Contamination MapText / LLM / Dataset
HumanEval
164 hand-written Python programming problems, each with a function signature, docstring, body and unit tests (7.7 tests per problem on average). Single split (test, 164 problems).
- Also known as
- HumanEval, OpenAI HumanEval, openai_humaneval
- Domain
- code
- License
- MIT
- Homepage
- github.com
Exposure by model
| Benchmark | Model | Level | Reason |
|---|---|---|---|
| Classic LLM benchmarks | LLM360 Amber | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | BLOOM | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude 3.5 Sonnet | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude 3.7 Sonnet | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude 3 (Opus, Sonnet, Haiku) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Opus 4 and Claude Sonnet 4 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Fable 5 / Claude Mythos 5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Mythos Preview | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Opus 4.5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Opus 5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Claude Sonnet 5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DCLM-7B-IT (DCLM-IT-7B) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DCLM-7B (DCLM-Baseline-7B) | Test data seen | a contamination analysis found test examples in the pre-training data (Via StarCoder data: Riddell et al. (2024) find 18.9% of HumanEval gold solutions (and 20.8% of MBPP) seen in the Python split of the Stack training data of StarCoderBase (60.40 GB searched), despite the Stack's string-matching decontamination.) [third-party study] |
| Classic LLM benchmarks | DeepSeek-R1-0528 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-R1-Distill | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-R1 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V3-0324 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V3.1 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V3.2-Exp | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V3.2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V4.1-Flash | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | DeepSeek-V4 (Pro / Flash) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemini 1.5 (Pro, Flash) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemini 2.5 (Pro, Flash) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemini 3 Pro | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemini 4 Argon | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemma 2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemma 3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemma 3n | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Gemma 4 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | GPT-4 | Test data seen | a contamination analysis found test examples in the pre-training data (25% of HumanEval problems matched GPT-4 pre-training data (whole benchmark checked); score on the non-contaminated subset was 65.58% vs 67.0% overall) |
| Classic LLM benchmarks | GPT-4o | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | GPT-5.2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | GPT-5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | GPT-6 Astra | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | gpt-oss | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | InternVL3.5 | Unknown | unknown for its base model Qwen3: training data not disclosed in enough detail |
| Classic LLM benchmarks | InternVL3 | Unknown | unknown for its base model Qwen2.5: training data not disclosed in enough detail |
| Classic LLM benchmarks | LLM360 K2-65B (K2 Diamond) | Test data seen | a contamination analysis found test examples in the pre-training data (Riddell et al. (2024): 12.2% of HumanEval gold solutions have a perfect (surface- or semantic-level) match in the Pile's GitHub split; CONDA 2024 contamination report also lists openai_humaneval as found (>0%) in EleutherAI/pile. (WIMBD's exact-match input-field check reports 0.00% for openai-humaneval in the Pile, Table 16.)) [third-party study] (via The Pile) |
| Classic LLM benchmarks | K2-Horizon | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | K2-V2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Llama 2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Llama 3.1 | Test data seen | a contamination analysis found test examples in the pre-training data (8-gram overlap gave contamination scores too high to estimate a performance gain, even at higher thresholds; percentage not reported (dash in Table 15).) |
| Classic LLM benchmarks | Llama 3.2-Vision (11B, 90B) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Llama 3.2 (1B, 3B) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Llama 3.3 70B | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Meta Llama 3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Llama 4 (Scout, Maverick) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | LLaVA-OneVision-1.5 | Unknown | unknown for its base model Qwen3: training data not disclosed in enough detail |
| Classic LLM benchmarks | LLaVA-OneVision | Unknown | unknown for its base model SigLIP: training data not disclosed in enough detail |
| Classic LLM benchmarks | Ministral 3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Mistral 7B | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Mistral Large 3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Mistral Medium 3.5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Mistral Small 4 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Mixtral 8x7B | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Molmo (0924) | Unknown | unknown for its base model OpenAI CLIP: training data not disclosed in enough detail |
| Classic LLM benchmarks | Molmo2 | Unknown | unknown for its base model Qwen3: training data not disclosed in enough detail |
| Classic LLM benchmarks | OpenAI o1 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | OpenAI o3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | OLMo (February 2024) | Test data seen | a contamination analysis found test examples in the pre-training data (WIMBD analysis by the Dolma authors: over 90% of the OpenAI HumanEval test set appears in Dolma documents (mostly via the GitHub code subset); only Paloma was decontaminated, not downstream benchmarks.) (via Dolma v1.5) |
| Classic LLM benchmarks | OLMo 2 (November 2024) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Olmo 3.1 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Olmo 3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Olmo Hybrid 7B | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-3.5 (mini / MoE) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-3 (mini / small / medium) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-4-mini-flash-reasoning | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-4-mini-reasoning | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-4-mini | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-4-reasoning / Phi-4-reasoning-plus | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Phi-4 | Not seen | the authors state its test portion was excluded from training |
| Classic LLM benchmarks | Phi-mini-MoE / Phi-tiny-MoE (SlimMoE) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Pixtral 12B | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Pixtral Large | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Pythia (deduplicated) | Test data seen | a contamination analysis found test examples in the pre-training data (Riddell et al. (2024): 12.2% of HumanEval gold solutions have a perfect (surface- or semantic-level) match in the Pile's GitHub split; CONDA 2024 contamination report also lists openai_humaneval as found (>0%) in EleutherAI/pile. (WIMBD's exact-match input-field check reports 0.00% for openai-humaneval in the Pile, Table 16.)) [third-party study] (via The Pile) |
| Classic LLM benchmarks | Pythia | Test data seen | a contamination analysis found test examples in the pre-training data (Riddell et al. (2024): 12.2% of HumanEval gold solutions have a perfect (surface- or semantic-level) match in the Pile's GitHub split; CONDA 2024 contamination report also lists openai_humaneval as found (>0%) in EleutherAI/pile. (WIMBD's exact-match input-field check reports 0.00% for openai-humaneval in the Pile, Table 16.)) [third-party study] (via The Pile) |
| Classic LLM benchmarks | Qwen2.5-VL | Unknown | unknown for its base model Qwen2.5: training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen2.5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3-2507 (Instruct-2507 / Thinking-2507) | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3-Next | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3-VL | Unknown | unknown for its base model Qwen3: training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3.5 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3.6 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3.8-Flash-Next | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3.8 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | Qwen3 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | SmolLM2 | Unknown | training data not disclosed in enough detail |
| Classic LLM benchmarks | SmolLM3 | Unknown | training data not disclosed in enough detail |
Notes
Copies found in public web data by third-party studies: Llama 1 pre-training corpus: Substantial share of HumanEval marked contaminated in the Llama 1 pre-training corpus, with significant estimated performance gain for Llama 1 models; percentages depend on metric/threshold (Figure 3a).; The Stack: 18.9% of HumanEval solutions have a perfect surface- or semantic-level match in The Stack (version used to train StarCoderBase).; The Stack: 31 HumanEval test problems (18.9%) have rephrased or verbatim counterparts in a 4G subset of The Stack.; StarCoder-Data: 26 HumanEval test problems (15.9%) have rephrased counterparts in a 2.4G subset of StarCoder-Data.; RedPajama-Data-1T: 14 HumanEval test problems (8.5%) have rephrased counterparts in a 16G sample of the RedPajama-Data-1T GitHub subset.; CodeAlpaca: 21 HumanEval test problems (12.8%) have rephrased counterparts in the GPT-3.5-generated CodeAlpaca-20K instruction data.
Sources
- Evaluating Large Language Models Trained on Code (arXiv v2) · paper · retrieved 2026-10-15
- openai/human-eval README · readme · retrieved 2026-10-15
- openai/openai_humaneval dataset card · dataset card · retrieved 2026-10-15