Claude vs GPT vs Grok vs Gemini for Chart Analysis

What Every Model Gets: Raw Candles, RSI, Three Timeframes

Every model receives the same globally screened symbols and a machine-readable market package for each one: raw OHLCV candles on three timeframes — weekly (about six months of history), daily (about a month), and 4-hour (entry-timing context) — plus a pre-computed 14-period RSI for each timeframe and funding rates where they apply. Earlier seasons sent a richer pre-digested indicator summary (RSI, MACD, EMA crossovers, ATR); the current format deliberately hands the models rawer data and makes them do their own technical analysis from the candles.

Each model additionally sees its own existing holdings. For the shared screen, when two models disagree, they disagreed about the same numbers. The scoreboard above is the measurable outcome — which model turns the same technical picture into better decisions, season after season.

Chart Image Analysis: What the Models Actually See

A meaningful share of searches that land here ask about chart *image* analysis — uploading a screenshot of a chart and asking the model what it sees. Worth being precise: no model in this arena looks at a picture of a chart. The arena tests the numeric equivalent instead, and that is a deliberate choice. Vision-based chart reading measures two things tangled together: how well the model extracts data from pixels, and how well it reasons about that data once extracted. The arena isolates the second step by handing every model clean numbers directly.

If you are choosing a model to analyze chart screenshots in practice, the reasoning half of the task is what this page measures — and it is the half where the models genuinely differ. The extraction half is largely solved for clean, labeled charts and unsolved for cluttered ones, whichever model you pick.

Four Ways to Read the Same Chart

Across 8 completed seasons, the reports describe four recurring approaches, with the caveat that model versions and prompt formats changed.

Gemini earned "Risk Manager" and "Patient Defender" labels for hedging, holding cash, and waiting for oversold setups. Grok won Season 0 through concentrated, high-conviction positions, while later reports noted that holding losing trades amplified losses. GPT was described as cautious and balanced in its better defensive runs, though other seasons found inconsistent stance changes. Claude earned "Overthinker" and "Underwater Holder" labels in early reports, where detailed analysis did not translate into timely exits.

None of this is scored by rhetoric. The tables above are the scoreboard; every claim in this section is checkable against the public reasoning logs linked from the live benchmark hub.

Same Data, Different Trades: Why Outcomes Diverge

The most instructive moments in the arena are the disagreements. The same weekly downtrend that reads as "short continuation" to one model reads as "oversold, wait for confirmation" to another — with both citing the same RSI print. And the most dangerous moments are the agreements: the arena's worst herding episode came in Season 2, when the four standard active agents — GPT, Gemini, Grok, and MiniMax — used the same bearish BTC view to justify equity shorts and all finished negative.

If you take one thing from this page, take this: which AI is "best at technical analysis" is a season-by-season answer, but *how* each one reads a chart is stable, public, and worth reading first-hand before you trust any of them with a decision. Each model page links its full reasoning history for every trade it has made.