claude-3-haiku-20240307 · Aider coding benchmarks · Refactoring · percent correct ↗
Anthropic / MODEL EVIDENCE
claude-3-haiku-20240307
5 benchmarks · 41 observations · 5 sources
Scores at a glance
5 benchmarks · large dot: claude-3-haiku-20240307; small dots: closest models where reported
claude-3-haiku-20240307 · RepoQA & LongCodeArena · RepoQA retrieval accuracy · similarity threshold 0.8 ↗
claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · 200-challenge full set ↗
6 more reported results
5.7692% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · crypto subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
0% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · forensics subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
8.3333% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · misc subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
2.5641% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · pwn subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
3.9216% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · rev subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
0% · claude-3-haiku-20240307 · NYU CTF Bench · pass@5 · web subset · NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
ForecastBench ↗54.8 Brier index
claude-3-haiku-20240307† · Overall difficulty-adjusted Brier index ↗
claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · LitQA2 ↗
32 more reported results
18% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SuppQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
23% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · FigQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
49% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · TableQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
22.6% · claude-3-haiku-20240307 · LAB-Bench · DbQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
45% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · ProtocolQA · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
45.1333% · claude-3-haiku-20240307 · LAB-Bench · SeqQA · subtask macro average · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
26% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · CloningScenarios · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
15% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_dga_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
18% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_gene_location_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
15% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_mirna_targets_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
48% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_mouse_tumor_gene_sets-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
24% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_oncogenic_signatures_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
11% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_tfbs_GTRD_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
22% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_variant_from_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
14% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_variant_multi_sequence_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
32% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_vax_response_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
27% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · DbQA_viral_ppi_task-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
17% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAid-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
57% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-AAseq-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
26% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_ORF-seq-numlen-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
47% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_ORF-transeff-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
61% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
57% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibshindprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
51% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-gene-gibssmaprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
83% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-geneprimers-enz-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
13% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-len-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
26% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-primers-len-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
86% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-enzprimers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
81% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_PCR-seq-primers-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
39% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_Prop-seq-gcpercent-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
21% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-lenfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
12% · claude-3-haiku-20240307 · LAB-Bench (v1) · Accuracy · SeqQA_RE-seq-numfrags-v1 · LAB-Bench: Measuring Capabilities of Language Models for Biology Research ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
claude-3-opus-20240229 →Anthropic
3 of 5 shared benchmark comparisons
How similarity is measured
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- Aider refactoring benchmark ↗
- RepoQA: Evaluating Long Context Code Understanding — published result table ↗
All 8 authors
Jiawei Liu, Jia Le Tian, Vijay Daita, Yuxiang Wei, Yifeng Ding, Yuhan Katherine Wang, Jun Yang, Lingming Zhang
- LAB-Bench: Measuring Capabilities of Language Models for Biology Research — published result table ↗
All 9 authors
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D. White, Samuel G. Rodriques
- Forecastbench — methodology and published results ↗
- NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security — official leaderboard ↗
All 13 authors
Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner, Boyuan Chen, Max Yin, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Muhammad Shafique
Names used in source tables
claude-3-haiku-20240307 · claude-3-haiku-20240307†