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Giving an AI model more time or computation to reason can help on some difficult problems—but it does not guarantee a better answer. Studies published in 2025 and 2026 report diminishing gains, cases where models reason away from answers they had previously gotten right, and benchmark-specific links between more reasoning-token use and lower accuracy. These findings concern particular models and tests, not every product with a “reasoning” setting.
What does “reasoning mode” actually mean?
“Reasoning mode” is a convenient umbrella term for systems that spend additional computation before returning an answer. Researchers measure this in different ways, including test-time compute, reasoning-token counts and chain-of-thought length. These measures are related, but they are not interchangeable—and neither is a provider’s “high” or “low” setting a universal measure of how much useful thinking a model performs.
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This is also different from improving a model through training. More inference-time computation gives a model more resources while it answers; a stronger model may use those resources more effectively. A 2026 Scientific Reports study, for example, found that o3-mini medium outperformed o1-mini without producing longer reasoning chains. That illustrates why token count alone cannot tell you which model will do better.
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How can more reasoning make an answer worse?
A model may reason past a correct answer
Findings of ACL 2026 describes “overthinking” as extended reasoning associated with a model abandoning an answer that was previously correct. The authors also report that the best thinking length varies with problem difficulty and that moderate stopping budgets maintained comparable accuracy while using less computation in their evaluation. This does not establish that longer reasoning caused every reversal, or that the same pattern occurs in every deployed model.
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Performance can rise, then fall
A NeurIPS 2025 paper, Does Thinking More Always Help? Mirage of Test-Time Scaling in Reasoning Models, reports an initial performance improvement followed by a decline as test-time thinking increased across its evaluated models and benchmarks. In other words, the relationship was not simply “more compute, more accuracy.”
The paper also evaluated a parallel-thinking approach: generating independent reasoning paths and selecting a response supported by consistent paths. Its authors reported up to 20% higher accuracy than extended thinking in their evaluations. That is a result for their method and test settings, not a guarantee that parallel reasoning will outperform a product’s built-in reasoning mode.
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Why does the difficulty of the question matter?
OptimalThinkingBench, presented at ICLR 2026, tests simple general queries across 72 domains and simple math alongside challenging reasoning tasks and difficult math. It evaluates 33 models, including models designed to think and models that do not use a reasoning mode. The authors report overthinking on simple prompts and underthinking by large non-thinking models on harder tasks; no evaluated model optimally balanced effort across the benchmark.
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That makes a single “think longer” rule a poor fit for mixed workloads. A short question may not benefit from extra deliberation, while a difficult one may need more than a model’s default allocation. The right amount of effort depends on the task, and a setting that helps one category of problem may waste resources or hurt results on another.
What do the token-use statistics show—and what don’t they show?
A 2026 Scientific Reports study examined o1-mini and o3-mini variants on Omni-MATH, a mathematics benchmark. The authors estimated an average marginal decrease in answer accuracy per additional 1,000 reasoning tokens, controlling for difficulty and domain. These are model- and benchmark-specific regression estimates, not general AI error rates:
| Model and setting | Reported association | Scope |
|---|---|---|
| o1-mini | 3.16% average marginal decrease in answer accuracy per additional 1,000 reasoning tokens | Omni-MATH; estimate by the 2026 Scientific Reports study authors, controlling for difficulty and domain |
| o3-mini medium | 1.96% average marginal decrease per additional 1,000 reasoning tokens | Omni-MATH; estimate by the same study authors, controlling for difficulty and domain |
| o3-mini high | 0.81% average marginal decrease per additional 1,000 reasoning tokens | Omni-MATH; estimate by the same study authors, controlling for difficulty and domain |
The results describe associations within that evaluation; they do not prove that adding tokens caused accuracy to fall. The authors note that difficult or unsolvable questions may themselves prompt more token use and that differences within a model tier may also matter. They could not fully rule out those explanations.
The study also reports a compute tradeoff between o3-mini settings: high used more than twice as many reasoning tokens on average as medium and gained 4% accuracy. High also spent extra tokens on some problems that medium had already solved. That is a benchmark result, not a general promise about the improvement a user should expect from choosing a higher setting.
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OpenAI’s 2026 CoT-Control evaluation measures a different property: whether a model follows instructions that constrain the form of its chain of thought. It covers more than 13,000 tasks from established benchmarks and tests 13 reasoning models. OpenAI reports controllability scores ranging from 0.1% to 15.4% across the tested frontier models, with controllability decreasing as test-time compute increased.
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Those percentages measure compliance with chain-of-thought instructions, not final-answer accuracy, hallucination rates or the likelihood that a user receives a harmful answer. OpenAI describes the tasks as practical proxies and says the reason for the low controllability scores is not yet understood. This safety-related measurement should not be treated as another accuracy result.
How should you choose or evaluate a reasoning setting?
Do not judge a model by how long its answer appears to deliberate, or assume that a “high” setting is more reliable by definition. For a meaningful comparison, focus on the task you actually need it to perform and consider:
- Task and difficulty: Check performance on the relevant subject and on questions at the difficulty you face.
- Final-answer accuracy: Compare how often the model reaches a correct, usable answer—not how many reasoning tokens it emits.
- Compute and time: More inference-time computation can use more resources. Treat latency as a separate consideration unless the evaluation measured it.
- Evidence quality: Look for a direct comparison on the same tasks and settings. An observed association does not establish that extra reasoning caused the outcome.
- Verifiability: Check important answers against reliable sources, calculations or other independent evidence, especially when mistakes would have consequences.
Microsoft Research has also reported that scaling chain-of-thought length impaired performance in certain mathematical reasoning domains. Taken together, these studies support a conditional conclusion: extra reasoning can help, do little, or backfire depending on the model, task and evaluation. They do not establish a universal rule for every AI product—or let a general “thinking” label predict whether an individual answer is trustworthy.
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