General-purpose benchmarks like MMLU have hit a wall. Nearly every frontier model now scores above 90%, which compresses them into a band so narrow that the remaining differences fall within measurement error. And yet vendor marketing still leans on these numbers. A high MMLU score tells you the model ingested a lot of undergraduate-level text. It tells you nothing about whether it can diagnose a failing Kubernetes pod, detect anomalies in industrial sensor streams, or assess CIS compliance across a cloud environment.
Domain-specific benchmarks have emerged to fill that gap. They measure AI performance on tasks that actually matter in specific verticals — using realistic scenarios and expert-defined scoring rather than broad multiple-choice question sets. This article is part of our comprehensive AI benchmark governance series, where we explore the full landscape of evaluation failures and practical responses. For the foundational context on why general benchmarks break down across all dimensions — contamination, saturation, cherry-picking — that article is worth reading first.
When does a general benchmark score become meaningless for your specific use case?
Here’s the simple diagnostic: can you map the benchmark’s test categories directly to your deployment use case? If you can’t, the score is not a capability signal — it’s marketing noise.
MMLU spans 57 subjects from elementary mathematics to US history and computer science. That breadth made it useful for early model comparisons. It no longer discriminates between frontier models. Benchmark saturation is now a problem across every domain — general knowledge, reasoning, math, and coding — as scores compress into an indistinguishable band.
Data contamination makes it worse. Many models were trained on data that included the benchmark questions themselves. Retrieval-based audits report over 45% overlap on QA benchmarks, and GPT-4 infers masked MMLU answers in 57% of cases — well above chance. Those inflated scores reflect memorisation, not reasoning ability.
So when should you switch to domain-specific evaluation? When your use case involves specialised workflows, industry-specific terminology, or multi-step processes that general benchmarks were never designed to test. If your deployment is in IT operations, industrial asset management, or legal document analysis, MMLU’s scope was never relevant to your decision in the first place.
How do benchmark difficulty levels translate to real-world capability signals?
Before jumping to domain-specific alternatives, it’s worth asking whether harder general benchmarks close the gap. They don’t. Here’s why.
The benchmark difficulty progression runs MMLU → GPQA → HLE, each level escalating complexity.
MMLU tests broad undergraduate-level knowledge across 57 subjects — saturated, table stakes now. GPQA (Graduate-Level Google-Proof Questions and Answers) tests graduate-level expert reasoning in sciences and still discriminates between frontier models. HLE (Humanity’s Last Exam) pushes to frontier academic difficulty, with 2,500 questions across dozens of subjects where even strong models achieve relatively low accuracy.
But higher difficulty does not mean better relevance. If your deployment is supply chain management or IT operations, neither GPQA nor HLE tests what matters. A harder general benchmark raises the ceiling on general knowledge — it does not close the gap between benchmark domains and enterprise verticals. The right question is simple: does any general benchmark’s domain coverage actually match your production requirements?
What are domain-specific benchmarks and which verticals have them?
A domain-specific AI benchmark is an evaluation framework built around tasks specific to a particular industry. We’re talking realistic scenarios from actual production workflows, with success criteria defined by domain experts — not academic test designers.
The benchmark-reality gap is what drives their creation. Traditional benchmarks miss what actually matters to practitioners: not just accuracy, but practical utility, workflow integration, and whether the AI output is genuinely usable. Established verticals include industrial operations (AssetOpsBench), IT operations (ITBench), life sciences (Chan Zuckerberg Initiative), legal (LegalBenchmarks.ai), healthcare (MultiMedQA), and finance (FinBen). Evidently AI maintains a database of 250+ LLM benchmarks if you need a comprehensive starting reference.
Access is less restricted than you’d expect. AssetOpsBench is on Kaggle and Hugging Face. ITBench is on GitHub with a Kaggle leaderboard. Many domain-specific benchmarks are available through open platforms, not locked behind institutional access — and they’re increasingly part of how verticals hold model claims accountable. The broader AI benchmark governance framework explains why that matters.
How does IBM Research’s AssetOpsBench evaluate industrial AI agents differently?
AssetOpsBench is an IBM Research benchmark for AI agents in industrial asset environments — maintenance planning, anomaly detection in sensor streams, KPI forecasting, work order prioritisation. It covers 2.3 million sensor telemetry points, 140+ curated scenarios across four agents, 4,200 work orders, and 53 structured failure modes. That’s a serious dataset.
The evaluation approach is multi-dimensional rather than binary. AssetOpsBench scores agents across six qualitative dimensions: Task Completion, Retrieval Accuracy, Result Verification, Sequence Correctness, Clarity and Justification, and Hallucination rate. An agent that completes 80% of a maintenance workflow before failing at the final step is fundamentally different from one that fails immediately — yet binary scoring treats both as failures.
TrajFM (Trajectory Failure Mode analysis) analyses the full sequence of actions the agent took — extracting failure patterns, clustering them using embeddings, and surfacing interpretable summaries. Knowing where and why failures occur in the trajectory is far more useful than a binary outcome score.
Community results show the gap between general capability and domain readiness clearly. Across 300+ agents, GPT-4.1 achieved a planning score of 68.2 and execution score of 72.4 — and no model met the 85-point deployment readiness threshold. Task accuracy drops from 68% in single-agent workflows to 47% in multi-agent coordination. AssetOpsBench is accessible via the AssetOps Leaderboard on Kaggle and a HuggingFace Space Playground. Pair this with production evaluation tooling and you’ve got the full evaluation stack covered.
What is ITBench and what does it measure about IT automation capability?
ITBench is an IBM Research benchmark set for IT operations agents covering three domains: site reliability engineering (Kubernetes diagnostics), FinOps cost management (cloud cost anomaly detection), and compliance assessment (CIS benchmark compliance). These are the tasks enterprise IT teams deal with every single day.
The scenarios are built from real-world incidents — including one where a single bug led to 20% data loss. An SRE agent must recognise an alert, determine its provenance, and provide a fix. A compliance agent must understand a regulation, translate it into actionable code, find the relevant section of the software, and verify compliance. As Nick Fuller, IBM Research VP of AI and Automation, put it: “You need to build trust in the systems,” “It’s even harder when you don’t have yardsticks to measure against.”
ITBench is available on GitHub (IBM/itbench-sample-scenarios) with an associated ITBench Leaderboard on Kaggle — part of IBM’s approach of making domain-specific agentic benchmarks publicly accessible rather than proprietary.
How do I find domain-specific benchmarks for my vertical?
AssetOpsBench and ITBench are just two examples of a much broader category. Finding the right benchmark for your vertical follows a consistent process regardless of domain.
Start with the major open platforms. Search Hugging Face for benchmark datasets and leaderboards. Browse Kaggle for enterprise AI competitions — this is where IBM’s AssetOps and ITBench leaderboards sit. Check Papers with Code for benchmark results linked to published research. Search arXiv using your vertical name plus “benchmark” or “evaluation” — many domain-specific benchmarks start as research papers before becoming public tools. The Evidently AI benchmark database covers 250+ benchmarks and is a useful first check.
Industry consortia are the second place to look. The Chan Zuckerberg Initiative‘s biology benchmarking suite is a good example. Experts from 42 institutions found that AI model measurement in biology had been characterised by reproducibility challenges, biases, and a fragmented ecosystem. CZI’s response was a unified benchmarking suite, freely available as an open-source Python package.
If no established benchmark exists for your vertical, the LegalBenchmarks.ai model is worth replicating. 500+ legal and AI/ML professionals worldwide produced the first independent benchmark for AI performance on real-world contract drafting tasks — using two LLM judges per draft, with disagreements escalated to legal experts. For a custom build: define representative tasks from your production workflows, recruit domain experts to annotate expected outputs and scoring criteria, and combine an LLM-as-evaluator approach with human-in-the-loop review. The benchmark governance community model covers how to structure this sustainably.
When should your team contribute to a domain-specific benchmark community?
Contributing makes sense when your team has production experience in a vertical where evaluation standards are still immature. Your real-world task data and failure modes are exactly what makes benchmarks useful to others.
The spectrum runs from low to high effort. Submitting agent results to existing Kaggle leaderboards — AssetOpsBench or ITBench — requires minimal investment. Contributing test cases or evaluation criteria requires domain expertise but builds real credibility in the vertical.
Benchmark overfitting is the systemic problem contributions address. When a community aligns too tightly around a fixed set of tasks, developers optimise for benchmark success rather than domain relevance. Models that perform well on curated tests but fail to generalise are the outcome. Fresh contributions break that cycle — and teams that contribute gain early access to evaluation frameworks and real influence over what gets measured in their vertical.
FAQ
Is GPQA or HLE a good replacement for MMLU?
GPQA and HLE are harder than MMLU and still discriminate between frontier models, but they’re not replacements in any meaningful sense. For enterprise deployments in IT operations, industrial asset management, or legal analysis, neither GPQA nor HLE tests what matters. The domain coverage still doesn’t match your production requirements. You need a benchmark that tests the actual tasks your AI will perform.
What if there is no domain-specific benchmark for my vertical?
Build a custom evaluation. Define representative tasks from your actual production workflows, recruit domain experts to annotate expected outputs and define scoring criteria, and use an LLM-as-evaluator approach for scalable assessment. Combine this with human-in-the-loop review for tasks requiring specialist judgement. LegalBenchmarks.ai demonstrates this is achievable well outside major research institutions.
What does benchmark saturation mean in practice?
Multiple models now score above 90% accuracy on benchmarks like MMLU and GSM8K. The remaining performance differences fall within measurement error — the scores are effectively indistinguishable, yet vendors still cite them as differentiators. Once the discriminative signal is gone, the benchmark has stopped doing its job.
How does data contamination affect benchmark scores?
Data contamination happens when models are trained on data that includes the benchmark questions and answers. The model has effectively memorised the test rather than demonstrating reasoning ability. Retrieval-based audits report over 45% overlap on QA benchmarks, and GPT-4 infers masked MMLU answers in 57% of cases. The result is inflated scores that don’t reflect genuine capability.
Can I use AssetOpsBench or ITBench to test my own AI agents?
Yes. AssetOpsBench is accessible via the HuggingFace Space Playground (ibm-research/AssetOps-Bench), the AssetOps Leaderboard on Kaggle, and GitHub (IBM/AssetOpsBench). ITBench is on GitHub (IBM/itbench-sample-scenarios) with an ITBench Leaderboard on Kaggle. Both are designed for public participation.
What is TrajFM and why does it matter for AI agent evaluation?
TrajFM (Trajectory Failure Mode analysis) analyses the full sequence of actions an AI agent takes during a multi-step task rather than only scoring the final outcome. It identifies where and why failures occur. An agent that fails at step 9 of 10 is fundamentally different from one that fails at step 1 — binary scoring can’t capture that distinction, but TrajFM can.
How is a domain-specific benchmark different from a general-purpose one?
A general-purpose benchmark like MMLU tests broad knowledge across dozens of academic subjects. A domain-specific benchmark tests AI performance on tasks specific to a particular industry — industrial maintenance planning, IT operations diagnostics, legal document analysis — using realistic scenarios and expert-defined success criteria. The key difference is relevance: domain-specific benchmarks measure what actually matters for the deployment context.
Why did the Chan Zuckerberg Initiative build its own AI benchmarks?
Without unified evaluation methods, the same model produced different scores across laboratories due to implementation variations — forcing researchers to spend three weeks building evaluation pipelines for tasks that should take three hours. General-purpose benchmarks don’t test cell clustering, perturbation expression prediction, or cross-species disease label transfer. CZI built benchmarks that do.
What is benchmark overfitting and how does it affect model selection?
Benchmark overfitting occurs when AI models are optimised to score well on a benchmark rather than to perform well on the real-world tasks it represents. High benchmark scores can be misleading — a model tuned for MMLU performance may underperform on production tasks. Domain-specific benchmarks with regularly refreshed test sets and multi-dimensional scoring are more resistant to overfitting than static general-purpose benchmarks.
Domain-specific benchmarks are not a niche concern for researchers. They are the mechanism by which verticals hold AI vendors accountable for claims that general benchmarks can no longer substantiate. AssetOpsBench, ITBench, and CZI’s biology suite all exist because practitioners needed evaluation tools that matched their production reality — and built them when none existed. The same logic applies to your deployment context.
For a complete overview of the benchmark governance landscape — including the regulatory trajectory, community evaluation infrastructure, and internal governance frameworks — see our AI benchmark governance guide.
