MathematicsSaturated
Trust Score:
25

MATH

12,500 AMC/AIME-level competition problems with worked solutions — usually evaluated on its 500-problem MATH-500 subset, the reasoning-model math standard.

Launched: Refresh: static
Status Assessment (saturated):

MATH resisted far longer than GSM8K: GPT-4 managed only ~42% on the full set in 2023. But the reasoning-model era ended it — o1 (2024) reached ~94.8% on MATH-500 and later models sit in the 90s, so it no longer discriminates at the frontier. saturated_date 2024-09 marks the o1 inflection. As with GSM8K, saturated ≠ solved: exact-answer scoring hides whether the reasoning was valid, and headroom has moved to AIME and FrontierMath.

Performance Timeline

Longitudinal progression of model scores against human baselines.

Performance & Historical Trajectory

Empirical score progression across model release dates and evaluation rounds.

Independent Vendor-Reported Human Baseline (40.0%)
ModelScoreDateSource TypeProvenance
o1 (MATH-500)94.8%2024-09-12vendor-reportedSource ↗
GPT-4 (full MATH)42.5%2023-03-14vendor-reportedSource ↗
GPT-3 175B (full MATH)5.6%2021-03-05independentSource ↗

Human Baseline & Difficulty Horizon

Calibrated human reference points, specialist benchmarks, and ceiling thresholds.
Measured Human Score40.0%Domain Expert Baseline
Baseline Protocol & Interpretation

Average human score among elite high school math students across 7 AMC/AIME subdomains.

Metric & Scoring Methodology

Verification protocols, aggregation formulas, and specialized metric variants.
Primary Metric:accuracy on MATH-500 — math-equivalence match on the boxed answer (%)
Scoring Engine:exact-match

Dataset & Compute Cost

Evaluation volume, public availability, API pricing, and local hardware requirements.
Total Dataset Size500Annotated evaluation items
Public Test SetPublicOpenly mirrored on repositories
Access GatingOpen AccessUnrestricted download
Evaluation LicenseOpen AccessDataset usage and redistribution terms
Frontier API Compute Cost:

$5 – $20 USD for full benchmark evaluation run on frontier APIs.

Recommended Local GPU Setup:

1x NVIDIA RTX 4090 (24GB) or A100 (40GB/80GB) via vLLM / SGLang

Official Dataset & Benchmark Files:Download / View Dataset Repository ↗

How to Run & Reproduce

Standardized evaluation protocols, CLI commands, and reproducible runner templates.
Prompt Regimezero-shot
Reasoning Modedirect
Sampling Temp0
Pass@k Budgetk = 1
Tools & SandboxPure Text
Scoring Verifierexact-match
Option AEleutherAI LM-Evaluation-Harness (Open-Weight Models)
lm_eval --model hf --model_args pretrained=<model_path> --tasks math --batch_size auto
Option BOpenCompass Evaluation Framework
opencompass --datasets math --models <model_config>
Python APIDeterministic Inference Loop Snippet
# Standard API Evaluation Loop
from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.0,
)
Standardized Reporting Requirement:

When publishing results for MATH, always report the exact prompt template, few-shot exemplar ordering, sampling temperature (temperature=0), maximum reasoning budget tokens, and the precise timestamped model snapshot ID.

Contamination & Memorization Analysis

Audit of pretraining exposure risks, memorization vectors, and refresh policies.
Overall Contamination Risk:HIGH
Refresh Cadence:

Static fixed snapshot

Test Set Exposure:

Public on web / HuggingFace