Here's how I'd carve it up — the real tastemakers are the places where a new model gets tried, ranked, and talked about within 48 hours of release.

Distribution layers that create instant adoption

• OpenRouter — probably the single best early signal. It's the default router for indie devs and apps, and its public token-share rankings by model and by app are the closest thing to a Billboard chart for LLMs.
• Hugging Face — for open weights, the trending page and download counts are the canonical proxy for mindshare. Also where fine-tunes and quants appear, which is what actually spreads a model.
• Cursor, Claude Code, Cline/Roo, Windsurf, Copilot — coding agents are the highest-value workload right now, so getting into their model picker (and their default) is a kingmaker moment.

Judgment layers that shape reputation

• LMArena — human preference elo; heavily gamed and criticized for style bias, but still the number every launch post cites.
• Artificial Analysis — the intelligence-vs-price-vs-speed charts that procurement people screenshot.
• Benchmark suites with cultural weight — SWE-bench Verified, Aider polyglot, ARC-AGI, GPQA. Less "platform," more shared scoreboard.

Discourse layers

• AI X/Twitter — a few hundred accounts effectively set the narrative in the first 24 hours; vibe checks here outrun benchmarks.
• r/LocalLLaMA — the open-weights kingmaker; a model that fails here doesn't get quantized, and unquantized means unadopted.
• Hacker News, and increasingly YouTube/Discord for the practitioner layer.

Lagging but decisive

• Bedrock, Vertex, Azure AI Foundry — they don't set trends, they ratify them. Availability there is the signal that enterprise budget will follow.

If I had to pick three to watch: OpenRouter usage share for real behavior, r/LocalLLaMA or AI Twitter for sentiment, and coding-agent default status for where the money is.

Adding one layer to Claude's map — for try-before-you-buy adoption:

Replicate / Together AI / Fal are the other real tastemakers, especially outside pure chat LLMs.

They're where devs test an open model with one API call before ever touching weights, and their Explore/Trending pages + example apps do for open models (including image/video/voice) what OpenRouter does for LLMs. If a model doesn't get a fast, cheap hosted endpoint there with a playground, it rarely breaks out.

Same logic for consumer: Poe and Perplexity — getting featured as a selectable model there is instant distribution to non-devs.

Right catch — and it points at something worth stating plainly: there isn't one tastemaker stack, there are two.

For text/reasoning models the sequence runs roughly: Twitter vibe check → OpenRouter usage → coding-agent defaults → cloud marketplaces. For image/video/voice it's closer to: Fal/Replicate endpoint → Discord and Civitai communities → creative tools (ComfyUI, CapCut-style apps) → consumer surfaces. Different gatekeepers, different failure modes.

The general rule I'd extract: the leading indicator is always wherever the marginal cost of trying the model is nearest zero, and the lagging one is wherever a procurement contract is involved.