When should I use a reasoning model?

When the task rewards deliberation and the cost of a wrong answer dominates the cost of thinking longer: multi-file coding diffs, root-cause debugging, planning at the start of an agent loop, legal/financial analysis, complex SQL. Don't use them for classification, summarization, tool-picking on a small set, or turn-by-turn support conversation — you pay 5-30× more for output a cheap model would produce equally well.

How do reasoning models differ across vendors?

Three axes matter. Visibility: OpenAI hides the reasoning trace, Anthropic and Google and DeepSeek show it. Design: OpenAI ships separate SKUs (o-series), Anthropic and Google use a hybrid parameter on the same weights. Cost: DeepSeek R1 is 1/10-1/30 the frontier US labs' price at similar reasoning quality, at the cost of lighter safety alignment. Pick by what your compliance/audit context requires.

Reasoning Model

Written by Max Zeshut

Founder at Agentmelt

An AI model that explicitly 'thinks through' problems step-by-step before producing an answer, spending additional Test-Time Compute on complex tasks. The 2025-26 landscape: OpenAI o-series (o1 → o3 → o3-Pro → o4-mini), Anthropic Claude with Extended Thinking (Sonnet 3.7 / Sonnet 4 / Opus 4), Anthropic uses a Hybrid Reasoning Model design where thinking is a per-request parameter on the same weights; DeepSeek R1 (Jan 2025) and its distilled Llama/Qwen family reset the price-performance frontier for open-weights reasoning; Google Gemini 2.5 series added configurable thinking. Reasoning models excel at math, logic, coding, and multi-step planning — but degrade on simple tasks and cost 3–30× more per response. The production question isn't reasoning-or-not, it's Model Routing and Reasoning Effort per turn.

Пример

A coding agent is asked to fix a subtle race condition in a payment processor. A non-reasoning model produces a plausible-looking diff that removes the symptom but reintroduces the race under a different concurrency pattern; a reasoning model (o3 or Sonnet 4 with 32K thinking tokens) walks through the invariants, identifies that the fix needs to be at the locking layer not the retry layer, and produces a diff that passes both the failing test and three new tests it proposes. Cost is ~5× higher; a single wrong-fix debugging cycle would cost 10-20× more in human time. Full 2026 landscape and routing patterns: /blog/reasoning-models-for-ai-agents-2026/.

Часто задаваемые вопросы

When should I use a reasoning model?: When the task rewards deliberation and the cost of a wrong answer dominates the cost of thinking longer: multi-file coding diffs, root-cause debugging, planning at the start of an agent loop, legal/financial analysis, complex SQL. Don't use them for classification, summarization, tool-picking on a small set, or turn-by-turn support conversation — you pay 5-30× more for output a cheap model would produce equally well.
How do reasoning models differ across vendors?: Three axes matter. Visibility: OpenAI hides the reasoning trace, Anthropic and Google and DeepSeek show it. Design: OpenAI ships separate SKUs (o-series), Anthropic and Google use a hybrid parameter on the same weights. Cost: DeepSeek R1 is 1/10-1/30 the frontier US labs' price at similar reasoning quality, at the cost of lighter safety alignment. Pick by what your compliance/audit context requires.

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