Reasoning Models — Middle¶

At middle level, focus on this question:

Can you classify your tasks into reasoning-helps and reasoning-wastes, control the effort dial, and explain why high temperature sabotages reasoning?

Classify every task before choosing the model class¶

Task shape	Reasoning model?	Why
Extraction, formatting, templating	No	Nothing to figure out; deliberation is pure overhead.
Classification, routing	No	Pattern-matching, not multi-step logic; standard model at temp 0 is equal and cheaper/faster.
Lookup, summarization	No	The difficulty is information access (retrieval), not inference.
Multi-step math, constraint problems	Yes	Step-checking catches the early plausible-but-wrong answer.
Planning and decomposition	Yes	Ordered sub-tasks before acting is exactly what thinking tokens produce.
Hard debugging, root-cause style analysis	Yes	Form-and-discard hypotheses needs deliberation.
Tool-use workflows	Usually no	The loop's iterative tool calls are the deliberation; a reasoning model adds a redundant inner one (see Senior).

Most reasoning models expose reasoning effort (low/medium/high, or a token budget): how much thinking to allow before answering.
Higher effort → better on genuinely hard tasks → more tokens, more latency, on every request. It's a cost dial as much as a quality dial.
Match effort to task difficulty: low for moderate tasks, high only for the hard tail. A fixed "high everywhere" setting is the most common way to 10× a bill for marginal quality.

Reasoning depends on coherent multi-step chains: each thinking step builds on the last, and a single off-track token can send the whole chain wandering.
High temperature deliberately increases the chance of off-track tokens. Stacking temperature 1.0 onto a reasoning task is paying for deliberation and then injecting noise into it — the chain derails more often, and you pay for the derailed attempts.
Rule: reasoning tasks want low temperature (often the model ignores the setting anyway — many reasoning classes use fixed internal sampling). Don't "make it creative" on a math or planning path.

Typical pattern for a moderately hard task: standard model ≈ 600 output tokens, ~2s. Reasoning model at medium effort ≈ 2,500 thinking + 600 visible tokens, ~15s.
That's ~4–5× the output cost and ~7× the latency — fine for a nightly batch analysis; unacceptable inside a chat reply.
Measure your own multiplier from usage fields; it varies by task and effort setting.

"High effort everywhere." Pays peak prices on easy requests that never needed thinking.
High temperature on reasoning paths. Directly sabotages the chain-of-thought coherence you're paying for.
Choosing a reasoning model to fix retrieval failures. The model reasons beautifully over the wrong or missing facts you gave it.
Ignoring latency until users complain. Hidden thinking time lands entirely on the interactive path; measure it per task before shipping.

Put every LLM task in your app through the table above; mark each reasoning / standard, with one-line justification.
For any reasoning usage, set the effort dial per task difficulty — and find the multiplier (tokens, seconds) on a real task, not from docs.
Audit sampling settings on every reasoning path: confirm low temperature, and note if the model overrides it anyway.

Every reasoning-model choice maps to a "Yes" row in the task table, not to importance or novelty.
Effort settings vary with task difficulty, and the cost multiplier is measured from usage data.
No reasoning path runs at creative temperature.

Why is a tool-use agent loop usually the wrong place for a reasoning model?
What does the effort dial actually control, and why is "high everywhere" a bill multiplier?
Why does high temperature specifically sabotage reasoning quality?