LLM Benchmarking

Single-trial success masks the non-deterministic nature of autonomous AI agents. Learn how to benchmark agent reliability using Pass@k metrics, evaluate top models like Claude 3.5 Sonnet and DeepSeek-V3, and build resilient execution pipelines.

Comparing LLM API costs strictly by dollars per million tokens misses what production applications actually cost. Learn how to benchmark models based on cost per correct outcome, agent trajectory overhead, and rubric quality.

A deep dive into Kimi K3's reasoning capabilities and what the Pelican benchmark reveals about the current state of large language models and constrained instruction following.

A deep dive into evaluating the agentic capabilities of open models like DeepSeek-V3 and Llama 3.1 using custom tooling and rigorous benchmarking frameworks.

An in-depth look at how Anthropic's 2023 study used 'anticipated software' to forecast the theoretical capabilities of LLMs in the global job market.

A comprehensive guide to the EVA (Evaluating Voice Agents) framework, exploring how to measure latency, accuracy, and conversational flow in modern AI voice systems.

A deep dive into the performance of NVIDIA's Nemotron 3 Nano small language model, utilizing the NeMo Evaluator framework to establish a new open standard for efficient AI benchmarking.