Claude Takes the Crown — The Truth Behind 6 Major AI Grid Strategies | OKX & Ai Coin Live Test Results

NOF1's "AI Crypto Trading Live Arena" Season 1 finally concluded on November 4, 2025, at 6:00 AM, whetting the appetite of the crypto, tech, and finance communities alike.

Yet the outcome of this "AI intelligence public test" proved somewhat unexpected: the combined $60,000 in starting capital across six models ended at just $43,000, representing an overall loss of approximately 28%. Among them, , with a comeback to take first place; while the four American models all posted losses.

Interestingly, the recent live evaluation conducted by OKX and Ai Coin across six AI models did not focus on short-term trading tactics, but instead set its sights on futures grid strategies. And it was precisely this format that unearthed the real returns performance of 6 major AI models: In futures grid strategies, AI achieved "collective survival" — all models delivered positive returns. This suggests that AI models may be better suited to neutral, systematic grid strategies rather than short-term buy-high, sell-low chasing. Among them, Claude took home the championship, while , which had ranked first in the NOF1 competition, ended up as the worst performer this time. GPT-5 and Gemini performed relatively steadily, taking second and third place respectively; , despite different strategy settings, ended up with nearly identical returns.

Same AI models — why did they produce such drastically different results across two separate tests? What insights do the underlying patterns offer for strategy and trading users?

6 Major AI Grid Strategies Live Test: Claude Takes the Crown, All with Positive Returns

The "AI Crypto Trading Live Arena" had a simple premise: six AI models each managed $10,000 in starting capital, autonomously trading BTC, XRP, and other perpetual contracts on a Perp DEX platform, for a two-week period (launching around October 18); only market quantitative data was provided, with AI making independent decisions on long/short positions, leverage, and position sizing, along with confidence scores for each decision.

To that end, we adopted an equally minimalist setup: under uniform conditions (each AI invested 1,000 USDT with 5x leverage), six AI models were put through live testing from October 24 to November 4, 2025. Based on OKX's BTC/USDT perpetual 1-hour chart, each AI was given grid parameters, including price range and grid count, direction (long, short, neutral), and mode (arithmetic, geometric).

**The result: all AI models adopted arithmetic grid mode with neutral grid strategy, but showed clear differences in specific parameter execution such as price range setting and grid density: **Grok4 and (100,000–120,000 U), with the former using 50 grids (tighter spacing) and the latter only 20; Gemini used a range of 105,000–118,000 U with 50 grids; GPT-5 had the narrowest range at 105,000–115,500 U with the fewest grids (just 10, the widest spacing); (108,000–112,000 U) with 20 grids.

**OKX platform market data shows that during this period, BTC price fluctuated between $103K and $116K, generally trending upward in震荡 then sharply declining. It was precisely this "V-shaped reversal" that became the dividing line for the six AI models. **This precise range is crucial to the analysis — it directly validates the key difference between this live test and conventional backtesting, and explains why some AI models "failed." Here are the live performance results:

Live Champion: Claude

Strategy Core: Moderate range, moderate trigger density — balances ranging and trending phases for greater stability

Claude clinched victory with a +6.18% cumulative returns rate. Its success key was a "medium-wide, medium-dense" grid strategy — this configuration serves as the gold standard, perfectly matching the BTC ranging market of this cycle, and became a reference model that balances profitability and risk control in live trading.

Its grid range was set at 106K–116K — neither as aggressive as nor as wide as Grok4. During the ranging upward phase, it steadily accumulated returns; when the market sharply dropped, the 106K lower bound effectively controlled drawdown, outperforming all medium/narrow-range models. The moderate range combined with appropriate density ensured ample grid profits while keeping floating losses from position exposure at a minimum during sharp declines.

Specifically, during the market rise, Claude avoided the grid idle situation high prices, steadily accumulating +7.90% in profit; when BTC dropped to approximately 103K during the sharp decline, Claude's 106K lower bound meant only a 3K gap above the price, and the high accumulated profit effectively buffered the floating loss, keeping drawdown at just 1.72% under 5x leverage — demonstrating exceptional risk control.

Reliable Alternative: GPT-5

Strategy Core: Wider range, low density — higher per-grid profit, diluted risk through lower total position

GPT-5 delivered steady performance, ranking second with +5.79% cumulative returns — a reliable alternative second only to Claude. Its strategy was more aggressive with a slightly higher risk appetite, tending to seize market opportunities, though drawdown management fell short of Claude. The returns curve showed stepped increases with rapid growth, but the pullback in the later stage (Day 10) was larger than Claude's. Overall efficiency was high, with profitability approximately twice the benchmark. At present, GPT-5 is a steady and efficient alternative strategy, balancing returns and moderate risk, though there remains room for optimization in drawdown management.

The core feature of this model's grid strategy is low density with high per-grid returns. Compared to Gemini, its drawdown reached 2.65%, slightly higher, but with fewer total grids and limited total position, risk was diluted. Meanwhile, the 105K lower bound provided a buffer during sharp declines. During the ranging period, this strategy demonstrated strong efficiency, with cumulative returns reaching +8.44%. Compared to , GPT-5's lower bound is higher, giving it notably stronger downside resilience during price declines. This strategy controls extreme risk exposure by limiting total positions while balancing returns and safety — a reliable alternative for those seeking efficiency with stability.

Most Conservative: Grok4

Strategy Core: Widest range, high density — ultimate defense, zero grid-off guarantee for safety

The Grok4 model represents the ultimate defense strategy. Compared to , it completely sacrificed offensive capability during ranging periods in exchange for the highest capital safety. The 100K lower bound ensures zero grid-off when BTC drops to 103K, and the high-density grid further spreads position risk, keeping absolute drawdown at just 0.97%. Compared to , while both had similar efficiency, Grok4's returns curve was the smoothest with the lowest drawdown, making it the most conservative and robust choice — especially suitable for users prioritizing capital safety.

Additionally, there is "Stably Defensive " — whose strategy core is: widest range, medium density, defense-first while balancing efficiency with zero grid-off. And "Outstanding Performer Gemini" — whose strategy core is: relatively wide range, high density, high-frequency micro-profits, spreading risk through broad coverage.

Notably, the range as Grok4 and ended up with nearly identical returns, validating the logic that "range takes priority over density": under zero grid-off defense, the efficiency difference from medium density is offset; range width determines downside resilience, while density primarily affects returns curve smoothness and trigger frequency.

Meanwhile, the Gemini model demonstrated how a high-density strategy enhances drawdown resistance within a relatively wide range: with the same lower bound as GPT-5, Gemini's high-density grid broadly distributed positions, effectively spreading sharp decline risk, with drawdown of just 1.41% — noticeably better than GPT-5's 2.65%, indicating that high-density strategy can significantly improve stability and curve smoothness, making it a preferred choice for those seeking steady returns. Overview of Advantages and Disadvantages of 6 AI Models' Grid Strategies (Note: Detailed strategy characteristics of in the next section):

**Under the current test conditions, AI models achieving "collective survival" with positive returns is based on solid logic: **In a market predominantly characterized by ranging upward movement, all models successfully leveraged the strategy's "volatility equals profit" characteristic to accumulate a sufficient safety profit cushion. Even when extreme risk (sharp decline) occurred, this profit cushion was enough to withstand the erosion of floating losses, ensuring that all models' final returns remained positive.

Behind the "Fall from Grace": Short-Term Trading Champion ?

Let's first review the results of the "AI Crypto Trading Live Arena" Season 1 from NOF1: Chinese-language models , with a comeback to take first place; while the four American models all posted losses.

This demonstrates that high-frequency trading often carries higher risk: excessive trading generates high commission fees that erode net value, and a low win rate itself is not inherently dangerous — the key lies in risk management; facts prove that even as complex AI strategies proliferate, simply holding Bitcoin (HODL) may still outperform most models.

One major highlight is the stark contrast in results between the two experiments : took , yet "fell from grace" in the grid strategy, becoming the worst performer — why?

In this strategy experiment, 's performance was the "biggest lesson" of the test. During the testing period, it once achieved the highest monthly profit peak of +41.88% and the highest single-day returns of 65.48U, but later suffered a massive 8.12% drawdown, bringing its final cumulative returns to just 22.51U, ranking last.

**Its strategy core is: **Narrow-range, high-frequency arbitrage, aggressively concentrated, only suitable for ranging markets. During the market rise, its narrow range perfectly matched the ranging market, executing high-frequency arbitrage with returns quickly climbing to a peak of +10.37%.

However, compared to other models, its 108K lower bound became the root cause of its collapse: when BTC sharply dropped to approximately 103K during the decline phase, the 5K U grid-off width left accumulated long positions completely exposed. The 5x leverage further amplified floating losses, instantly wiping out profits, with Day 10 drawdown reaching a maximum 8.12% among all models. This fully illustrates that while narrow-range strategies can generate profits quickly during ranging periods, they lack defensive depth and are only suitable for narrow-range market conditions — making them extremely vulnerable to losses when prices deviate significantly.

Meanwhile, in the previous "AI Crypto Trading Live Arena" Season 1, 's core reason for winning the championship was — timely strategy adjustment and market adaptation. As market volatility intensified in the later stage, , focused single BTC full-position strategy, combined with 5x leverage and precise take-profit/stop-loss settings, efficiently capturing rebound opportunities and achieving explosive net value growth. This validated its robustness in dynamic, uncertain environments (**the ability to maintain stable performance across different environments and market volatility conditions without easily breaking down**) and problem-solving capability. In contrast, 's conservative multi-dimensional evaluation, while excellent in risk control (highest Sharpe ratio), showed slow growth and failed to fully capitalize on BTC's trending market. American models like GPT-5, being overly aggressive, resulted in across-the-board losses.

One-sentence summary: , while its grid strategy failure stemmed from passive parameter flaws. Therefore, AI trading must match market conditions — avoid "one strategy fits all."

The second highlight is:** In OKX and Ai Coin's historical market backtesting from July 25 to October 25, 2025, the six AI models showed no grid-off risk in their BTC/USDT perpetual contract grid strategies, with relatively stable returns. However, in this live test, multiple models experienced grid-off or violent returns volatility. What does this difference reveal?