One API Key, Many Models: Switching Models Mid-Conversation Without Losing Context

Why Switch Models Mid-Conversation?

Different models excel at different tasks. You might start a conversation with one model for brainstorming, then switch to another for coding help or summarization. With a single API key on our platform, you can change the model between turns without losing the conversation history.

How to Change the Model Field

All major API formats allow you to specify the model in each request. For chat completions, the model parameter determines which model processes the current turn. To switch models, simply change the value of model in your next API call.

Example (Python with OpenAI-compatible SDK):

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.aggregator.com/v1"
)

# First turn: use Claude
response = client.chat.completions.create(
    model="claude-3-sonnet",
    messages=[{"role": "user", "content": "Explain quantum computing."}]
)

# Second turn: switch to GPT
response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[
        {"role": "user", "content": "Explain quantum computing."},
        {"role": "assistant", "content": response.choices[0].message.content},
        {"role": "user", "content": "Now simplify that for a 10-year-old."}
    ]
)

What Happens to Message History?

The message history is not stored by the API; you must send the full conversation each time. When you switch models, the new model receives the same message array you provide. It will interpret the history according to its own training and capabilities.

• Context preservation: As long as you include all previous messages, the new model sees the full context.
• Model-specific quirks: Some models may format responses differently or have different tokenization, but the semantic content remains.
• Role handling: Ensure roles (system, user, assistant) are consistent. Some models may have different system prompt support.

Token Accounting Across Models

Tokens are counted per request based on the model used for that turn. When you switch models:

• The input tokens for the new turn include the entire message history you send.
• The output tokens are generated by the new model.
• Billing is calculated per model according to its official price, multiplied by our platform fee (×1.3 for users).

There is no cross-model token pooling; each request is independent.

Best Practices for Seamless Switching

• Maintain a consistent message format: Use the same role names and structure across models.
• Be mindful of context length: Different models have different maximum context windows. If you switch to a model with a smaller window, you may need to truncate history.
• Test with a small conversation: Before relying on mid-conversation switching in production, test with a few turns to ensure compatibility.
• Use system prompts wisely: Some models may ignore or handle system prompts differently. If needed, include critical instructions in the first user message.

Example: Switching from Claude to DeepSeek

Suppose you start with Claude for creative writing, then switch to DeepSeek for code generation. You send the entire history to DeepSeek with a new user query. DeepSeek processes the context and generates a response. Token usage for that turn is based on DeepSeek's pricing.

Conclusion

Switching models mid-conversation is straightforward: change the model parameter and include the full message history. Token accounting is per request, and context is preserved as long as you send it. Our platform supports many models with one API key, so you can leverage the strengths of each without managing multiple accounts.