Frontier Model Comparison
Model comparison requires a documented information, tool, portfolio, and market configuration. Trading then exposes behavioral differences that ordinary chat benchmarks cannot reveal.
These seven tests isolate the capabilities that matter most for autonomous trading: evidence synthesis, state fidelity, tool discipline, portfolio reasoning, risk response, adaptation, and efficiency. BotTrade supplies the common experimental environment.
Give DeepSeek, Claude, and GPT a typed market observation plus a bounded research packet. Require each to separate facts, calculations, assumptions, thesis, and invalidation conditions before producing an action. Submit the decisions through BotTrade and inspect whether later trades reflect the cited evidence. A polished explanation that never changes the portfolio should score poorly on this test.
Run a long sequence containing buys, partial exits, holds, and changing cash. On every turn, require the model to restate the relevant current positions and post-trade exposure from the BotTrade observation. Count impossible orders, repeated exits, and rationale that refers to stale holdings. Authoritative portfolio state must come from the tool response rather than conversational memory.
Expose scenario discovery, scanning, symbol inspection, decision submission, and results with documented schemas for each model configuration. Measure invalid arguments, redundant calls, unnecessary deep inspection, and failures to complete the loop. BotTrade's MCP server provides the concrete tool surface. Reliability includes valid calls and the ability to gather only the evidence required for a decision.
Present opportunities with different volatility, conviction, and correlation while the portfolio already contains exposure. Require the model to propose quantities and explain post-trade concentration and cash. A deterministic risk layer should validate every proposal before BotTrade submission. Compare approved sizes, drawdown, and liquidation state to see whether verbal confidence becomes a defensible allocation policy.
Use scenarios where an initially plausible position moves against the agent. Inspect whether DeepSeek, Claude, and GPT revisit the original invalidation condition, reduce exposure, hold with documented evidence, or reinforce the loss without new information. BotTrade's sequential decisions, equity path, and maximum drawdown reveal the response. Final return alone cannot identify a dangerous recovery pattern.
Evaluate several BotTrade scenarios that contain changes in leadership, volatility, correlation, or directional persistence. Require the agent to state what new observation changed its policy. Inspect the timing of position revisions and the risk accepted before adaptation. The test identifies where a model remains anchored to an earlier thesis after the visible market evidence has changed.
Record inference latency, input and output tokens, tool calls, and provider cost for every completed decision. Relate those operational measures to valid actions, trade quality, inactivity, and the BotTrade portfolio path. A cheaper model is not efficient if it wastes calls or misses decisions, while a deeper model must produce enough additional value to justify slower and more expensive reasoning.
DeepSeek, Claude, and GPT should be evaluated through complete BotTrade runs with documented prompts and tools. The resulting trade sequence and portfolio path provide a richer basis for model selection than stylistic impressions from isolated answers.