Research
A live-market benchmark of LLM trading agents across GPT-4o, GPT-4.1, Claude Sonnet 4 and Gemini 2.0 Flash found that agent architecture, not the underlying model, is what determines whether a trading agent behaves aggressively or conservatively
Agent Market Arena, presented at the ACM Web Conference 2026, ran four distinct agent frameworks across five LLM backbones in live crypto and stock markets and found behavioural style tracked the framework's design, not which model was powering it.
This finding runs directly against the assumption baked into most vendor comparisons in this space, that swapping in a stronger frontier model produces a better or more consistent trading agent. Agent Market Arena's result says the opposite: put the same reasoning model, say Claude Sonnet 4, inside a single-agent framework versus a memory-based multi-agent framework, and you get meaningfully different risk-taking behaviour, aggressive in one, conservative in the other, regardless of which model is doing the underlying reasoning. The framework is the product; the model is closer to an interchangeable component.
That has an immediate practical consequence for anyone building or evaluating a trading agent: benchmarking 'which LLM is best for trading' is close to the wrong question. A fairer comparison holds the agent architecture constant and varies the model, or vice versa, but conflating the two, which is what most public leaderboards and vendor demos currently do, produces conclusions that will not transfer once you swap either half of the pairing. A firm choosing between GPT-4.1 and Gemini 2.0 Flash for a trading agent should be asking which of their own orchestration frameworks each model performs best inside, not which model wins some generic benchmark.
The live-market design matters here too, not just the finding. Backtested benchmarks let an agent's mistakes get evaluated after the fact against data the model may have partially memorised or that carries no execution risk. Running four frameworks across five models in live crypto and stock markets simultaneously means the agents were making decisions under the same real-time information constraints and slippage a production system would face, which is a meaningfully harder and more honest test than the backtest-heavy literature this field has mostly produced until now.
The caution worth sitting with is that 'architecture matters more than model' is itself a claim about four specific frameworks and five specific models as of mid-2026, not a law of nature. Both the frontier model layer and the agent-orchestration layer are moving quickly enough that this ordering could invert within a year if model reasoning improves faster than framework design does. Treat the result as evidence to weight framework selection heavily today, not as a permanent ranking of what matters in agentic trading system design.
Read the original: ACM Web Conference 2026 (WWW '26) - A live-market benchmark of LLM trading agents across GPT-4o, GPT-4.1, Claude Sonnet 4 and Gemini 2.0 Flash found that agent architecture, not the underlying model, is what determines whether a trading agent behaves aggressively or conservatively. Commentary is the independent editorial view of Share Trading; the original article is credited to its publisher.