# JudgeLM-7B-v1.0 (JudgeBench) Benchmark Scores & Performance > JudgeLM-7B-v1.0 (JudgeBench) has published results in 4 original benchmark tables. The evaluated configuration, source metrics, and precision remain visible below. These results do not produce a general model score or rank. Canonical page: https://benchlm.ai/models/native-judgebench-judgelm-7b-v1-0 Last updated: 2026-10-01 General benchmark catalog last updated: October 1, 2026. This profile’s source review has its own date above. ## Model Details | Property | Value | |----------|-------| | Creator | BAAI | | Source Type | Open Weight | | Reasoning Type | Unspecified | | Context Window | Not established by evaluation | | Official model card | [Checkpoint identified by JudgeBench](https://huggingface.co/BAAI/JudgeLM-7B-v1.0) | | Overall Score | Not computed (source protocol results only) | | Overall Rank | Unranked | ## Family & Coverage - Family: JudgeLM-7B-v1.0 (JudgeBench) - Variant: benchmark-system - Benchmarks covered: 0 of 645 - Coverage note: Original benchmark result tables appear below; these metrics are separate from weighted benchmark slots. ## Original benchmark results [All model results (JSON)](/api/data/benchmarks?model=native-judgebench-judgelm-7b-v1-0) · [Numeric metrics (CSV)](/api/data/benchmarks?model=native-judgebench-judgelm-7b-v1-0&format=csv) ### Maintainer results on GPT-4o response pairs JudgeBench’s paper and maintained leaderboard report judges under different prompts, reasoning efforts, and response-model splits. Accuracy is a percentage. The paper’s 700-pair GPT-4o split is distinct from the 270-pair Claude split. The app appends the same Nemotron CSV to both tabs without specifying a response split; we show that supplement once with its split unresolved. Live one-decimal values and paper two-decimal values are preserved separately. GPT-4o-2024-05-13 response split. The public app rounds to one decimal; paper tables retain two decimals. JudgeBench: A Benchmark for Evaluating LLM-Based Judges — Sijun Tan and colleagues. Numeric results transcribed and reformatted; evaluation notes and model links added by BenchLM. Published numeric precision is retained. [Published source](https://huggingface.co/spaces/ScalerLab/JudgeBench) · [Full JudgeBench results](/benchmarks/judgebench) | Judge | Type | Knowledge (%) | Reasoning (%) | Math (%) | Coding (%) | Overall (%) | | --- | --- | --- | --- | --- | --- | --- | | JudgeLM-7B-v1.0 | Fine-Tuned Judge | 23.4 | 29.6 | 32.1 | 11.9 | 25.1 | ### Paper: judge methods JudgeBench’s paper and maintained leaderboard report judges under different prompts, reasoning efforts, and response-model splits. Accuracy is a percentage. The paper’s 700-pair GPT-4o split is distinct from the 270-pair Claude split. The app appends the same Nemotron CSV to both tabs without specifying a response split; we show that supplement once with its split unresolved. Live one-decimal values and paper two-decimal values are preserved separately. Paper v2; source group labels and published precision are retained. JudgeBench: A Benchmark for Evaluating LLM-Based Judges — Sijun Tan and colleagues. Numeric results transcribed and reformatted; evaluation notes and model links added by BenchLM. Published numeric precision is retained. [Published source](https://arxiv.org/abs/2410.12784v2) · [Full JudgeBench results](/benchmarks/judgebench) | Judge | Knowledge (%) | Reasoning (%) | Math (%) | Coding (%) | Overall (%) | | --- | --- | --- | --- | --- | --- | | JudgeLM-7B | 23.38 | 29.59 | 32.14 | 11.90 | 25.14 | ### Paper: judgment outcomes JudgeBench’s paper and maintained leaderboard report judges under different prompts, reasoning efforts, and response-model splits. Accuracy is a percentage. The paper’s 700-pair GPT-4o split is distinct from the 270-pair Claude split. The app appends the same Nemotron CSV to both tabs without specifying a response split; we show that supplement once with its split unresolved. Live one-decimal values and paper two-decimal values are preserved separately. Paper v2; source group labels and published precision are retained. JudgeBench: A Benchmark for Evaluating LLM-Based Judges — Sijun Tan and colleagues. Numeric results transcribed and reformatted; evaluation notes and model links added by BenchLM. Published numeric precision is retained. [Published source](https://arxiv.org/abs/2410.12784v2) · [Full JudgeBench results](/benchmarks/judgebench) | Judge | A>B count | A