Top 3 AI Testing Platforms in 2025: Comparison between Maxim AI, Langfuse, and Braintrust

TL;DR

Advanced AI models currently solve less than 2% of problems in FrontierMath, a benchmark designed by expert mathematicians to test research-level mathematical reasoning. This represents a significant gap between current AI capabilities and human-level mathematical expertise. As AI systems approach this milestone, organizations must prepare with robust evaluation frameworks, continuous monitoring systems, and cross-functional collaboration tools to ensure reliable deployment of increasingly capable AI agents.

Current AI models excel at many tasks, from generating text to recognizing images. Yet when confronted with advanced mathematical reasoning, even the most sophisticated systems reveal fundamental limitations. FrontierMath, a new benchmark developed by Epoch AI in collaboration with over 60 mathematicians from leading institutions including MIT, Harvard, and UC Berkeley, exposes this gap with stark clarity.

The benchmark consists of hundreds of original, unpublished mathematics problems spanning most major branches of modern mathematics. These range from computationally intensive challenges in number theory and real analysis to abstract questions in algebraic geometry and category theory. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community.

This performance gap raises critical questions about AI's path toward artificial general intelligence (AGI) and what happens when systems finally cross this threshold. More importantly for organizations building AI applications today, it highlights the urgent need for comprehensive AI evaluation and observability infrastructure.

The Mathematical Reasoning Challenge: Why FrontierMath Matters

Beyond Traditional Benchmarks

Traditional AI benchmarks like GSM-8K and MATH have become saturated, with leading models achieving near-perfect scores. While leading AI models now achieve near-perfect scores on traditional benchmarks like GSM-8k and MATH, they solve less than 2% of FrontierMath problems, revealing a substantial gap between current AI capabilities and the collective prowess of the mathematics community.

This saturation problem creates a false sense of progress. When benchmarks become too easy, they fail to differentiate between genuinely capable systems and those that have simply memorized patterns from training data. FrontierMath addresses this by introducing problems that require genuine reasoning rather than pattern matching.

The Design Philosophy

FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing the risk of data contamination. This design choice is critical because data contamination—the inadvertent inclusion of test problems in training data—has become a significant challenge in AI evaluation.

The problems span four difficulty tiers:

• Tiers 1-3: Cover undergraduate through early graduate level mathematics
• Tier 4: Research-level mathematics that challenges even expert mathematicians

Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper-end questions, multiple days. This level of difficulty ensures the benchmark remains relevant as AI capabilities advance.

Expert Validation

The mathematical community has validated FrontierMath's difficulty. Fields Medalists Terence Tao, Timothy Gowers, and Richard Borcherds, along with International Mathematical Olympiad (IMO) coach Evan Chen, shared their thoughts on the challenge. Timothy Gowers noted that the problems "appear to be at a different level of difficulty from IMO problems."

This expert endorsement establishes FrontierMath as a legitimate measure of advanced reasoning capabilities, not just another academic exercise. The benchmark tests abilities that would qualify as genuine mathematical research—the kind of work that advances human knowledge.

Understanding the Performance Gap: What Current AI Cannot Do

The Limits of Pattern Recognition

Current AI systems, including large language models like GPT-4o and Gemini 1.5 Pro, operate primarily through pattern recognition. They excel at identifying patterns in vast datasets and applying those patterns to similar problems. However, mathematics, especially at the research level, requires precise, logical thinking, often over many steps.

Each step in a proof or solution builds on the one before it, meaning that a single error can render the entire solution incorrect. This sequential dependency exposes a fundamental limitation in how current AI systems reason. They struggle with:

• Multi-step reasoning: Maintaining logical consistency across dozens or hundreds of reasoning steps
• Creative insight: Discovering novel proof strategies that haven't appeared in training data
• Domain transfer: Applying techniques from one mathematical domain to solve problems in another
• Abstract thinking: Working with highly abstract mathematical concepts that lack concrete analogies

The Automation Paradox

Interestingly, AI systems scored high on easier math benchmarks like GSM8K and MATH—above 90 percent—but scored around 2 percent on the advanced problems. This creates what we might call an "automation paradox": AI can automate many routine tasks but struggles with the kind of deep, creative thinking that defines expertise.

For organizations building AI applications, this paradox has practical implications. It means that AI evaluation cannot rely on simple accuracy metrics. Teams need sophisticated evaluation frameworks that test reasoning capabilities across multiple dimensions.

The Role of Computation vs. Reasoning

Mathematical problems in FrontierMath aren't just computationally intensive—they require genuine mathematical insight. The benchmark incorporates problems with definitive, computable answers that can be verified using automated scripts. However, finding those answers requires more than computational power; it demands understanding mathematical structures and relationships.

This distinction matters because it suggests that scaling computation alone won't bridge the gap. Systems need new architectures and approaches to reasoning that go beyond today's transformer-based models.

The Path to AGI: What Crossing This Threshold Would Mean

Defining Artificial General Intelligence

Unlike artificial narrow intelligence (ANI), whose competence is confined to well‑defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming.

Artificial general intelligence (AGI) is a hypothetical stage in the development of machine learning (ML) in which an artificial intelligence (AI) system can match or exceed the cognitive abilities of human beings across any task. When AI systems can solve FrontierMath problems at human expert levels, we'll have strong evidence that AGI is approaching.

Timeline Uncertainties

Recent surveys of AI researchers give median forecasts ranging from the late 2020s to mid‑century, while still recording significant numbers who expect arrival much sooner—or never at all. The wide range in these predictions reflects genuine uncertainty about the technical path to AGI.

Some industry leaders project more aggressive timelines. In June 2024, the AI researcher Leopold Aschenbrenner, a former OpenAI employee, estimated AGI by 2027 to be "strikingly plausible". However, FrontierMath's results suggest significant technical hurdles remain.

Implications for AI Development

When AI systems can solve research-level mathematical problems, several implications follow:

Scientific acceleration: AI could contribute to cutting-edge research across multiple disciplines, from physics to biology to computer science itself. This would fundamentally change the pace of scientific discovery.

Economic transformation: The organizations that get it right now will be poised to win in the coming era. Companies that successfully integrate AGI-level systems into their operations will gain substantial competitive advantages.

New evaluation challenges: As AI capabilities increase, evaluation becomes more complex. Organizations need frameworks that can assess not just accuracy but also reasoning quality, safety, and alignment with human values.

Building Reliable AI Systems in the Pre-AGI Era

The Critical Role of AI Observability

Even with current AI capabilities, organizations face significant challenges in deploying reliable AI applications. AI observability has emerged as a critical discipline for understanding and improving AI system behavior in production.

Comprehensive observability requires:

• Distributed tracing: Track requests across complex AI agent systems to identify bottlenecks and failures
• Real-time monitoring: Detect quality issues and anomalies as they occur in production
• Automated evaluations: Run periodic quality checks on production data to catch regressions
• Root cause analysis: Understand why AI systems make particular decisions

Maxim's observability suite addresses these needs by providing teams with tools to track, debug, and resolve live quality issues with minimal user impact. The platform creates multiple repositories for production data that can be logged and analyzed using distributed tracing techniques.

Evaluation Frameworks for Complex Reasoning

As AI systems attempt more complex tasks, evaluation becomes increasingly important. Traditional metrics like accuracy or F1 score don't capture the nuances of reasoning quality. Organizations need evaluation frameworks that assess:

• Logical consistency: Does the system maintain consistent reasoning across multiple steps?
• Factual accuracy: Are claims grounded in reliable information?
• Reasoning transparency: Can we understand how the system arrived at its conclusions?
• Edge case handling: How does the system perform on unusual or ambiguous inputs?

Maxim's evaluation framework provides both machine and human evaluations to quantify improvements or regressions. Teams can access off-the-shelf evaluators through the evaluator store or create custom evaluators suited to specific application needs. This flexibility enables organizations to measure quality across multiple dimensions relevant to their use cases.

Simulation-Driven Development

Testing AI systems only in production is risky and expensive. AI simulation enables teams to test agents across hundreds of scenarios and user personas before deployment.

Maxim's simulation capabilities allow teams to:

• Simulate customer interactions across real-world scenarios and user personas
• Monitor agent responses at every step of a conversation
• Evaluate agents at a conversational level, analyzing the trajectory chosen and identifying failure points
• Re-run simulations from any step to reproduce issues and identify root causes

This simulation-driven approach reduces the risk of deploying undertested systems while accelerating development cycles.

The Importance of Cross-Functional Collaboration

Building reliable AI systems requires collaboration between multiple teams:

• AI engineers: Build and optimize models and agent systems
• Product managers: Define requirements and success metrics
• QA engineers: Design test scenarios and validate quality
• Data scientists: Analyze system behavior and identify improvement opportunities

Maxim's platform facilitates this collaboration by providing interfaces that work for both technical and non-technical users. While engineers can leverage high-performance SDKs in Python, TypeScript, Java, and Go, product teams can configure evaluations and create custom dashboards without writing code.

Preparing for Increasingly Capable AI Systems

Infrastructure Requirements

As AI capabilities advance toward AGI-level performance, infrastructure requirements will evolve. Organizations need:

Scalable compute: Systems that can handle increasing computational demands while managing costs effectively. Bifrost, Maxim's AI gateway, provides unified access to 12+ providers through a single OpenAI-compatible API, enabling automatic failover and load balancing across providers.

Robust monitoring: Comprehensive observability that tracks system behavior across multiple dimensions. This includes not just performance metrics but also quality indicators and safety measures.

Flexible experimentation: Rapid iteration capabilities that enable teams to test new models, prompts, and configurations quickly. Maxim's Playground++ supports prompt versioning, deployment with different variables, and side-by-side comparisons of quality, cost, and latency.

Data management: Seamless data curation for evaluation and fine-tuning. As systems become more capable, the quality of training and evaluation data becomes increasingly important.

Governance and Safety Considerations

Consider the ethical and security implications. This should include addressing cybersecurity, data privacy, and algorithm bias. As AI systems become more capable, governance frameworks become more critical.

Organizations should implement:

• Access controls: Fine-grained permissions that limit who can deploy or modify AI systems
• Audit trails: Comprehensive logging of system decisions and changes
• Budget management: Cost controls that prevent unexpected expenses from AI usage
• Safety evaluations: Regular assessments of potential harms or misuse scenarios

Bifrost includes built-in governance features such as usage tracking, rate limiting, and hierarchical budget management with virtual keys and team controls.

Continuous Learning and Adaptation

The path to AGI isn't a single leap but a series of incremental improvements. Organizations need systems that support continuous learning:

• Human-in-the-loop feedback: Mechanisms for collecting expert judgments on AI outputs
• Automated quality monitoring: Systems that detect regressions without manual review
• Iterative improvement cycles: Workflows that incorporate learnings back into model development
• Performance tracking: Longitudinal analysis of how system capabilities evolve over time

Maxim's data engine enables teams to continuously curate and enrich datasets from production data, incorporating human feedback and evolving evaluation criteria as systems improve.

The Human Element

Continue to place humans at the center. Invest in human–machine interfaces, or "human in the loop" technologies that augment human intelligence. As AI systems become more capable, the human role shifts from performing tasks to guiding and validating AI outputs.

This shift requires:

• Training programs: Help team members develop skills for working effectively with AI systems
• Interface design: Create tools that make AI outputs transparent and actionable
• Decision frameworks: Establish clear guidelines for when human judgment should override AI recommendations
• Cultural adaptation: Build organizational cultures that embrace AI as an augmentation tool rather than a replacement

The Broader Implications of Advanced AI Reasoning

Transforming Knowledge Work

When AI systems can perform research-level reasoning, the nature of knowledge work changes fundamentally. Rather than replacing human experts, advanced AI would:

• Accelerate research: Explore hypotheses and test theories at unprecedented speed
• Augment creativity: Generate novel approaches to problems that humans might not consider
• Democratize expertise: Make expert-level analysis accessible to non-specialists
• Scale collaboration: Enable coordination of insights across domains and disciplines

These changes will require new workflows, tools, and organizational structures.

Economic and Social Considerations

The timing of AGI's emergence is uncertain. But when it does arrive—and it likely will at some point—it's going to be a very big deal for every aspect of our lives, businesses, and societies.

Organizations should consider:

• Workforce implications: How roles and skills requirements will evolve
• Competitive dynamics: How AI capabilities will reshape industry competition
• Value creation: Where human judgment adds unique value that AI cannot replicate
• Societal impact: Broader effects on employment, education, and social structures

Maintaining Trust and Reliability

As AI systems become more capable, maintaining user trust becomes more challenging and more important. Users need confidence that:

• AI recommendations are based on sound reasoning
• Systems behave consistently and predictably
• Errors and limitations are clearly communicated
• Human oversight remains in place for critical decisions

Building this trust requires transparent evaluation practices, comprehensive testing, and ongoing monitoring. Organizations that establish strong trust through reliable AI deployments will be better positioned as capabilities advance.

Conclusion

FrontierMath reveals that current AI systems, despite impressive capabilities in many domains, still fall far short of human-level mathematical reasoning. The less than 2% success rate on these research-level problems represents not just a technical challenge but a fundamental gap in how AI systems think and reason.

This gap should not discourage AI development but rather inform it. Organizations building AI applications today must recognize that even as capabilities improve, robust evaluation, comprehensive observability, and human oversight remain essential. The path to AGI requires not just more powerful models but better infrastructure for testing, monitoring, and improving AI systems.

As AI systems advance toward expert-level reasoning capabilities, the organizations best positioned to benefit will be those that have invested in comprehensive AI quality management. This means implementing evaluation frameworks that go beyond simple accuracy metrics, establishing observability systems that provide deep insights into AI behavior, and building cross-functional workflows that enable rapid iteration and improvement.

The question isn't whether AI will eventually pass "humanity's last exam"—most experts believe it will. The question is whether organizations are building the infrastructure, processes, and culture needed to deploy increasingly capable AI systems reliably and responsibly.

Ready to build reliable AI systems that scale? Get started with Maxim AI to implement comprehensive evaluation, simulation, and observability for your AI applications.

Frequently Asked Questions

What is FrontierMath and why does it matter for AI development?

FrontierMath is a benchmark consisting of hundreds of original, research-level mathematics problems created by expert mathematicians from institutions like MIT, Harvard, and UC Berkeley. Current state-of-the-art AI models solve less than 2% of these problems, revealing a significant gap between AI capabilities and human expert performance. The benchmark matters because it provides an objective measure of advanced reasoning capabilities that existing benchmarks no longer capture, as models have saturated traditional tests like GSM-8K and MATH with near-perfect scores.

How close are we to achieving artificial general intelligence?

Estimates vary widely among experts. Recent surveys of AI researchers give median forecasts ranging from the late 2020s to mid-century for achieving AGI. Some industry leaders like Leopold Aschenbrenner have suggested 2027 as "strikingly plausible," while others believe it remains decades away. The FrontierMath results suggest significant technical hurdles remain, as even advanced models struggle with research-level reasoning tasks that require genuine mathematical insight rather than pattern matching.

What evaluation methods should organizations use for complex AI systems?

Organizations should implement multi-dimensional evaluation frameworks that assess logical consistency, factual accuracy, reasoning transparency, and edge case handling—not just simple accuracy metrics. This includes combining automated evaluations using custom and off-the-shelf evaluators, human-in-the-loop assessments for nuanced quality checks, simulation-based testing across diverse scenarios, and continuous monitoring of production systems. Platforms like Maxim provide comprehensive evaluation capabilities that span experimentation, simulation, and observability phases.

Why is AI observability important as systems become more capable?

As AI systems tackle more complex tasks, understanding their behavior becomes increasingly critical for maintaining reliability and trust. AI observability enables teams to track requests across complex agent systems using distributed tracing, detect quality issues in real-time, run automated evaluations on production data, and perform root cause analysis when problems occur. Without comprehensive observability, organizations deploy AI systems blind to potential issues, risking user trust and business impact.

How can teams prepare for more advanced AI capabilities?

Teams should invest in scalable infrastructure, including AI gateways for unified provider access, robust monitoring and observability systems, flexible experimentation platforms, and comprehensive data management for evaluation and fine-tuning. Organizations must also implement governance frameworks addressing security, privacy, and bias, establish cross-functional collaboration workflows between engineering and product teams, and develop human-in-the-loop processes that augment rather than replace human judgment. Building these foundations now positions organizations to leverage more capable AI systems as they emerge.

What makes mathematical reasoning particularly challenging for current AI?

Mathematics requires precise, sequential reasoning where each step builds on previous ones, and a single error invalidates the entire solution. Current AI systems excel at pattern recognition but struggle with multi-step reasoning requiring dozens or hundreds of logically consistent steps, creative insight to discover novel proof strategies, domain transfer applying techniques across different mathematical areas, and abstract thinking with concepts lacking concrete analogies. This reveals fundamental limitations in how current architectures process information and maintain logical consistency.

How does simulation improve AI agent development?

Simulation enables teams to test AI agents across hundreds of scenarios and user personas before production deployment, significantly reducing risk and accelerating development cycles. Teams can simulate customer interactions, monitoring agent responses at every conversational step, evaluate agents at a conversational level, analyzing chosen trajectories and identifying failure points, re-run simulations from any step to reproduce and debug issues, and apply learnings systematically to improve agent performance. This simulation-driven approach catches issues in controlled environments rather than discovering them through user-facing failures.