RRepoGEO

REPOGEO REPORT · LITE

openai/frontier-evals

Default branch main · commit 51052ced · scanned 6/30/2026, 11:39:17 AM

GitHub: 1,231 stars · 168 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface openai/frontier-evals, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-evaluation, ai-benchmarking, frontier-models, large-language-models, agent-evaluation, software-engineering-evals, smart-contract-security, openai
  • highreadme#2
    Reposition the README's opening statement to highlight unique focus

    Why:

    CURRENT
    # Frontier Evals

Code for evals measuring frontier model capabilities.
    COPY-PASTE FIX
    # Frontier Evals

This repository provides a standardized framework for rigorous, adversarial, and safety-oriented evaluation of cutting-edge "frontier" AI models, particularly Large Language Models (LLMs), on complex real-world tasks. It includes specialized benchmarks like PaperBench for AI paper replication, SWE-Lancer for freelance software engineering, and EVMBench for smart contract security.
  • mediumreadme#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Other Evaluation Frameworks

Unlike general LLM evaluation frameworks such as LM-Harness or Hugging Face Evaluate, OpenAI Frontier Evals focuses on rigorous, often adversarial, and safety-oriented evaluation of cutting-edge "frontier" LLMs. Our benchmarks are designed for complex, real-world tasks, pushing the boundaries of model capabilities and identifying critical limitations.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface openai/frontier-evals
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
stanford-crfm/helm
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. stanford-crfm/helm · recommended 1×
  2. mlcommons/mlperf-inference · recommended 1×
  3. EleutherAI/lm-evaluation-harness · recommended 1×
  4. google/BIG-bench · recommended 1×
  5. openai/evals · recommended 1×
  • CATEGORY QUERY
    How to effectively benchmark frontier AI models for complex real-world tasks?
    you: not recommended
    AI recommended (in order):
    1. HELM (stanford-crfm/helm)
    2. MLPerf (mlcommons/mlperf-inference)
    3. EleutherAI's LM Evaluation Harness (EleutherAI/lm-evaluation-harness)
    4. Big Bench (google/BIG-bench)
    5. OpenAI Evals (openai/evals)
    6. Scale AI
    7. Appen
    8. IBM ART (Trusted-AI/adversarial-robustness-toolbox)
    9. CleverHans (cleverhans-lab/cleverhans)

    AI recommended 9 alternatives but never named openai/frontier-evals. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for tools to evaluate AI agents on software engineering and smart contract security challenges.
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MBPP
    3. SmartBug
    4. Echidna
    5. Slither
    6. Codeforces
    7. LeetCode
    8. OWASP Top 10

    AI recommended 8 alternatives but never named openai/frontier-evals. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of openai/frontier-evals?
    pass
    AI named openai/frontier-evals explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts openai/frontier-evals in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/frontier-evals explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo openai/frontier-evals solve, and who is the primary audience?
    pass
    AI named openai/frontier-evals explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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openai/frontier-evals — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite