---
pretty_name: "YC-Bench"
language:
  - en
license: apache-2.0
size_categories:
  - n<1K
task_categories:
  - text-generation
tags:
  - benchmark
  - agents
  - long-horizon
  - simulation
  - evaluation
citation: |
  @misc{collinear-ai2025ycbench,
        author       = {{Collinear AI}},
        title        = {{YC-Bench}: Your Company Bench — A Long-Horizon Coherence Benchmark for {LLM} Agents},
        year         = {2025},
        howpublished = {\url{https://github.com/collinear-ai/yc-bench}}}
---

# 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](https://github.com/collinear-ai/yc-bench)

## Evaluation

Download `run_yc_bench_job.py` from this repo, then:

```bash
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](https://docs.litellm.ai/docs/providers) 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`](sample_eval_result.yaml) in this repo for the format.

## License

Apache 2.0