Master the art of parameter tuning across OpenAI, Google Gemini, Anthropic Claude, and DeepSeek to achieve optimal AI model performance
Large Language Model (LLM) parameters are the key to unlocking optimal performance from AI models. This comprehensive guide covers critical parameters like temperature, top-p, top-k, and advanced settings across major providers.
GPT-4, GPT-3.5 with comprehensive parameter support including frequency/presence penalties and logit bias.
Gemini Pro/Ultra featuring the largest context window and unique top-k parameter support.
Claude 3.5/3 with simplified, safety-focused parameter design and high-quality reasoning.
DeepSeek-V3 offering OpenAI compatibility with cost-effective pricing and strong coding capabilities.
Essential parameters that form the foundation of LLM output control across all major providers.
Temperature controls the randomness and creativity of model outputs. Lower values produce more deterministic results, while higher values increase creativity and variation.
| Provider | Range | Default | Notes |
|---|---|---|---|
| OpenAI | 0.0-2.0 | 1.0 | Full range support |
| Google Gemini | 0.0-2.0 | 1.0 | Full range support |
| Anthropic Claude | 0.0-1.0 | 1.0 | Limited to 1.0 maximum |
| DeepSeek | 0.0-2.0 | 1.0 | OpenAI compatible |
Top-p (nucleus sampling) considers only tokens whose cumulative probability reaches the specified threshold. This provides more dynamic token selection compared to top-k.
| Provider | Range | Default | Recommendation |
|---|---|---|---|
| OpenAI | 0.0-1.0 | 1.0 | Use with temperature |
| Google Gemini | 0.0-1.0 | 0.95 | Default optimized |
| Anthropic Claude | 0.0-1.0 | 1.0 | Simple setup |
| DeepSeek | 0.0-1.0 | 1.0 | OpenAI compatible |
Top-k limits token selection to the k most probable candidates. Not all providers support this parameter, with some preferring nucleus sampling instead.
| Provider | Support | Range | Default |
|---|---|---|---|
| OpenAI | Not supported | - | - |
| Google Gemini | Supported | 1-2048 | 64 |
| Anthropic Claude | Not supported | - | - |
| DeepSeek | Not specified | - | - |
Controls the maximum number of tokens in the generated response. Essential for managing response length and API costs.
| Provider | Range | Context Window | Notes |
|---|---|---|---|
| OpenAI | 1-128,000 | 128K tokens | Model dependent |
| Google Gemini | 1-32,768 | 2M tokens | Largest context window |
| Anthropic Claude | 1-200,000 | 200K tokens | High token limit |
| DeepSeek | Model dependent | 128K tokens | OpenAI compatible |
Sophisticated controls for fine-tuning model behavior and addressing specific output requirements.
Reduces the likelihood of repeating tokens based on their frequency in the text so far. Positive values discourage repetition, negative values encourage it.
{
"model": "gpt-4",
"frequency_penalty": 0.5,
"messages": [{"role": "user", "content": "Write a creative story"}]
}| Provider | Support | Range | Use Cases |
|---|---|---|---|
| OpenAI | Full support | -2.0 to 2.0 | Creative writing, code generation |
| Google Gemini | Not supported | - | - |
| Anthropic Claude | Not supported | - | - |
| DeepSeek | OpenAI compatible | -2.0 to 2.0 | Same as OpenAI |
Reduces the likelihood of repeating any token that has appeared in the text so far. Unlike frequency penalty, it doesn't matter how often the token has appeared.
Scales with repetition count
Stronger effect on frequently repeated tokens
Binary presence detection
Equal effect on any repeated token
| Provider | Support | Range | Best Practice |
|---|---|---|---|
| OpenAI | Full support | -2.0 to 2.0 | 0.3-0.6 for creativity |
| Google Gemini | Not supported | - | - |
| Anthropic Claude | Not supported | - | - |
| DeepSeek | OpenAI compatible | -2.0 to 2.0 | Same as OpenAI |
Text sequences that will halt generation when encountered. Useful for controlling output format and preventing unwanted continuation.
{
"model": "gpt-4",
"stop": ["\\n\\n", "END", "---"],
"messages": [{"role": "user", "content": "List three items"}]
}| Provider | Support | Limit | Format |
|---|---|---|---|
| OpenAI | Supported | Up to 4 sequences | Array of strings |
| Google Gemini | Supported | Array of strings | Array of strings |
| Anthropic Claude | Supported | Array of strings | Array of strings |
| DeepSeek | OpenAI compatible | Up to 4 sequences | Array of strings |
Modifies the likelihood of specified tokens appearing in the completion. Allows fine-grained control over token selection by token ID.
{
"model": "gpt-4",
"logit_bias": {
"50256": -100, // Suppress specific token
"1234": 10 // Boost specific token
},
"messages": [{"role": "user", "content": "Generate a response"}]
}| Provider | Support | Range | Implementation |
|---|---|---|---|
| OpenAI | Full support | -100 to 100 | By token ID |
| Google Gemini | Not supported | - | - |
| Anthropic Claude | Not supported | - | - |
| DeepSeek | OpenAI compatible | -100 to 100 | By token ID |
Comprehensive comparison of parameter support and unique features across major LLM providers.
| Parameter | OpenAI | Google Gemini | Anthropic Claude | DeepSeek |
|---|---|---|---|---|
| Models | GPT-4, GPT-3.5 | Gemini Pro, Ultra | Claude 3.5, Claude 3 | DeepSeek-V3 |
| Context Window | 128K tokens | 2M tokens | 200K tokens | 128K tokens |
| Temperature | 0.0-2.0 (default: 1.0) | 0.0-2.0 (default: 1.0) | 0.0-1.0 (default: 1.0) | 0.0-2.0 (OpenAI compatible) |
| Top-p | 0.0-1.0 (default: 1.0) | 0.0-1.0 (default: 0.95) | 0.0-1.0 (default: 1.0) | 0.0-1.0 (OpenAI compatible) |
| Top-k | Not supported | 1-2048 (default: 64) | Not supported | Not specified |
| Max Tokens | 1-128,000 | 1-32,768 | 1-200,000 | Model dependent |
| Frequency Penalty | -2.0 to 2.0 (default: 0.0) | Not supported | Not supported | OpenAI compatible |
| Presence Penalty | -2.0 to 2.0 (default: 0.0) | Not supported | Not supported | OpenAI compatible |
| Stop Sequences | Up to 4 sequences | Array of strings | Array of strings | OpenAI compatible |
| Logit Bias | -100 to 100 (by token ID) | Not supported | Not supported | OpenAI compatible |
OpenAI provides the most comprehensive set of parameters, making it ideal for fine-tuned control over model behavior.
Applications requiring precise control over repetition, token selection, and deterministic outputs. Ideal for production systems with specific output requirements.
Google Gemini offers the largest context window and unique top-k parameter support, excelling in long-form content processing.
Long-form content analysis, document processing, and applications requiring extensive context understanding. Excellent for research and analysis tasks.
Anthropic Claude focuses on safety and high-quality reasoning with a simplified parameter set that prioritizes reliability.
Applications prioritizing safety, consistency, and high-quality reasoning. Ideal for educational content, analysis, and applications requiring reliable outputs.
DeepSeek offers OpenAI API compatibility with cost-effective pricing and strong performance in coding and technical tasks.
Cost-sensitive applications, code generation, technical documentation, and scenarios requiring OpenAI compatibility with budget constraints.
Optimized parameter configurations for specific applications and objectives.
Objective: Maximize accuracy and consistency while minimizing hallucinations and creative interpretations.
| Temperature | 0.1-0.3 | Low randomness for consistency |
| Top-p | 0.1-0.3 | Focus on most probable tokens |
| Top-k | 10-20 | Limited token candidates (Gemini) |
| Frequency Penalty | 0.0 | No repetition discouragement |
| Presence Penalty | 0.0 | Allow factual repetition |
{
"temperature": 0.2,
"top_p": 0.2,
"frequency_penalty": 0.0,
"presence_penalty": 0.0,
"max_tokens": 500
}{
"temperature": 0.2,
"top_p": 0.2,
"top_k": 15,
"max_tokens": 500
}{
"temperature": 0.2,
"top_p": 0.2,
"max_tokens": 500
}Objective: Maximize creativity and variety while maintaining coherence and quality.
| Temperature | 0.7-1.2 | Higher creativity and variation |
| Top-p | 0.8-0.95 | Diverse token selection |
| Top-k | 100-200 | Broader vocabulary (Gemini) |
| Frequency Penalty | 0.3-0.8 | Discourage repetitive language |
| Presence Penalty | 0.3-0.6 | Encourage varied vocabulary |
{
"temperature": 0.9,
"top_p": 0.9,
"frequency_penalty": 0.5,
"presence_penalty": 0.4,
"max_tokens": 2000
}{
"temperature": 0.9,
"top_p": 0.9,
"top_k": 150,
"max_tokens": 2000
}{
"temperature": 0.8,
"top_p": 0.9,
"max_tokens": 2000
}Objective: Balance creativity with syntactic correctness and functional accuracy.
| Temperature | 0.2-0.4 | Moderate creativity, maintain syntax |
| Top-p | 0.3-0.5 | Focus on probable code patterns |
| Top-k | 20-40 | Limited but relevant options (Gemini) |
| Frequency Penalty | 0.1-0.3 | Slight discouragement of repetition |
| Presence Penalty | 0.1-0.3 | Encourage varied naming patterns |
{
"temperature": 0.3,
"top_p": 0.4,
"frequency_penalty": 0.2,
"presence_penalty": 0.2,
"max_tokens": 1500,
"stop": ["```", "\\n\\n\\n"]
}{
"temperature": 0.3,
"top_p": 0.4,
"top_k": 30,
"max_tokens": 1500,
"stop": ["```", "\\n\\n\\n"]
}{
"temperature": 0.3,
"top_p": 0.4,
"max_tokens": 1500,
"stop": ["```", "\\n\\n\\n"]
}Strategic approaches to parameter optimization, testing methodologies, and cost-effective implementation.
Begin with lower temperature (0.3-0.5) and adjust upward based on needs. This ensures quality baseline before introducing creativity.
Make incremental parameter changes and test thoroughly. Document the impact of each adjustment on output quality.
Match parameter settings to specific objectives. Creative tasks benefit from higher randomness, factual tasks require consistency.
Understand each provider's strengths and default behaviors. Optimize parameters for the specific model architecture.
Understanding how parameters work together is crucial for optimal results.
| Parameter Combination | Effect | Recommendation |
|---|---|---|
| High Temperature + Low Top-p | Constrained creativity | Use for controlled variation |
| Low Temperature + High Top-p | Deterministic with broader options | Good for consistent quality |
| High Frequency + Presence Penalty | Strong repetition avoidance | Risk of unnatural language |
| Temperature + Top-k (Gemini) | Dual randomness control | Adjust one primarily |
| Provider | Cost Tier | Strengths | Best For Budget-Conscious |
|---|---|---|---|
| DeepSeek | Low | OpenAI compatibility, strong coding | High-volume applications |
| OpenAI | Medium-High | Full parameter control | Critical production systems |
| Google Gemini | Medium | Large context, multimodal | Long-document processing |
| Anthropic Claude | Medium-High | Safety, consistency | Safety-critical applications |