The open weights year
Twelve months that took open models from interesting to unavoidable, and where the gap actually sits.
January opened with an MIT-licensed reasoning model that repriced the entire sector in a week. December closes with open weights as a normal, boring option in any serious architecture discussion.
Here is the year, and what it means for the next one.
the releases that mattered#
DeepSeek R1 (January). MIT license, published training methodology, distilled variants that ran on consumer hardware. The RL-on-verifiable-rewards recipe was reproduced widely within weeks.
Qwen3 (April). Eight models, Apache 2.0, MoE variants with excellent quality-per-active-parameter, 119 languages.
Kimi K2 (July). A trillion parameters, open weights, tuned for agentic tool use, with a genuinely novel training stability contribution.
gpt-oss (August). OpenAI's first open weights since 2019, Apache 2.0, with a 20B variant that runs on a laptop.
Plus continuous releases from Mistral, Zhipu, MiniMax, Meta, Microsoft, Google, and a long tail of fine-tunes.
where the gap actually is#
The frontier-to-open gap held at roughly six to twelve months all year. That stability is the most important finding, because it means open weights are not converging on the frontier and are not falling behind — they are trailing at a fixed distance.
But "six months behind" undersells the practical position, because the gap is not uniform:
Nearly closed: code completion, summarization, extraction, classification, translation, structured output, straightforward tool use. For these, a good open model is not meaningfully worse than a frontier model, and it costs a fraction.
Meaningfully behind: long-horizon agentic work, complex multi-step reasoning, instruction following over many turns, reliability at the tail. This is where frontier models earn their price.
Not comparable: anything requiring the surrounding infrastructure — enterprise support, uptime guarantees, safety tooling, indemnification. Open weights give you the model and nothing else.
what changed structurally#
Licensing got genuinely permissive. Two years ago "open" meant a research license with a prohibited-use list. Now the frontier of open releases is Apache 2.0 and MIT. That is a real change and it removed the legal review that was blocking adoption.
The runtime story got boring. Ollama, llama.cpp, vLLM, MLX, LM Studio. One command. An OpenAI-compatible endpoint. The friction that kept open models in the enthusiast category is gone.
Hosting became competitive. Multiple providers serve open models at prices well below frontier API rates, with real SLAs. You can use open weights without running anything.
The center of gravity moved east. The most capable, most permissively licensed open releases came predominantly from Chinese labs. That is a strategic fact with policy consequences that are being worked out loudly and mostly unproductively.
the practical architecture for next year#
The shape that makes sense:
- Open weights, self-hosted or on a cheap provider, for high-volume, well-defined tasks. Classification, extraction, embedding, first-pass drafting.
- Frontier API for the hard tail: planning, complex reasoning, anything customer-facing where a bad answer is expensive.
- A router deciding between them, with the escalation rate instrumented.
- Your own eval set in your own repository, run against every candidate.
That is not a hedge. It is what the cost and capability curves actually imply.
the prediction#
The gap holds at roughly six to twelve months through next year. Open weights absorb an increasing share of production workload by volume while frontier models keep the high-value tail.
The interesting question is not capability. It is whether the labs currently releasing weights continue to, and that is a business decision that could change in either direction with one quarter's strategy review.
Download the ones you care about. They cannot be un-released.
— Dom, December 19, 2025