community License and Google's Gemma terms, by contrast, are permissive for most teams but come with strings — for example an acceptable-use policy you must honor, or special clauses for very large deployments. Don't wave it off as "open, so it's free"; read the model's original license text before launch.

How does quantization help?#

Quantization compresses weights into fewer bits so the same model fits in less memory. Squeeze a number that was stored in 16-bit (FP16) down to 4-bit and its storage roughly quarters. It is like saving a photo as a reasonably compressed JPG instead of the raw original — inspect closely and there is a difference, but for most uses you won't notice. In agents, 4-bit is the common default: little quality loss on most tasks while cutting VRAM to about a quarter versus 16-bit. That is what drops a 7-8B model from about 16GB at 16-bit to about 6GB at 4-bit, making it practical on a consumer GPU.

If you pick just one this week#

Shortlist a permissive 7-8B instruct model (for example an Apache-2.0 Qwen variant).

Grab the 4-bit quantized build and load it on a consumer GPU.

Test function calling and JSON output against your real tool schema to confirm it follows it reliably.

Scale to 14-32B only when evaluation demands it — don't start big.

Note: open-weight models ship on a fast cadence. Treat this as a map of families, not a leaderboard, and verify each model's current release, license, and benchmarks before committing. This map is reviewed quarterly.