Are reasoning models smarter?

They are better at some multi-step tasks and worse as a universal default. Measure.

Why is the bill so high?

Thinking tokens and retries. Cap reasoning effort if the API allows it.

Can open models reason?

Some distilled reasoners exist. Compare their quality index and then run your hard eval.

Should I use them for RAG?

Only for multi-hop questions. FAQ RAG should stay on a fast model.

Reasoning models explained — extra thinking, extra tokens

Reasoning models spend more compute (and often more billed tokens) before they answer. They help on multi-step math, hard coding, and planning. They waste money on “what’s the weather in the policy docs.”

Compare reasoning SKUs to each other and to a strong non-reasoning flagship on cost per correct answer, not cost per million tokens alone. See also o-series vs Claude.

Highest quality index right now

Live OpenRouter pricing and Arena quality, cached about an hour. Not a static blog table.

Model	Provider	Blend $ / 1M	Quality	Context
Qwen3.8 Max (0902)	Qwen	4.00	95.1	1,000,000
Claude Opus 5	Anthropic	15.00	93.4	1,000,000
Claude Fable 5	Anthropic	30.00	91.1	1,000,000
GLM 5.3 Flash	Z.ai	0.16	89.7	1,048,576
Qwen3.8 27B	Qwen	1.71	89.0	1,000,000

Hidden tokens

Some APIs bill a chain of thought you never see. Your observability must log billed tokens, not just visible output. Otherwise the “cheap” reasoning row is a surprise.

When they win

Problems with a checkable answer: code that must pass tests, math, structured plans. When the answer is taste (“make this landing page friendlier”), extra thinking is a slow thesaurus.

Latency

Users wait for the thinking. For chat, consider a fast draft plus an optional “think harder” button that calls the reasoning SKU.

Arena Elo distortion

Crowd voters may reward long, careful answers — or punish slowness. Don’t overfit quality index when choosing a reasoning default.

Architecture pattern

Classifier (tiny) → solver (reasoning) → narrator (cheap). Most graphs should not be 100% reasoning models. Use the leaderboard to pick each node’s id.

FAQ

Are reasoning models smarter?: They are better at some multi-step tasks and worse as a universal default. Measure.
Why is the bill so high?: Thinking tokens and retries. Cap reasoning effort if the API allows it.
Can open models reason?: Some distilled reasoners exist. Compare their quality index and then run your hard eval.
Should I use them for RAG?: Only for multi-hop questions. FAQ RAG should stay on a fast model.

reasoning models explainedwhat is o1LLM chain of thoughtextended thinking models

Compare o-series vs Claude