Anthropic API 400 Error — Context Length Exceeds Model Maximum
Symptom
- API returns
400 Bad Request - Error:
{"error": {"type": "invalid_request_error", "message": "prompt is too long"}} - Or:
context_length_exceeded/max_tokens_exceeded - Request fails immediately — no response generated
- Agent may retry with same context and fail again in a loop
Root Cause
The total tokens in messages + max_tokens exceeds the model’s context window. Model limits:
- claude-haiku-4-5: 200K tokens input
- claude-sonnet-4-6: 200K tokens input
- claude-opus-4-6: 200K tokens input
Most common causes: unbounded conversation history, large tool results, or system prompt + conversation combined exceeding limit.
Fix
Option 1: Measure before sending
import anthropic
client = anthropic.Anthropic()
def count_tokens(messages, system=""):
"""Count tokens without sending the request"""
response = client.messages.count_tokens(
model="claude-sonnet-4-6",
system=system,
messages=messages,
)
return response.input_tokens
def build_safe_messages(history, system_prompt, max_input_tokens=180000):
"""Trim history to fit within context limit"""
total = count_tokens(history, system_prompt)
while total > max_input_tokens and len(history) > 2:
# Remove oldest non-system message pair
history = history[2:] # Remove oldest user + assistant turn
total = count_tokens(history, system_prompt)
return history
Option 2: Estimate tokens cheaply and truncate
def estimate_tokens(text):
"""Rough estimate: 1 token ≈ 4 characters"""
return len(str(text)) // 4
def truncate_messages_to_limit(messages, max_tokens=150000, reserve_for_output=8192):
budget = max_tokens - reserve_for_output
total = 0
result = []
# Always keep system message
system_msgs = [m for m in messages if m.get('role') == 'system']
for msg in system_msgs:
total += estimate_tokens(msg['content'])
result.append(msg)
# Add recent messages until budget runs out
non_system = [m for m in messages if m.get('role') != 'system']
for msg in reversed(non_system):
msg_tokens = estimate_tokens(msg['content'])
if total + msg_tokens > budget:
break
result.insert(len(system_msgs), msg)
total += msg_tokens
return result
Option 3: Catch 400 and auto-trim
from anthropic import BadRequestError
async def complete_with_trim(messages, system, model="claude-sonnet-4-6"):
max_retries = 3
for attempt in range(max_retries):
try:
return client.messages.create(
model=model,
system=system,
messages=messages,
max_tokens=4096
)
except BadRequestError as e:
if "prompt is too long" in str(e) and attempt < max_retries - 1:
# Remove 20% of oldest messages and retry
trim_count = max(2, len(messages) // 5)
messages = messages[trim_count:]
print(f"Context too long — trimmed {trim_count} messages, retrying")
else:
raise
Option 4: Summarize before hitting limit
CONTEXT_WARNING_THRESHOLD = 150000 # Warn at 75% of 200K
async def managed_conversation(messages, system):
token_count = count_tokens(messages, system)
if token_count > CONTEXT_WARNING_THRESHOLD:
print(f"Context at {token_count} tokens — summarizing old turns")
# Summarize first half of conversation
old_messages = messages[:len(messages)//2]
summary = await client.messages.create(
model="claude-haiku-4-5-20251001", # Use cheap model for summarization
max_tokens=2000,
messages=[{
"role": "user",
"content": f"Summarize this conversation in 5 bullet points:\n{old_messages}"
}]
)
messages = [
{"role": "user", "content": f"[Earlier summary: {summary.content[0].text}]"},
{"role": "assistant", "content": "Understood, continuing from that context."},
*messages[len(messages)//2:]
]
return await complete_with_trim(messages, system)
Model Context Limits Quick Reference
| Model | Max Input | Safe Limit (with output) |
|---|---|---|
| claude-haiku-4-5 | 200K | 190K |
| claude-sonnet-4-6 | 200K | 190K |
| claude-opus-4-6 | 200K | 190K |
Expected Token Savings
Agent retry-looping on 400 error: ~5,000 wasted + task failure Proactive trimming: task completes, 0 extra tokens
Environment
- Anthropic API (all current models)
- Most common in: long agent sessions, file-reading agents
- Source: Anthropic API documentation, direct experience
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