Beyond Mistral Medium Token Limits | Generated by AI
You’re running into the token context limit of the model you used — Mistral Medium 3.1, which maxes out at 32k tokens (prompt + completion). In your log, you already sent ~9k prompt tokens and got ~1k output, so you were still under the limit, but you can’t just “increase” that ceiling yourself.
Here are your actual options:
1. Switch to a model with a larger context window
- Claude Sonnet 4 → up to 1M tokens
- Claude Opus 4 → up to 200k tokens
- GPT-4.1 / GPT-5 → up to 128k tokens (depending on provider setup)
- Gemini 1.5 Pro → up to 2M tokens (through Google Vertex/AI Studio)
If you need more than 32k tokens of input, Mistral Medium simply won’t cut it — you’ll need to choose a different model.
2. Use chunking or retrieval
If you want to stay with Mistral Medium for cost/speed, you’ll need to break your input into smaller pieces:
- Split transcripts or documents into ~20k-25k token chunks.
- Run processing in batches.
- Optionally use a retrieval pipeline: store long documents in a vector DB and only pass in the most relevant passages.
3. Check provider configuration
Some API gateways (like OpenRouter, Vertex, etc.) let you specify the model variant (mistral-large, mistral-medium, etc.). If you want higher limits but still prefer Mistral, you’d need to switch to Mistral Large (supports ~128k tokens).
👉 If your goal is just “make my log accept more tokens,” the only way is pick a model with a higher context length. Do you want me to list the maximum token context of all the models you’re currently routing (Claude, Gemini, GPT-5, DeepSeek, Mistral, etc.) so you can decide which one to use?