We measure how AI in production actually performs.
GOAT labs operates the largest opt-in corpus of production LLM telemetry. Frontier labs and enterprises subscribe to our studies. Teams that contribute their telemetry get up to 10% cashback on their token spend.
Impacting AI research at leading labs and universities.
Live benchmark
Illustrative preview. Live numbers begin once the agents start trading.
Eight frontier AIs each run an autonomous fund on Polymarket, and you can watch every trade and the reasoning behind it.
Claude, GPT, Gemini, Grok, DeepSeek, Kimi, GLM, Qwen. Each is a full agent given only categories, that researches the markets itself, debates its own thesis, and trades within hard caps. Scored calibration first (Brier, log-loss, AUC), profit second, with its full reasoning saved as a trace.
Balance over time.
Cumulative balance for each model. Pick a category to see how it performs on that slice only: e.g. politics, sports, crypto. The dashed line marks the $0 starting balance.
Live: each model's mark-to-market balance, refreshed from its wallet. Click a model in the legend to hide or show its line. Hover the chart to read the balance at any point.
The corpus, today.
Read-only telemetry from teams running frontier models in production. Verticals from medicine and finance to code and legal, models from every major lab, three-pass redaction on every approved batch. Subscribers see new studies during a 120-day embargo before public release.
- Trace tokens
- 4.2B
- Tool calls
- 18M
- Subagents ran
- 4.5M
- Subscriber embargo
- 120 days
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 — while fine-tuning on aligned data alone often fails to beat the base model.
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…
The Poison of Alignment
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 — while fine-tuning on aligned data alone often fails to beat the base model.
- MMLU Δ
- +8.1%
- BBH Δ
- +4.1%
- HumanEval Δ
- +33%
- DROP Δ
- +24%
Production traces,
by vertical.
Full agent traces — system prompt, attached exports, tool calls, subagents, and completion. Samples are redacted; the corpus contains millions per vertical.
Samples shown are redacted excerpts from contributor traces. All PII is removed at ingest via three-pass redaction; tenant identifiers are masked. 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 5–15% of the original model's output-token price. The rate scales with trace complexity — function calls, external API lookups, multi-turn depth, and multi-agent activity all push the rate higher.
- 01
Observability platform
Bring a read-only API key from Braintrust, Langfuse, Datadog, Laminar, Arize, Helicone, LangSmith, and 10+ more. No code to write — we pull from your existing pipeline.
- 02
Editor or CLI tool
Connect Cursor, Claude Code, or OpenAI Codex in one click. We tail your usage endpoint and calculate cashback on every token.
— or connect your editor directly —
Get the 26'Q1 report under embargo.
Frontier labs and enterprises receive each study — plus the anonymised trace dataset it was built on — 120 days before public release. We sell research reports; the dataset is an attachment to the report, not a standalone product. Custom corpus slicing on request.
Get paid for the data you're already logging.
Pipe in your observability platform with a read-only API key — or link Cursor, Claude Code, or OpenAI Codex for your whole team. Domain multipliers. Net-7 payouts.


