DeepMyst’s token optimization feature reduces the number of tokens used in your LLM interactions, lowering costs while preserving the quality of responses. DeepMyst Token Optimization

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How Token Optimization Works

DeepMyst employs advanced compression techniques that:
  1. Identify Redundancies: Our system analyzes message content to identify repeated phrases, patterns, and information
  2. Compress Content: Redundant content is intelligently compressed to reduce token count
  3. Preserve Context: Key information is maintained to ensure response quality isn’t compromised
  4. Restore Meaning: The optimization is transparent to the model, allowing it to generate high-quality responses
The result is significantly reduced token usage without sacrificing the quality of responses – saving you money while maintaining performance.

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Benefits of Token Optimization

Cost Reduction

Reduce token usage by up to 75%, directly lowering your API costs

Preserved Quality

Maintain response quality while using fewer tokens

Longer Conversations

Fit more context within token limits for extended dialogues

Faster Processing

Reduced token counts can lead to faster processing times

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Using Token Optimization

Enabling token optimization is as simple as adding the \-optimize flag to any model name in your API request: 
from openai import OpenAI

client = OpenAI(
    api_key="your-deepmyst-api-key",
    base_url="https://api.deepmyst.com/v1"
)

# Add the -optimize flag to the model name
response = client.chat.completions.create(
    model="gpt-4o-mini-optimize",  # Note the -optimize flag
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the history of artificial intelligence"}
    ]
)

print(response.choices[0].message.content)

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Optimization Levels (Coming Soon)

DeepMyst automatically applies the appropriate level of optimization based on context, but you can also specify a particular optimization level: 
# Specify optimization level (1-5, where 5 is maximum optimization)
response = client.chat.completions.create(
    model="gpt-4o-mini-optimize-3",  # Level 3 optimization
    messages=[
        {"role": "user", "content": "Summarize the key events of World War II"}
    ]
)
LevelDescriptionToken ReductionUse Case
1Light optimization10-25%When maximum preservation is critical
2Balanced (Default)25-40%General purpose optimization
3Enhanced40-55%Good balance of savings and quality
4Aggressive55-70%When cost savings are a priority
5Maximum70-75%+When maximum savings are required

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Real-World Examples

Here are some real-world examples of token optimization in action:

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Example 1: Long-Form Content

Without Optimization:
  • Message size: 6,500 tokens
  • Response size: 1,200 tokens
  • Total tokens: 7,700
  • Cost (at 0.01/1Ktokens):0.01/1K tokens): 0.077
With Optimization:
  • Optimized message size: 2,200 tokens (66% reduction)
  • Response size: 1,200 tokens
  • Total tokens: 3,400
  • Cost: $0.034
  • Savings: 56%

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Example 2: Conversation History

Without Optimization:
  • 10-turn conversation: 12,000 tokens
  • New response: 800 tokens
  • Total tokens: 12,800
  • Cost (at 0.01/1Ktokens):0.01/1K tokens): 0.128
With Optimization:
  • Optimized conversation: 3,600 tokens (70% reduction)
  • New response: 800 tokens
  • Total tokens: 4,400
  • Cost: $0.044
  • Savings: 65%

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Best Practices

For maximum benefit from token optimization:
  1. Use in multi-turn conversations: The benefits compound as conversation history grows
  2. Apply to information-dense queries: Content with repetition and patterns benefits most
  3. Balance with reasoning: Combine optimization with reasoning techniques for complex tasks
  4. Monitor quality: Watch for any impact on response quality and adjust optimization level if needed
  5. Preserve key instructions: Keep important system messages and instructions clear and precise

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Optimization Analytics

The DeepMyst dashboard provides detailed analytics on your token optimization: Deepmyst Dashboard These analytics help you:
  • Track total tokens saved
  • Monitor cost reduction over time
  • Compare optimization effectiveness across different models
  • Identify opportunities for further optimization

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When to Use Token Optimization

Token optimization is particularly valuable for:
  • Multi-turn conversations: As context grows, so do the savings
  • Long-form content processing: When analyzing or generating extensive content
  • High-volume applications: Applications making many API calls
  • Fixed-budget projects: When working within strict cost constraints
  • Context-window-limited scenarios: When you need to fit more content within token limits

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Implementation Tips

To maximize the effectiveness of token optimization:
  1. Start with balanced optimization: Begin with the default optimization level
  2. Test with your specific use cases: Different content types benefit differently
  3. Monitor quality metrics: Ensure optimization doesn’t impact critical outputs
  4. Combine with other DeepMyst features: Use alongside reasoning or routing for even better results
  5. Apply selectively: Use optimization where it makes the most sense for your workload