Open-Source LLMs 2026: DeepSeek V4, Llama 4, Qwen 3.5, Gemma 4, and Kimi K2.6 Compared

The Frontier Is Now Open

In 2026, the capability gap between open-weight and closed-source LLMs is measured in single benchmark points. DeepSeek V4-Pro hits 80.6% on SWE-bench Verified under MIT license. Qwen 3.5 leads open weights on GPQA Diamond at 88.4%. Llama 4 Scout offers a 10-million-token context window.

Here is every major open-weight model family you need to know.

DeepSeek V4 Pro

The strongest open-weight coding agent model in 2026. MoE architecture with 1.6T total parameters, 49B active. 1M token context. MIT license — most permissive among frontier models. Best for coding agents and software engineering workflows.

Meta Llama 4 Scout, Maverick, Behemoth

Scout (109B, 17B active) offers a 10M token context window — unmatched for whole-codebase reasoning. Maverick (400B, 17B active) balances performance and efficiency. Behemoth (2T total, 64B active) is the enterprise top end. Meta custom license with 700M MAU clause.

Alibaba Qwen 3.5 and Google Gemma 4

Qwen 3.5 leads on graduate-level reasoning (88.4% GPQA Diamond). Apache 2.0 license. Best for complex reasoning and multimodal applications. Gemma 4 from Google is the most efficient — 31B variant hits 80% on LiveCodeBench at one-tenth the active params of frontier MoEs. Apache 2.0 license.

Kimi K2.6 and Which Model to Use

Kimi K2.6 tops the Artificial Analysis Intelligence Index at 54 among open-weight models. Excels at long-horizon coding. For coding agents: DeepSeek V4 Pro. For reasoning: Qwen 3.5. For long context: Llama 4 Scout. For local: Gemma 4. For production: mix and match based on cost and capability requirements.