Claude Opus 4.7 vs GPT-5.5 vs DeepSeek V4 Pro: 13-Task Reasoning Benchmark

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Architecture Diagram

Reasoning benchmarks are often noisy when models grade themselves or when prompt sets are too shallow. This project addresses both by using an independent judge and a harder task mix.

The value is in transparent methodology: documented prompt suite, reproducible scripts, and explicit visualization outputs.

Practical Use

If you need to choose between expensive frontier models, this kind of benchmark gives signal on reliability, latency, and category-specific strengths instead of relying on single-score marketing claims.

Because the run flow is script-driven, teams can rerun with their own judge and compare deltas over time.

Run the Project

git clone https://github.com/dakshjain-1616/Claude-Opus-4.7-vs-GPT-5.5-vs-DeepSeek-V4-Pro-Reasoning-Benchmark
cd Claude-Opus-4.7-vs-GPT-5.5-vs-DeepSeek-V4-Pro-Reasoning-Benchmark
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
echo 'OPENROUTER_API_KEY=sk-or-...' > .env

python scripts/smoke_run.py
pytest --suite=hard -v
python scripts/report.py
python scripts/viz.py

README run path is pytest plus report scripts (not run_benchmark.py), and the commands above follow that baseline.

Architecture Walkthrough

The claude opus vs gpt55 vs deepseek v4 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 claude opus vs gpt55 vs deepseek v4 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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