Solving the Multi-LLM Context Tokenization Gap

Why token counting isn’t a solved problem when building across providers

Jonathan Murray highlights that context windows are not interoperable across major LLM providers. Tokenizers from OpenAI, Anthropic, and Google often produce count discrepancies of 10–20% for the same block of text.

Why This Matters

In technical reality, a single token estimate fails because code and prose tokenize differently across model versions. Relying on generic margins leads to either unnecessary truncation that degrades conversation quality or unpredictable routing failures when a new model ingests prior context that is already over its specific limit.

Key Insights

• Token count variance of 10–20% exists between providers like OpenAI and Claude as identified in 2026.
• Context-window overflow occurs when switching providers mid-conversation because the new model re-processes the full history through a different tokenizer.
• Provider-aware token counting measures prompts against the specific target model’s tokenizer before the routing layer sends the request.
• Adaptive context window management components allow systems to trim or compress history calibrated to the specific model receiving the request.

Practical Applications

• Use case: Multi-model routing layers using per-provider measurements to avoid pricing surprises. Pitfall: Using a single safety margin for all providers, leading to premature truncation.
• Use case: Context management systems trimming history calibrated specifically to the receiving model. Pitfall: Inconsistent truncation where different models see different segments of the same conversation history.

References:

• https://dev.to/backboardio/the-hidden-challenge-of-multi-llm-context-management-1pbh