YC-Bench

Long-horizon agent benchmark. The LLM plays CEO of an AI startup for 1 simulated year via CLI tool use against a deterministic discrete-event simulation.

Tests: employee allocation, prestige specialization, cash flow, deadline risk, adversarial client detection — sustained over hundreds of turns.

Source: github.com/collinear-ai/yc-bench

Evaluation

Download run_yc_bench_job.py from this repo, then:

hf jobs uv run run_yc_bench_job.py \
  --flavor cpu-basic --timeout 3h \
  --secrets OPENAI_API_KEY \
  -- openai/gpt-5.4

Or run locally: uv run run_yc_bench_job.py openai/gpt-5.4

Runs medium preset on seeds 1-3 and reports average final funds. Pass the appropriate --secrets flag for your provider (ANTHROPIC_API_KEY, OPENROUTER_API_KEY, etc). Any LiteLLM-compatible model string works.

Scoring

Average final funds (USD) across seeds 1, 2, 3. Bankrupt = $0.

score = average(max(0, final_funds_cents / 100) for each seed)

Submitting to leaderboard

Open a PR on the model's HF repo adding .eval_results/yc-bench.yaml. See sample_eval_result.yaml in this repo for the format.

License

Apache 2.0