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.

21 September 2026
When Jev holds up as a judge

Jev was compared with DeepSeek V4 Flash, GPT-5.6 Luna, GPT-4.1 Mini, and Gemma 4B on 616 answer-correctness pairs from JudgeBench and 1,086 groundedness claims from LLM-AggreFact. Each judgment was scored against an objectively verified or human-authored reference label.

Groundedness
Jev0.0%
Luna direct0.0%
Luna + reasoning0.0%
GPT-4.1 Mini0.0%

1,086 claimshigher is better

Answer correctness
Luna + reasoning0.0%
Jev0.0%
Luna direct0.0%
GPT-4.1 Mini0.0%

616 independent pairshigher is better

9 September 2026
Moonshot vs Fireworks for Kimi K3 frontend agents

Kimi K3 ran through Moonshot and Fireworks in 30 direct latency requests per provider and 60 planned Figma-to-HTML runs per provider. Paired intervals compare matched designs.

Median time to first token
Fireworks
0.00s
Moonshot
0.00s

55% faster

Visual similarity
Fireworks0.000
Moonshot0.000
4 September 2026
Loop vs Braintrust MCP + Codex for production agent investigations

Loop and Braintrust MCP + Codex each ran five workflows on identical 200-trace projects. Each tool was scored against a handwritten rubric measuring how well it surfaced insights about the traces and cited supporting evidence so traceability could be verified.

Insight checks passed
Braintrust MCP + Codex0
Loop0

22 condition-neutral checks

Production analysis time
Loop
0m 0s
Braintrust MCP + Codex
0m 0s

39% faster

31 August 2026
You.com vs built-in web search
20 August 2026
Behavior scoring vs output scoring for coding agents
12 August 2026
Compare Kimi K3 and DeepSeek V4
7 August 2026
Testing whether language model harnesses transfer the wrong strategy
20 July 2026
Paper MCP vs Figma MCP for frontend agents
15 July 2026
How we chose the model behind Topics with Baseten

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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