George Cameron, co-founder of Artificial Analysis, explains empirical findings from benchmarking LLMs across intelligence and factual hallucination evaluations.
Assertion Not publicly verifiable
Models perform better in custom agent harnesses than native web chatbots
“And what's really interesting is that if you compare, for instance, Claude, 4.5 Opus using the Claude web chatbot, it performs worse than the model in our Agentic harness. And so in every case, the model performs better in our agentic harness than its web chat…”
Insight
Cameron: Models perform better with minimal tools than rigid frameworks
“I think where we're getting to is that these models have gotten smart enough, they've gotten better, better tools that they can perform better when just given a minimalist set of tools and let them run, let the model Control the agentic workflow rather than us…”
Assertion Supported
Cameron: Model performance correlates with total parameters, not active parameters
“We, in our benchmark, see a lot of performance correlated more with total parameters than active, and not that correlated with how sparse like the models are. Our accuracy benchmark is part of a omniscience. It's very correlated with total. It's not correlated…”
Assertion Contradicted
Frontier models are cheaper for agentic tasks because they require fewer turns
“Interestingly, in Tau Tau Two Bench Telecom, it's cheaper to run, you know, on a per token basis, more expensive models, like a GBD five, compared to some smaller open source models, because the some of the GBD five, for instance got to the answer faster. And …”
Opinion
George Cameron: Mixtral 8x7B transformed the landscape for serverless inference providers
“We had Mixtrel A times seven B and it was a key. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost.”
Insight
Cameron: Inference economics incentivize larger, sparser AI models over dense architectures
“It's, I think, less about total parameters in many cases when thinking about inference costs and more around number of active parameters, and so there's a bit of an incentive towards larger, sparser models.”