Data●●●Banger
We benchmarked 18 LLMs on OCR (7K+ calls) – cheaper models win
7,560 runs proving cheaper models beat expensive ones on production OCR tasks.
Big BrainSolve My Problem
TimoKerr
511mo ago
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Kitchen Rush: a benchmark for accurate AND fast native tool calling
0 starsPython
Kitchen Rush, Overcooked inspired LLM tool calling benchmark
by bombastic311·Jun 16, 2026·2 points·0 comments
AI Analysis
●●SolidBig BrainNiche Gem
Thinking time becomes game time lost — finally measures latency alongside accuracy.
Strengths
- •Latency-as-game-mechanic is genuinely novel compared to BFCL and ToolSandbox.
- •Fully deterministic runs with browser replay viewer for auditing model behavior.
- •Zero core dependencies, single KR score makes model comparison straightforward.
Weaknesses
- •Benchmark only — doesn't improve agents, just measures them.
- •Zero stars on GitHub suggests very early adoption and unproven utility.
Category
Target Audience
ML engineers evaluating real-time agent models
Similar To
BFCL · ToolSandbox · τ-bench
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