Skip to main content

llm-code-benchmark

AI Cost Tracking

PyPI Version Python License AI Cost Human Time Model

  • 🤖 LLM usage: $2.7790 (45 commits)
  • 👤 Human dev: ~$1030 (10.3h @ $100/h, 30min dedup)

Generated on 2026-07-19 using openrouter/qwen/qwen3-coder-next


Private benchmark repository for selecting OpenRouter models for repair-agent and validator-agent.

The benchmark never stores API keys. Paid runs require OPENROUTER_API_KEY from the environment or GitHub Actions Secrets. Standard CI and catalog-smoke do not make paid model requests.

python -m pip install -e ".[test]"
python -m pytest tests -q
llm-code-benchmark catalog-smoke --max-models 2

Paid smoke benchmark, after explicit approval and budget setup:

llm-code-benchmark live-smoke --max-models 2 --max-tasks 2 --repetitions 1 --budget-usd 0.25

Recommended next smoke uses explicit models instead of router auto-selection:

llm-code-benchmark live-smoke --model meta-llama/llama-4-scout,deepseek/deepseek-v4-flash --max-models 2 --max-tasks 2 --repetitions 1 --budget-usd 0.25 --dry-run

Remove --dry-run only after explicit paid-run approval.

Reports are written to reports/latest/ and immutable snapshots under reports/history/. Repair schema validation is intentionally separate from semantic patch validation, so path mismatches and invalid diffs are reported as repair task statuses rather than JSON schema failures.

Repair Output Modes

Repair benchmark requests now prefer file_edits: the model returns repository-relative paths and complete final file contents, and the benchmark generates a deterministic unified diff locally before validation and git apply --check. Native patch output remains supported and is scored separately through native_patch_success_rate; structured edits are tracked through structured_edit_success_rate. search_replace, create, and delete are not enabled in this version.

License

Licensed under Apache-2.0.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llm_code_benchmark-0.2.2.tar.gz (55.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llm_code_benchmark-0.2.2-py3-none-any.whl (49.4 kB view details)

Uploaded Python 3

File details

Details for the file llm_code_benchmark-0.2.2.tar.gz.

File metadata

  • Download URL: llm_code_benchmark-0.2.2.tar.gz
  • Upload date:
  • Size: 55.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for llm_code_benchmark-0.2.2.tar.gz
Algorithm Hash digest
SHA256 3b85a8126c8fa6fbaea313a62c46cb15947974fd1d91136adc5aa2ac7ad47b13
MD5 03ac5d36bb017f71e2a9b323e231f4c2
BLAKE2b-256 a8d5642506fc7fb961c1ed0b58b8ea79aa9136a9b85dbd8561b2a25b376d7cf0

See more details on using hashes here.

File details

Details for the file llm_code_benchmark-0.2.2-py3-none-any.whl.

File metadata

File hashes

Hashes for llm_code_benchmark-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 ee76ce83bf942cfcc1c45c8530bdbb80767d35f3ef96d045bb545fff90d66301
MD5 46a97a203d07a757b1492da808d3daef
BLAKE2b-256 7c2b44aef1cb4be67f43cc1d89c19091b805bc50008ef0dba0dc3ce8f3e1de9d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page