Benchmarks

OpenAI released three API audio models on March 20, 2025: gpt-4o-transcribe, gpt-4o-mini-transcribe, and gpt-4o-mini-tts. The post says the STT models beat Whisper v2 and v3 on FLEURS and other benchmarks across 100+ languages, while the TTS model adds style control but stays limited to monitored preset synthetic voices. The key shift is controllable TTS plus lower WER; the post does not disclose pricing or latency figures.

OpenAI reports that o1-preview and o1-mini often drive adversarial attack success rates close to zero as inference-time compute increases. The paper tests math tasks, SimpleQA prompt injection, Attack Bard images, and StrongREJECT misuse prompts; it labels the result as preliminary, and the truncated post does not fully disclose all failure cases. The key point is that this gain comes from longer reasoning at inference, not adversarial training.

OpenAI launched ChatGPT Pro at $200 per month, with unlimited access to OpenAI o1, o1-mini, GPT-4o, Advanced Voice, and a higher-compute o1 pro mode. The post specifies a stricter 4/4 reliability metric, where a question counts only if the model answers correctly in all four attempts, but it does not disclose concrete quotas or latency figures. The key signal is compute tiering: longer reasoning time is now a paid product feature.

OpenAI published 2 papers on Nov 21, 2024, outlining its external human red teaming process and a new automated red teaming method. The post discloses 3 concrete design choices for external testing—threat-model-based team selection, versioned model access, and structured feedback via API or ChatGPT interfaces—but this excerpt does not fully disclose the automated method's metrics or results.

OpenAI open-sourced SimpleQA, a 4,326-question benchmark for factual short-answer QA and model calibration. Two independent AI trainers verified each item; a 1,000-question audit showed 94.4% agreement and an estimated inherent error rate near 3%. The key signal: it is built to challenge frontier models, and the post says GPT-4o scores below 40%.

OpenAI introduced sCM and scaled continuous-time consistency models to 1.5B parameters on ImageNet at 512×512. The post says sCM reaches sample quality comparable to leading diffusion models in 2 sampling steps, with about 50x wall-clock speedup. Its largest model generates one sample in 0.11s on a single A100 at batch size 1 without inference optimization.

OpenAI analyzed millions of ChatGPT requests to test whether user names trigger harmful stereotypes, finding an overall rate of about 0.1%. The study used GPT-4o as a privacy-preserving evaluator; its gender-related judgments matched human raters over 90% of the time, while race and ethnicity agreement was lower. The key signal is model drift across versions: GPT-3.5 Turbo showed the highest task-level bias.

OpenAI released MLE-bench, a benchmark built from 75 Kaggle competitions to measure ML engineering ability in AI agents. The best setup, o1-preview with AIDE scaffolding, reached at least Kaggle bronze-medal level on 16.9% of tasks; the benchmark code is open-source.

OpenAI launched an API distillation workflow on October 1, 2024, letting developers use outputs from GPT-4o and o1-preview to fine-tune cheaper models such as GPT-4o mini. The suite includes Stored Completions, Evals in beta, and fine-tuning; setting store:true auto-saves input-output pairs with no added latency, per the post. Pricing includes 2M free GPT-4o mini training tokens per day and 1M for GPT-4o through October 31; Evals are free up to 7 runs per week through year-end if shared with OpenAI.

OpenAI released o1-preview and reported 74% single-sample accuracy on AIME 2024, versus 12% for GPT-4o. The post says o1 reached the 89th percentile on Codeforces and exceeded human PhD experts on GPQA Diamond; it attributes this to large-scale RL and gains from both train-time and test-time compute. The key signal is scaling reasoning with compute, not just pretraining a larger base model.

OpenAI has opened GPT-4o fine-tuning to developers on all paid tiers, with 1M free training tokens per org per day through September 23. Training costs $25 per 1M tokens, and inference costs $3.75 per 1M input tokens and $15 per 1M output tokens on gpt-4o-2024-08-06. The signal for practitioners: partners reported 43.8% on SWE-bench Verified and 71.83% on BIRD-SQL with fine-tuned GPT-4o.

OpenAI released SWE-bench Verified, a human-validated subset built with the benchmark’s authors to assess real software issue resolution more reliably. The post names 3 failure modes in SWE-bench: overly narrow tests, underspecified issue statements, and unreliable environment setup; as of Aug. 5, 2024, top agents scored about 20% on SWE-bench and 43% on SWE-bench Lite. The key point is that the original benchmark can systematically underestimate coding-agent ability.

OpenAI trained GPT-4-family prover-verifier games so stronger models write solutions weaker models can verify; under time-limited human review, correctness-only optimization led to nearly 2x more evaluation errors. The post says the large and small models differ by about 3 orders of magnitude in pretraining compute, and checkability training recovers about half the performance gain of correctness-only optimization; the full experimental numbers are not fully disclosed in the provided text.