Reasoning models represent a paradigm shift. Instead of generating tokens quickly, they “think” before responding. The result is dramatically better performance on complex tasks—but with different tradeoffs than standard models.
Here’s how to use reasoning models effectively in production.
Understanding Reasoning Models
How They Differ
reasoning_model_characteristics:
standard_models:
approach: "Generate tokens sequentially"
latency: "Fast (1-3 seconds typical)"
quality: "Good for straightforward tasks"
cost: "Per token, predictable"
reasoning_models:
approach: "Think, then generate"
latency: "Slower (10-60+ seconds)"
quality: "Excellent for complex tasks"
cost: "Thinking tokens add up"
tradeoff:
- More thinking = better answers
- Latency and cost increase together
- Not always worth the tradeoff
When to Use
reasoning_model_use_cases:
good_fit:
- Complex multi-step problems
- Code review and debugging
- Mathematical reasoning
- Strategic analysis
- Research synthesis
poor_fit:
- Simple Q&A
- Real-time chat
- High-volume, low-complexity
- Latency-sensitive applications
decision_framework:
- Is accuracy critical?
- Is the problem complex?
- Can users wait 30+ seconds?
- Does the value justify the cost?
Production Patterns
Async Processing
class ReasoningModelService:
"""Handle reasoning model requests asynchronously."""
async def submit_request(
self,
request: ReasoningRequest
) -> str:
"""Submit request, return job ID."""
job_id = generate_job_id()
await self.queue.publish(
topic="reasoning-requests",
message={
"job_id": job_id,
"request": request.dict(),
"submitted_at": datetime.utcnow().isoformat()
}
)
# Notify user of expected wait
await self.notify_user(
request.user_id,
f"Processing your request. Expected wait: 30-60 seconds."
)
return job_id
async def process_request(self, message: dict):
"""Worker that processes reasoning requests."""
request = ReasoningRequest.parse(message["request"])
response = await self.reasoning_model.generate(
prompt=request.prompt,
max_thinking_tokens=request.max_thinking
)
await self.result_store.set(
message["job_id"],
{
"response": response.content,
"thinking_tokens": response.thinking_tokens,
"completion_tokens": response.completion_tokens,
"completed_at": datetime.utcnow().isoformat()
}
)
await self.notify_user(
request.user_id,
"Your analysis is ready."
)
Routing Based on Complexity
class ModelRouter:
"""Route to reasoning model only when needed."""
async def route_request(
self,
request: GenerateRequest,
context: RequestContext
) -> GenerateResponse:
# Classify complexity
complexity = await self._assess_complexity(request)
if complexity == "simple":
return await self.fast_model.generate(request)
elif complexity == "medium":
return await self.capable_model.generate(request)
else: # complex
if context.can_wait:
return await self.reasoning_model.generate(request)
else:
# Fall back with disclaimer
response = await self.capable_model.generate(request)
response.add_note("For deeper analysis, use extended thinking mode")
return response
async def _assess_complexity(self, request: GenerateRequest) -> str:
indicators = [
"analyze" in request.prompt.lower(),
"step by step" in request.prompt.lower(),
len(request.prompt) > 2000,
"prove" in request.prompt.lower(),
"debug" in request.prompt.lower(),
]
complexity_score = sum(indicators)
if complexity_score >= 3:
return "complex"
elif complexity_score >= 1:
return "medium"
return "simple"
UX Considerations
reasoning_model_ux:
set_expectations:
- Show estimated wait time
- Explain why it takes longer
- Provide progress indicators
handle_long_waits:
- Allow user to continue other tasks
- Send notification when complete
- Don't block the UI
show_value:
- Display thinking summary
- Highlight confidence
- Show reasoning steps (when helpful)
Cost Management
reasoning_cost_management:
strategies:
- Route carefully (not everything needs reasoning)
- Set thinking token limits
- Cache complex analyses
- Batch similar requests
monitoring:
- Track thinking vs completion tokens
- Measure cost per successful task
- Compare to standard model outcomes
Key Takeaways
- Reasoning models trade latency for accuracy
- Use async patterns for production integration
- Route based on complexity—don’t over-use
- Set user expectations about wait times
- Monitor costs carefully—thinking tokens add up
- Cache results for expensive analyses
- Not every task needs deep thinking
- The quality improvement is real for complex tasks
- UX matters—long waits need good handling
Reasoning models are powerful. Use them strategically.