Independent eval research. Open methodology, published datasets, and the tools to run your own studies.

Original, open-source studies

Model comparisons, agent setups, cost, and modalities, measured on real tasks. Read the methodology and results, or rerun a study on your own data.

20 August 2026
Behavior scoring vs output scoring for coding agents

Each enforcement variant was scored 2 ways. First by whether they adhered to that behavior, and second by the number of tests passed. We found that the agent could have leaky behavior that wasn't surfaced by output-only scoring.

Behavior adherence
Lockdown0%
Vanilla0%
Flag0%

30 tasksdeterministic detector

Output pass rate
Vanilla0%
Flag0%
Lockdown0%

test_passed on the same 30 tasks

20 July 2026
Paper MCP vs Figma MCP for frontend agents

A coding agent rebuilt 27 complex designs through each MCP server, three trials each. Visual similarity scores CLIP embedding distance to the reference screenshot, and faithfulness comes from a claude-sonnet-5 judge.

Visual similaritycomplex designs
Paper0.000
Figma0.000

0–1 CLIP score0.93 practical ceiling

Faithfulnessjudge score
Paper0.000
Figma0.000

0–1complex designs

10 July 2026
Evaluating the GPT-5.6 family

225 procedurally generated tasks span arithmetic, symbolic rules, and data transforms, graded by exact match in code with no partial credit. Each model runs every task three times.

Overall solve rate
Sol0%
Terra0%
Luna0%
Sonnet 50%
Opus 4.80%
Fable0%

225 code-graded taskshigher is better

12 August 2026
Compare Kimi K3 and DeepSeek V4
7 August 2026
Testing whether language model harnesses transfer the wrong strategy
15 July 2026
How we chose the model behind Topics with Baseten
9 July 2026
Evaluating speech-to-text models
6 July 2026
Evaluating the USA vs Belgium World Cup matchup
2 July 2026
From World Cup matchups to research maps: evaluating Parallel's web research agents

Run evals like us

Each skill packages best practices from leading AI teams and researchers into a plain SKILL.md file your coding agent can read. Use them to get started running evals on your data.

Claude Code
Codex
Cursor
Gemini CLI
GitHub Copilot
opencode
Browse the repo

Point your agent at a skill

Ask it to do what the skill describes, like validating a scorer against human labels or finding hidden failure modes.

gh skill install braintrustdata/eval-library braintrust-validate-eval-scorer
skills/braintrust-validate-eval-scorer/SKILL.md
---
name: braintrust-validate-eval-scorer
description: Validate automated eval scorers and LLM judges against expert-reviewed reference data.
---
# Validate the scorer
  1. 1.Name the reference tier before computing anything.
  2. 2.Verify alignment: scorer outputs and reference labels must line up at the item and criterion level.
  3. 3.Report agreement (κ or α) with uncertainty, not raw accuracy.
  4. 4.Lead with the most decision-relevant false acceptance before any aggregate, and enumerate the dangerous cells case by case.
  5. 5.Break errors down by class and severity.
  6. 6.Test sensitivity and shortcuts: inject known regressions and improvements and confirm the scorer moves; probe whether length, confidence, or polish raise the score independent of quality; probe whether text addressed to the judge moves it; slice agreement by subgroup.

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