Long-Horizon Agent Benchmark: Claude Opus 4.7 vs Kimi K2.6 vs DeepSeek V4 Pro on 50+ Step Tasks

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Architecture Diagram

Most evaluations reward short-answer quality. Agent systems fail later, after many tool calls, when state tracking and planning consistency become harder.

This benchmark emphasizes that long-horizon reality by comparing quality curves against tool-call depth across multiple task categories.

Why This Is Different

It is not only a leaderboard. It captures how quickly models plateau, where they degrade, and how cost and latency evolve as tasks become longer.

That makes the results useful for real agent architecture decisions, not just model branding comparisons.

Run the Project

git clone https://github.com/dakshjain-1616/long-horizon-agent-bench.git
cd long-horizon-agent-bench
make install
cp .env.example .env

lhb list-tasks
lhb benchmark -m opus-4.7 --judge openai/gpt-5.5 -o outputs/bench_opus
make plots
make dataset

README canonical CLI is lhb with Makefile helpers for plots and dataset export, which keeps benchmarking and reporting reproducible.

Architecture Walkthrough

The long horizon agent benchmark repository is organized around a clear pipeline, so you can trace the full flow from input handling to final output without guesswork. This makes onboarding easier for new contributors and helps teams debug faster when behavior changes after updates.

Practical Use Cases

If you are evaluating long horizon agent benchmark for production, start with a small real-world dataset, run the included commands end to end, and compare output quality, latency, and operational complexity. This gives a practical signal that is stronger than a toy demo.

Implementation Notes

The project is useful as both a standalone tool and a reference implementation. You can copy patterns from this codebase into your own stack, especially around evaluation discipline, reproducibility, and operator visibility.

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