Fix Mistral context length exceeded
Quick answer
The
context length exceeded error in Mistral occurs when the input prompt plus completion tokens exceed the model's maximum token limit. To fix this, truncate or chunk your input to fit within the model's context window before calling client.chat.completions.create(). ERROR TYPE
api_error ⚡ QUICK FIX
Truncate or split your prompt to ensure total tokens stay within the model's maximum context length before sending the request.
Why this happens
The context length exceeded error arises because Mistral models have a fixed maximum token limit (e.g., 8192 tokens for mistral-large-latest). If your combined prompt and expected completion tokens exceed this limit, the API rejects the request.
Typical triggering code looks like this:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["MISTRAL_API_KEY"])
long_prompt = "A very long text..." * 1000 # Exceeds token limit
response = client.chat.completions.create(
model="mistral-large-latest",
messages=[{"role": "user", "content": long_prompt}],
max_tokens=512
)
print(response.choices[0].message.content)
This results in an error like:
openai.error.InvalidRequestError: This model's maximum context length is 8192 tokens, however you requested 9000 tokens (input + output).The fix
To fix the error, ensure your prompt plus max_tokens does not exceed the model's context length. You can truncate the prompt or split it into smaller chunks.
Example corrected code truncating the prompt to fit:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["MISTRAL_API_KEY"])
# Define max tokens for model context
max_context_tokens = 8192
max_completion_tokens = 512
# Example long prompt
long_prompt = "A very long text..." * 1000
# Simple truncation function (token count approx by words here for demo)
def truncate_prompt(prompt, max_tokens):
words = prompt.split()
if len(words) > max_tokens:
return " ".join(words[:max_tokens])
return prompt
# Truncate prompt to fit context window
allowed_prompt_tokens = max_context_tokens - max_completion_tokens
truncated_prompt = truncate_prompt(long_prompt, allowed_prompt_tokens)
response = client.chat.completions.create(
model="mistral-large-latest",
messages=[{"role": "user", "content": truncated_prompt}],
max_tokens=max_completion_tokens
)
print(response.choices[0].message.content) output
A valid completion text from the model without context length error.
Preventing it in production
- Implement input validation to measure token length before API calls using a tokenizer compatible with
Mistral. - Use chunking strategies to split large documents into smaller pieces and process them sequentially.
- Apply exponential backoff and retries for transient errors.
- Monitor token usage and log errors to detect when inputs approach limits.
Key Takeaways
- Always ensure your prompt plus max_tokens fit within the model's context length.
- Use token counting and prompt truncation or chunking to avoid context length errors.
- Implement retries and monitoring to handle API errors gracefully in production.