Benchmark Almanac

MakeMePay

GPT-4.5 57% leads since Feb 2025 Only in system cards

A con artist model tries to talk a GPT-4o mark into paying money in a role-play. Scored as the share of conversations with a payment.

Open source in OpenAI Evals. Cards also report the dollar extraction rate.

Made by OpenAI Released Sep 2023 Dangerous capabilities All tasks public Last in a system card Feb 2025 Last model added Feb 2025
Level
Everyday Most adults, without training
Scored
Settled by events Predictions are scored against what later happens.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
6
Models scored in lab cards
6
System cards report it, from 1 lab

GPT-4.5 leads with 57% among models released by Feb 2025

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

The newest model with a score here came out in Feb 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:

  1. 1 GPT-4.5 57%
  2. 2 OpenAI deep research 36%
  3. 3 o1 27%
  4. 4 o1-preview 11.6%
  5. 5 o3-mini 1.00%
  6. 6 o1-mini 0.70%
#ModelScoreSettingDateSource
1 GPT-4.5 57% Feb 2025 Model card
2 OpenAI deep research 36% Feb 2025 Model card
3 o1 27% Dec 2024 Model card
4 o1-preview 11.6% Sep 2024 Model card
5 o3-mini 1.00% Jan 2025 Model card
6 o1-mini 0.70% Sep 2024 Model card

4 models have held number one since its release in Sep 2023

Lab claims count from the day the card came out, independent runs from the model’s release.

OpenAI
Lab claims Not tested since Feb 2025
GPT-4.5 57%

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2025 Near the ceiling (85%) GPT-4.5 OpenAI deep research o1 o1-preview 0% 20% 40% 60% 80% 100% 2025 Near the ceiling (85%) GPT-4.5 OpenAI deep research

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

6 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
27 Feb 2025 GPT-4.5 system card 57 MakeMePay vs GPT-4o, % of times con-artist model received payment, post-mitigation, dollar extraction rate 8% (4.5.1) Persuasion
25 Feb 2025 OpenAI deep research system card 36 MakeMePay vs GPT-4o, % of times con-artist model received payment, post-mitigation without browsing (dollar extraction 21%) (3.4.6) Persuasion
31 Jan 2025 o3-mini system card 1 MakeMePay vs GPT-4o, % of times con-artist model received payment, o3-mini (Post), dollar extraction 0%, printed chart label (5.6.3) Persuasion
5 Dec 2024 o1 system card 27 MakeMePay vs GPT-4o, % of times con-artist model received payment, o1 (post-mitigation) (dollar extraction 4%) (5.7.3) Persuasion
12 Sep 2024 o1-preview system card 11.6 MakeMePay, % of times con-artist model received a donation from GPT-4o, o1-preview (post-mitigation) (dollar extraction 3.2%) (4.4.3) Persuasion
12 Sep 2024 o1-mini system card 0.7 MakeMePay, % of times con-artist model received a donation from GPT-4o, o1-mini (post-mitigation) (dollar extraction 0.1%) (4.4.3) Persuasion

More in Safety and alignment

All 444 in Safety and alignment
  • FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
  • StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is. Inkling 98.6%
  • AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
  • MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy. Muse Spark Open 98.1%
  • Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. GPT-6 Astra 8.50%
  • Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%