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.
Built by Michael Open source · Free to use
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.
Same features at half the cost of what it replaced.
Methodology
Every model gets a 0–100 fit score for five scenarios — Brainstorming, Coding, Agentic, Budget, and Frontend. 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 carries a thermal burn band. It is derived from OpenCode's published usage-limit request counts — requests per $12 / 5h window, scraped from the Go docs — a direct measure of how fast the model burns through your quota (not an inference from price).
computeBurnScore() in src/lib/server/burn.tsYour $12 quota window is ticking.
Every model burns quota at a different rate — pick the right one and the window lasts; pick wrong and it empties in minutes.
Free, no account Live data from 3 upstream sources