5 posts tagged with "reasoning-models"

Extended thinking wins benchmarks on novel reasoning. At a tool boundary — the moment your agent has to pick which function to call, when to call it, and what arguments to pass — that same thinking budget often makes things worse. The model weighs three equivalent tools that a fast model would have disambiguated in one token. It manufactures plausible-sounding ambiguity where none existed. It burns a thousand reasoning tokens to second-guess the obvious search call, then calls search anyway. You paid the reasoning tax on a decision that didn't need reasoning.

This is the quiet cost center of agentic systems in 2026: not the reasoning model itself, which is priced fairly for what it does well, but the reasoning model deployed at the wrong step of the loop. The anti-pattern hides in plain sight because the top-of-loop task looks hard ("answer the user's question"), so teams wrap the entire loop in high-effort thinking mode and never notice that 80% of the thinking budget is being spent deliberating on tool-choice micro-decisions the model already got right on its first instinct.

Pull the last week of production traces and look at your latency dashboard. You almost certainly have p50 and p99 on total request latency. You probably have token throughput. You may even have a tokens-per-second chart, because a provider benchmark talked you into it. What you almost certainly do not have is a per-model, per-route, per-tenant histogram of time to first token — the single number that governs how fast your product feels.

This is not a small oversight. For any streaming interface — chat, code completion, agent sidebars, voice — perceived speed is set by how long the user stares at a blinking cursor before anything appears. Once the first token lands, the user is reading; subsequent tokens compete with their reading speed, not with their patience. Total latency matters for throughput planning and budget. TTFT matters for whether the product feels alive.

The gap between these two numbers is widening. Reasoning models can produce identical total latency to their non-reasoning siblings while pushing TTFT from 400 ms to 30 seconds. A routing change that "keeps latency flat" can silently turn a snappy assistant into a hanging window. If you are not graphing TTFT, you are shipping UX regressions you cannot see.

Most teams adopting reasoning models do the same thing: they copy their existing system prompt, point it at o1 or Claude Sonnet with extended thinking, and assume the model upgrade will do the rest. Benchmarks improve. Production accuracy stays flat — or drops. The issue isn't the model. It's that the mental model for prompting never changed.

Reasoning models don't work like instruction-following models. The strategies that squeeze performance out of GPT-4o — elaborate system prompts, carefully curated few-shot examples, explicit "think step by step" instructions — were designed for a different inference architecture. Applied to reasoning models, they constrain the exact thing that makes these models valuable.

This post is a practical guide to the differences that matter and the adjustments that actually work.

Here is a number that should give you pause before adopting a reasoning model for your agent: a single query that costs 7 tokens with a standard fast model costs 255 tokens with Claude extended thinking and 603 tokens with an aggressively-configured reasoning model. For an isolated chatbot query, that is manageable. But inside an agent loop that calls the model twelve times per task, you are not paying a 10x premium — you are paying a 10x premium times twelve, compounded further by the growing context window that gets re-fed on every turn. Billing surprises have killed agent projects faster than accuracy problems.

The question is not whether reasoning models are better. On hard tasks, they clearly are. The question is whether they are better for your specific workload, at your specific position in the agent loop, and by a margin that justifies the cost. Most teams answer this incorrectly in both directions — they either apply reasoning models uniformly (burning budget on tasks that don't need them) or avoid them entirely (leaving accuracy gains on the table for the tasks that do).

There is a study where researchers asked a reasoning model to compare two numbers: 0.9 and 0.11. One model took 42 seconds to answer. The math took a millisecond. The model spent the remaining 41.9 seconds thinking — badly. It re-examined its answer, doubted itself, reconsidered, and arrived at the correct conclusion it had already reached in its first three tokens.

This is the overthinking problem, and it is not a corner case. It is what happens when you apply inference-time compute indiscriminately to tasks that don't need it.

The emergence of reasoning models — o1, o3, DeepSeek R1, Claude with extended thinking — represents a genuine capability leap for hard problems. It also introduces a new class of production mistakes: deploying expensive, slow deliberation where fast, cheap generation was perfectly adequate. Getting this decision right is increasingly central to building AI systems that actually work.