When Reasoning Helps (and When Not)

Reasoning models are not universally better - they are slower, more expensive, and overkill for simple tasks. This page gives you a practical framework for deciding when to route to a reasoning model vs a standard model.

The Core Question

A reasoning model helps when the task has a verifiable correct answerthat requires multiple non-trivial steps to reach. The extra thinking adds value when:

1. There is a right answer (not just a preferred style)
2. Finding it requires checking intermediate steps
3. Mistakes in intermediate steps cascade to wrong final answers

When none of these conditions hold, the thinking tokens are wasted - you pay more and wait longer for no quality gain.

Tasks That Benefit From Reasoning

Tasks That Don't Benefit

The Latency Trade-off

Reasoning model response times (time-to-final-answer):

• GPT-6 Luna: 5–15 seconds
• Claude Sonnet 5, effort low-medium: 15–45 seconds
• GPT-6 Astra / Claude Fable 5.1, full effort: 1–5+ minutes
• DeepSeek-R1 (API): 15–60 seconds depending on problem complexity

These latencies are acceptable for async workflows (nightly analysis, batch processing, background agents) but are disruptive in synchronous user-facing apps. Design your UX accordingly.

The Cost Trade-off

At 10,000 hard queries/day, the difference between GPT-6 Luna and full-effort GPT-6 Astra is roughly $500 vs $5,000 per day at typical per-query costs. Reasoning should be reserved for tasks where the quality improvement justifies the cost.

Routing Pattern: Classify First

In production systems, automatically routing queries to the right model is more cost-effective than always using a reasoning model. A simple approach:

1. Fast classifier - Use a small, cheap model to classify the incoming query: is it a reasoning task or not?
2. Route accordingly - Reasoning tasks go to GPT-6 Astra/DeepSeek-R1; everything else goes to GPT-6 Luna or a smaller model
3. Fallback - If the standard model returns low confidence, retry with the reasoning model

Adjustable Effort: Start Low

Reasoning is now typically a dial, not a separate model choice - Claude Sonnet 5, for example, exposes an effort-level setting from low to xhigh on the same model rather than requiring you to switch to a different SKU. The recommended approach:

1. Default to GPT-6 Luna, or Sonnet 5 at low-medium effort for any task you've classified as reasoning-intensive
2. If the answer quality is insufficient, escalate to a higher effort level
3. Only move to GPT-6 Astra or Claude Fable 5.1 at full effort if the lower tiers consistently fail on the task type
4. Use DeepSeek-R1 when data residency is required or for cost-sensitive high-volume reasoning workloads

Reasoning in Agentic Systems

One of the most impactful uses of reasoning models is in multi-step agents. Standard models performing long chains of tool calls accumulate errors - each step has some probability of failure, and errors compound. Reasoning models are dramatically more reliable as the "planner" or "orchestrator" because:

• They can check their own plan for inconsistencies before executing
• They can reason about failure modes and build in contingencies
• They recover more gracefully when a tool call returns unexpected results

A common pattern: use a reasoning model for the initial planning step and for error recovery, then use a faster/cheaper model for execution steps that are straightforward once the plan is set.

RAG + Reasoning

Reasoning models do not replace retrieval. A reasoning model will reason very well over the information it has - but if it doesn't have the relevant information, it will hallucinate convincingly reasoned-but-wrong answers. The combination that works:

1. Retrieve relevant documents with a fast retrieval model / vector search
2. Pass retrieved chunks as context to the reasoning model
3. Let the reasoning model synthesise and reason over that context

This gives you both fresh / private knowledge (from retrieval) and deep reasoning quality (from TTC).

Testing Whether Reasoning Helps Your Use Case

If you're unsure whether a reasoning model improves your specific application:

1. Collect 50–100 representative examples from your use case
2. Run them through both a standard model and GPT-6 Luna
3. Evaluate outputs using your quality metric (correctness, user rating, etc.)
4. Calculate the quality delta and cost delta
5. If quality delta / cost delta ratio justifies it, switch