# Model Parameters Guide
Understanding model parameters helps you fine-tune agent behavior for optimal results.
## Core Parameters
### Temperature (0.0 - 2.0)
Controls randomness in responses.
**Low (0.0 - 0.3)**
- Deterministic, consistent output
- Same input → same output
- Best for: Code generation, calculations, factual queries
**Medium (0.4 - 0.7)** \[Default]
- Balanced creativity and consistency
- Slight variations in responses
- Best for: General conversation, analysis
**High (0.8 - 2.0)**
- Creative, unpredictable output
- High variation
- Best for: Brainstorming, creative writing
**Examples:**
```
Query: "Explain smart contract reentrancy"
Temperature 0.0:
"Reentrancy occurs when a function calls an external contract..."
[Same answer every time]
Temperature 0.7:
"Reentrancy is a vulnerability where..."
[Slight variations in phrasing]
Temperature 1.5:
"Picture this: your smart contract is like a bank vault..."
[Creative analogies, different approaches]
```
### Top P (0.0 - 1.0)
Alternative to temperature. Controls diversity by probability threshold.
**How it works:**
Model generates candidate words with probabilities:
- `ethereum` (40%)
- `blockchain` (30%)
- `crypto` (20%)
- `web3` (10%)
**top_p = 0.9**: Include top candidates until 90% cumulative probability
→ Considers: ethereum (40%) + blockchain (30%) + crypto (20%) = 90%
**top_p = 0.5**: Only top 50%
→ Considers: ethereum (40%) only
**Settings:**
- **0.1 - 0.5**: Very focused, conservative
- **0.6 - 0.9**: Balanced diversity \[Default: 0.9]
- **0.95 - 1.0**: Maximum diversity
⚠️ **Don't adjust both temperature AND top_p** - use one or the other.
### Presence Penalty (-2.0 to 2.0)
Penalizes words that already appeared, reducing repetition.
**Negative (-2.0 to -0.1)**
- Encourages repetition
- Reinforces key terms
- Good for: Technical documentation
**Zero (0.0)** \[Default]
- Natural repetition
- Balanced
**Positive (0.1 to 2.0)**
- Discourages repetition
- Forces vocabulary diversity
- Good for: Creative content, avoiding redundancy
**Example (explaining DeFi):**
```
Presence Penalty = -1.0:
"DeFi protocols provide DeFi services. DeFi users can access DeFi platforms..."
[Repetitive, reinforces "DeFi"]
Presence Penalty = 0.0:
"DeFi protocols enable decentralized services. Users can access these platforms..."
[Natural repetition]
Presence Penalty = 1.5:
"Decentralized finance protocols enable trustless services. Participants can access blockchain-based platforms..."
[Varied vocabulary]
```
### Frequency Penalty (-2.0 to 2.0)
Penalizes words based on how OFTEN they've appeared.
**Difference from Presence Penalty:**
- **Presence**: Did word appear? (yes/no)
- **Frequency**: How many times? (count)
**Negative (-2.0 to -0.1)**
- Word spam possible
- Very high repetition
**Zero (0.0)** \[Default]
- Natural frequency
**Positive (0.1 to 2.0)**
- Strong anti-repetition
- Forces alternative phrasings
**Example:**
```
Frequency Penalty = -2.0:
"The yield yield yield yield optimizer optimizes yield yield..."
[Excessive repetition]
Frequency Penalty = 0.0:
"The yield optimizer analyzes yield opportunities and optimizes returns..."
[Natural]
Frequency Penalty = 2.0:
"The return optimizer analyzes APY opportunities and maximizes profits..."
[Varied terms]
```
### Reasoning Effort (Claude Sonnet 4 Only)
**low**
- Faster responses
- Basic reasoning
- Good for: Simple queries, quick answers
**medium** \[Default]
- Balanced speed and depth
- Standard reasoning
- Good for: Most use cases
**high**
- Slower responses
- Deep analysis
- More tokens used
- Good for: Complex problems, research, multi-step logic
## Recommended Settings by Use Case
### DeFi Analysis Agent
```json
{
"frequency_penalty": 0.3,
"presence_penalty": 0.2,
"reasoning_effort": "medium",
"temperature": 0.3,
"top_p": 0.8
}
```
_Consistent, factual, avoids repetition_
### Creative Content Writer
```json
{
"frequency_penalty": 0.6,
"presence_penalty": 0.5,
"reasoning_effort": "low",
"temperature": 0.9,
"top_p": 0.95
}
```
_Diverse, creative, varied vocabulary_
### Code Generator
```json
{
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"reasoning_effort": "high",
"temperature": 0.1,
"top_p": 0.9
}
```
_Deterministic, precise, allows technical repetition_
### Research Assistant
```json
{
"frequency_penalty": 0.2,
"presence_penalty": 0.1,
"reasoning_effort": "high",
"temperature": 0.4,
"top_p": 0.85
}
```
_Thorough, analytical, comprehensive_
### Trading Advisor
```json
{
"frequency_penalty": 0.1,
"presence_penalty": 0.0,
"reasoning_effort": "medium",
"temperature": 0.2,
"top_p": 0.8
}
```
_Consistent, reliable, factual_
## Tuning Tips
### Start Conservative
Begin with default/low values, then increase gradually:
1. Test with temperature = 0.3
2. If too rigid, bump to 0.5
3. Adjust incrementally until optimal
### Single Variable
Change ONE parameter at a time to understand its effect.
### Context Matters
- **Short prompts** → Lower temperature (needs focus)
- **Long, detailed prompts** → Can increase temperature
- **Ambiguous queries** → Lower temperature (consistency)
- **Creative tasks** → Higher temperature
### Monitor Quality
Track across multiple queries:
- Consistency
- Accuracy
- Creativity
- Relevance
- Token usage
## Parameter Interactions
**Temperature + Top P**
- Don't tune both - use one
- Temperature is more intuitive
- Top P gives finer control
**Presence + Frequency Penalties**
- Can use both together
- Presence = reduce repetition
- Frequency = strongly reduce repetition
- Typical combo: presence=0.3, frequency=0.5
**Reasoning Effort + Temperature**
- Independent controls
- Reasoning affects thinking depth
- Temperature affects output randomness
- Can combine: reasoning=high, temp=0.3 for thorough consistent analysis
## Advanced: Context Window
**Max Tokens**
Not a tuning parameter, but important:
- Sets maximum response length
- Different per model
- Costs scale with length
**Models:**
- GPT-3.5: 4k context
- GPT-4: 8k context
- GPT-4-32k: 32k context
- Claude Sonnet 4: 200k context
For agents, set based on expected output:
- **Quick answers**: 500-1000 tokens
- **Analysis**: 1000-2000 tokens
- **Comprehensive**: 2000-4000 tokens
## Troubleshooting
**Problem: Too random/inconsistent**
→ Decrease temperature (try 0.3)
→ Decrease top_p (try 0.8)
**Problem: Too repetitive**
→ Increase presence_penalty (0.5-1.0)
→ Increase frequency_penalty (0.5-1.0)
**Problem: Too boring/generic**
→ Increase temperature (0.7-1.0)
→ Increase top_p (0.95)
**Problem: Off-topic responses**
→ Decrease temperature
→ Improve system prompt clarity
**Problem: Too brief**
→ Check max_tokens
→ Adjust prompt to request detail
**Problem: Too verbose**
→ Increase frequency_penalty
→ Add "be concise" to system prompt
## Testing Methodology
1. **Baseline**: Test with defaults
2. **Hypothesis**: "Increasing temp will make more creative"
3. **Test**: Run same query 5x with temp=0.8
4. **Measure**: Rate creativity, accuracy, consistency
5. **Compare**: vs baseline
6. **Iterate**: Adjust and retest
## Resources
- [OpenAI Parameter Guide](https://platform.openai.com/docs/api-reference/chat/create)
- [Anthropic Model Parameters](https://docs.anthropic.com/claude/docs/models-overview)
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**Experiment and iterate** - optimal settings vary by use case and agent personality.
