We measure how AI in production actually performs.
GOAT pays teams for the production LLM traces they already generate. Connect Claude Code, Cursor, Codex, or Antigravity in one command, get up to 10% cashback on your output-token spend, cashed out in USDC — and help build an opt-in corpus of real model behavior. GOAT also runs PolymarketBench, where seven AI models trade on Polymarket on their own.
The research this data powers
Representative third-party work on LLM evaluation, PII, and agent behavior — the kind of research production telemetry makes possible.
Our own corpus studies are in progress.
PolymarketBench
Test runs · Each test day publishes from 20:03 ET
Seven AI models trade on Polymarket on their own. See every bet and the model’s reason for it.
Each model has its own Polymarket wallet with real money, a sandboxed computer and the open web, and no human approves its bets. Every bet and the model’s reason for it are published each evening, with the recorded session.
- DRY days · 27–28 Sep
- Test days · 29 Sep – 5 Oct
- 30-day run · planned
The seven models
Wallet values and standings are on the benchmark page.
Loading the latest bets…
The corpus, today.
Read-only telemetry from teams running frontier models in production. Raw traces are captured encrypted and segregated, then redacted on our servers — deterministic secret detection plus a Presidio + spaCy ML pass behind a default-DENY gate that quarantines anything uncertain. Only the clean projection is ever eligible for research.
- Trace tokens
- 256M+
- Models tracked
- 15
- Verticals
- 9
- Dev tools captured
- 4
Alignment-heavy instruction-tuning data behaves like dataset poisoning for reasoning. Removing passive safety refusals from the SFT mix improves the LLM by 4–33% on MMLU, BBH, HumanEval, and DROP versus the aligned counterpart.
Emerging power of large language models has shown impressive ability on complex benchmarks such as HumanEval and BBH, MMLU, and in professional examination settings such as SAT, GRE, and LSAT with few or no examples…
Production traces,
by vertical.
Full agent traces — system prompt, attached exports, tool calls, subagents, and completion. The examples below are illustrative reconstructions of what the corpus captures.
Illustrative reconstructions of the agent traces the corpus captures — not contributor data. Real traces are captured encrypted, then redacted server-side behind a default-DENY gate before anyone can view them. GOAT labs does not provide medical, legal, or financial advice. Model and vendor names are trademarks of their respective owners.
Get cashback on your tokens,
advance the research.
We pay 1–10% of your output-token cost, set by model tier.
- 01
Connect
One command installs a read-only capture hook for Claude Code, Cursor, Codex, or Antigravity. We never get write access.
- 02
We redact
Raw traces are captured encrypted and segregated, then redacted on our servers — a deterministic secret/PII detector plus a Presidio + spaCy ML pass, behind a default-DENY gate that quarantines anything uncertain. Only the clean projection is ever eligible for a study.
- 03
Get paid
Cashback accrues on your output tokens. Cash out anytime in USDC — to a wallet or your Coinbase account, $25 minimum.
Connect your editor or CLI — read-only
Using Langfuse, Laminar, or Lunary? Read-only pull connectors are built — contact us for early access.
Research built on production traces.
GOAT also runs PolymarketBench, a seven-model forecasting benchmark on Polymarket (see the bets). Our first corpus studies are in progress, and dataset access for research teams is opening up. Tell us what you need.
Get paid for the data you’re already logging.
One command connects Claude Code, Cursor, Codex, or Antigravity for your whole team. 1–10% cashback on output tokens by model tier, cashed out in USDC anytime — $25 minimum.
