Large language models
Qwen's open-weight releases mean Chinese models now compete alongside proprietary systems. We would choose a model by task performance, deployment rights and cost per accepted result. A fixed ranking of four labs is a poor guide.
Research snapshot · updated Nov 12, 2026
Core thesis
By mid-2026 the frontier is four to five labs – Anthropic, OpenAI, Google, xAI – trading the lead generation to generation. Meta abandoned open-weight Llama for the proprietary Muse Spark (April 2026), ceding the open lead to DeepSeek and Qwen, which anchor a capable open tier months behind the closed frontier. Frontier training runs now cost several billion dollars, not the ~$1bn of 2024. Value accrues to the model-and-platform layer and, increasingly, to whoever controls inference compute and agentic tool-use; the open tier is the ceiling on closed-API pricing.
State of the art (2026)
Anthropic suspended access to its new models Claude Fable 5 and Mythos 5 on 12 June 2026 and announced on 30 June that access was restored. Mythos is open only to an approved set of US organisations. By September 2026 the main providers listed OpenAI's GPT-6 Astra, Anthropic's Claude Fable 5.1, Google's Gemini 3.8 Flash and xAI's Grok 4.6 as their newest models. Development effort has moved from model size towards reasoning at run time and agents that work through long tasks. We do not have a current, comparable ranking of the leading models.
Open source vs closed — the moat question
Meta's Llama, Mistral, and DeepSeek demonstrate that capable open-source models can be trained at fraction of frontier cost. But the gap on the hardest tasks (complex reasoning, agentic multi-step planning) is widening. The moat is not in the model weights — it's in RLHF quality, safety tuning, enterprise distribution, and the data flywheel from production usage.
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Signal stack
Evidence stacked leading → lagging
9 signals
Signal stack
Evidence stacked leading → lagging
Technology-native KPIs
Metrics that predict trajectory, tracked over time
1 tracked
Technology-native KPIs
Metrics that predict trajectory, tracked over time
Landscape map
Who builds what, and who depends on whom
192 players · 5 layers
Landscape map
Who builds what, and who depends on whom
Catalyst calendar
Dated events that will move the position
3 ahead
Catalyst calendar
Dated events that will move the position
Technology roadmap
Milestones on the path to maturity
8 milestones
Technology roadmap
Milestones on the path to maturity
Watchlists
Companies, people and papers, each with a condition for removing it
20 · 20
Watchlists
Companies, people and papers, each with a condition for removing it
Decision frameworks
What our view means for your job
Locked
Decision frameworks
What our view means for your job
Thesis changelog
When our view changed, and why
11 updates
Thesis changelog
When our view changed, and why
Change our mind
3 disconfirming conditions
Comparable wave
The historical analogue on the S-curve
Common mistakes
What the market gets wrong right now
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The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on Large language models has changed. All of it is in CanaryIQ.