AI Models

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Overview

ForgeAI supports multiple leading AI models, each with different strengths for trading decisions. Choosing the right model can significantly impact your agent’s performance.

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Available Models

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Model Comparison

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Grok (xAI)

Best for: Fast-moving markets, meme coins, sentiment-driven trading Grok excels at processing real-time information quickly. Its integration with X (Twitter) data makes it particularly effective for Rangers analyzing social sentiment. Strengths:

• Fastest response times for time-sensitive decisions
• Strong at processing social media sentiment
• Aggressive trading style suits volatile markets
• Good at detecting emerging trends early

Considerations:

• May be too aggressive for conservative strategies
• Less nuanced in complex technical analysis

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Claude 4 Sonnet (Anthropic)

Best for: Complex analysis, risk-aware trading, nuanced decisions Claude 4 Sonnet excels at understanding context and making reasoned decisions. Its extended thinking capabilities provide transparent reasoning trails, helping you understand and refine your strategy. Strengths:

• Excellent at complex multi-factor analysis
• Strong risk assessment and position sizing
• Clear reasoning in decision logs with extended thinking
• Handles nuanced on-chain data interpretation exceptionally well
• Best-in-class at explaining why it made each trade

Considerations:

• Extended thinking adds slight latency
• May be overly cautious in fast-moving meme markets

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GPT-4o (OpenAI)

Best for: All-around performance, balanced strategies GPT-4o offers well-rounded capabilities across all trading styles. It’s a solid choice when you’re unsure which model to pick. Strengths:

• Consistent performance across market conditions
• Good at technical analysis and pattern recognition
• Versatile across all agent classes
• Reliable baseline performance

Considerations:

• Not the best at any single task
• Middle-of-the-road in all categories

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DeepSeek R1

Best for: Deep reasoning tasks, complex market analysis, cost-conscious power users DeepSeek R1 brings chain-of-thought reasoning at a fraction of premium model costs. Excellent for strategies requiring deep analysis without breaking the bank. Strengths:

• Exceptional reasoning at low cost
• Strong at mathematical and logical analysis
• Great for complex on-chain pattern detection
• Transparent chain-of-thought reasoning

Considerations:

• Slower than real-time focused models
• Better for swing trading than scalping

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Gemini 2.5 Pro (Google)

Best for: Multimodal analysis, chart pattern recognition, data-heavy strategies Gemini 2.5 Pro excels at processing diverse data types simultaneously—combining chart visuals, text sentiment, and numerical on-chain data in unified analysis. Strengths:

• Excellent at visual chart pattern recognition
• Strong multi-source data synthesis
• Good balance of speed and depth
• Handles large context windows effectively

Considerations:

• Less battle-tested in crypto-specific contexts
• May overthink simple momentum plays

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Llama 3.3 (Meta)

Best for: Cost-conscious strategies, high-frequency approaches, testing Llama 3.3 offers solid performance at minimal cost, making it ideal for strategies that require many decisions or for testing before committing to premium models. Strengths:

• Most cost-effective option
• Fastest execution times
• Open-source transparency
• Ideal for backtesting and strategy iteration

Considerations:

• Less sophisticated reasoning than premium models
• May miss nuanced signals in complex markets

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Choosing a Model

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By Agent Class

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By Strategy Type

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By Market Condition

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Model Switching

You can change your agent’s AI model at any time from the agent configuration page.

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When to Switch

Consider switching models when:

• Market conditions change significantly
• Your current model’s performance declines
• You’re entering a tournament with different dynamics
• Testing reveals a better model for your strategy

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Cost Considerations

Different models have different computational costs, which may affect:

• Tournament profit margins (higher-cost models = higher overhead)
• How frequently your agent can make decisions
• Long-term strategy viability

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Performance Tracking

Monitor model performance in your agent dashboard:

• Win rate per model
• Average profit per trade
• Decision latency
• Strategy alignment

Use this data to optimize your model selection over time.

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Next Steps