I switched because AI got too expensive. OpenCode Go made it make sense.
Like a lot of developers, I watched my AI subscriptions drift upward while the value stayed flat. OpenCode Go flipped the model: $10/month, thirteen open coding models, generous quota windows. But it does not tell you which model fits which task. That is what ZenPick is for.
Origin story
What ZenPick does
Three things, on one page. A sortable table of every Go model with live benchmark scores. A quota calculator that turns a token estimate into a number-of-requests per window. A detail drawer that names the closed-source model each Go model replaces, and why.
Everything runs on a stale-while-revalidate cache, so the page is fast on revisit and never asks the upstream APIs for the same data twice within six hours.
The numbers
First month $5.
Across 6 providers.
Stale-while-revalidate.
Cross-referenced via modelgrep + LLM Stats.
Methodology
Every model gets a 0–100 fit score for five scenarios — Brainstorming, Coding, Competitive, Agentic, and Budget. Scores are normalized across the current model population, so the ordering is always meaningful and always non-empty. The table sorts by the active scenario; if no scenario is active, it sorts by raw coding benchmark.
Tags, migration hints, and thermal burn rates are inferred. Nothing on the page is hand-curated. If the upstream data changes, the page changes.
Data & attribution
Benchmark scores, pricing, and speed data are aggregated by modelgrep.com (OpenRouter pricing + Artificial Analysis benchmarks) and llm-stats.com (benchmark scores, rankings, and pricing), and used with attribution. The model list, endpoint types, and quota windows come from the OpenCode Go documentation .
Everything ZenPick computes on top — scenario fit scores, burn rate tiers, migration hints — is derived from those sources, in the schematic above. The data and the attribution are kept close on purpose.
| Provider | License | Use in ZenPick |
|---|---|---|
| modelgrep.com attribution required | Free public API (no key required) | Benchmark scores, OpenRouter pricing, speed/latency, model metadata |
| llm-stats.com attribution required | Per LLM Stats API terms | Benchmark scores, rankings, pricing, model metadata |
| opencode.ai/docs/go | Per OpenCode Go docs | Model list, endpoint types, quota windows |
Thermal key
Every model in ZenPick is tagged with one of three thermal burn rates. It is a single classification — derived from blended price per million tokens — that tells you how fast the model will consume your $12/5h window.
burnRateFromPrice() in src/lib/burn.tsReady to compare?
Find the model that matches your task and keep your OpenCode Go quota from disappearing too fast.