Open-weight models

Models whose trained weights are downloadable (run them yourself) — distinct from “fully open source” (weights + training code + data, which is rare). The defining 2026 movement in the llm-provider landscape open-source-llms-2026.

State of play (2026)

Open-weight is now “good enough for serious production,” not just a cheap alternative. Leaders: deepseek V4 Pro/R1, Meta Llama 4, Alibaba Qwen3, google gemma-4, Moonshot Kimi K2.6, Z.ai GLM-5.2 (MIT; the #1 open-weight model on artificial-analysis as of 2026-06), Mistral Large 3/Small 4, Microsoft Phi-4, and — from 2026-07 — Thinking Machines Lab‘s Inkling (Apache-2.0, 975B/41B, multimodal).

The open field is not all Chinese. Inkling’s arrival prompted Willison to count the US open-weight roster as Inkling, NVIDIA Nemotron, and Gemma 4 — three inkling-willison-review. Against qwen, deepseek, z-ai and Kimi, that’s a thin bench, and two of the three come from companies whose real business is elsewhere (chips, ads). Inkling is the outlier: a startup whose open weights are the strategy, monetized one layer up at Tinker.

Three structural trends

• MoE dominance — almost every flagship is a sparse Mixture-of-Experts: large total params, small active set (Gemma 4 25.2B/3.8B; Llama 4 Maverick 400B/17B; DeepSeek R1 671B/37B). The mechanism is a gating/router that picks the top-k experts per token (usually k=1 or k=2) and sums only those, so most of the network stays idle — conditional computation that can use less inference compute than a dense model despite carrying far more total parameters moe-architecture. That total-vs-active split is exactly why open-weight flagships get frontier capability at practical serving cost (cf. llm-inference, kv-cache). DeepSeek’s variant adds shared experts that always activate alongside the routed ones moe-architecture.

• Context explosion — 256K → 1M → 10M (Llama 4 Scout).

• Licensing clarity — Apache-2.0 won the permissive race (Qwen3, Gemma 4, Mistral Large 3); MIT for Phi-4 and DeepSeek R1. Meta’s Llama “Community” license is more restrictive; “read the terms” applies (Kimi’s Modified MIT). A third shape, and the terms tightening as the capability closes (2026-07-28, kimi-k3). Moonshot dropped Modified MIT for a bespoke Kimi K3 License: MIT-shaped for use, modification, distribution and sale, with one carve-out — run a Model-as-a-Service business above US$20M revenue over any consecutive 12 months and you need a separate commercial agreement. Embedding the model in an end-user product, or relaying to someone else’s hosting, is explicitly outside the definition. So the axis now holds three shapes: truly permissive (Apache-2.0/MIT — Qwen, GLM, Inkling), user-threshold (Llama’s 700M-MAU clause), and revenue-threshold on serving (Kimi K3). The pattern in the last two is the same: free for everyone except the clouds and API resellers who would monetize serving it. Worth holding beside the capability story — K3 is the strongest open-weight challenge to the closed frontier this spoke has recorded, and it arrived on the least permissive licence Moonshot has used.

• Multimodality reaches the open/local tier — open weights are no longer text-only: gemma-4 12B ships encoder-free vision + native audio (raw audio projected straight into the token space), runnable on 16GB VRAM/unified memory gemma-4-12b-announcement. Memory footprint, not just licensing, is becoming the open-weight battleground. Inkling shows how wide that battleground is: also encoder-free vision + audio, also Apache-2.0 — but 2TB+ of aggregated VRAM in BF16, ~600GB quantized to NVFP4 inkling-announcement. “Downloadable weights” spans a 16GB laptop and a 16×H200 cluster; the license tells you nothing about who can actually run it.

• The behaviour is editable, not just the deployment (added 2026-07-26). A permissive license on downloadable weights also licenses modifying them, safety posture included. gemma-4-31b-it-scotoma is Gemma 4 31B-it with its refusal direction thinned across layers 7–41 and republished under Google’s own Apache-2.0 (abliteration). Licensing clarity has been read here as permission to run; it is equally permission to rewrite and redistribute. Nothing in Apache-2.0 distinguishes the two.

Why it matters

Open weights enable local/private deployment (Ollama, vLLM, llama.cpp) and break the pricing floor — the competitive pressure that makes deepseek‘s low llm-api-pricing possible and forces proprietary labs to justify their premium on llm-benchmarks.

Related

open-source-llms-2026 · moe-architecture · deepseek · kimi-k3 · llama · llm-provider · llm-benchmarks · llm-inference