When does AI stop thinking too much?

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In 2026, the "Token can't burn" phenomenon caused by LLM's "overthinking" problem and the "really overhead overhanging" phenomenon forced the entire industry from an early blind pursuit of the length of the thought chain to a comprehensive review of rational pricing and adaptive computational resilience. This model "can't stop thinking is not a mere technical redundancy, but a systematic interweaving of current inference-period search engine flaws, rigidity of credit distribution and lack of model cognitive retrieval capabilities".

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01. How severe is the negative impact of "thinking too much" on LLM?
In addition to Token bills, what are the negative impacts of overthinking LLM?...

How serious is the negative impact of "thinking too much" on LLM?

Test-Time Compute Scaling has been recognized as a core pillar of complex logical breakthroughs in big language models. LLM demonstrates its superior ability in advanced mathematical inference, algorithm-writing, and complex multi-step decision-making by introducing iteration, reflection, and regression driven by explicit long chain thinking in the process of self- regression generation.

2 However, with the widespread deployment of LLM in the industry, the systemic inefficiencies of the "overthinking" model have been rapidly highlighted, with protracted reasoning trajectories leading to increased query costs and delays, and intensive loaded reasoning significantly increasing infrastructure stress, constituting a major bottleneck for AI landing.[1-1]

1 "Overthinking" refers to the fact that many models that have derived the correct answer early in the generation, and have even clearly drawn phased conclusions in the preceding context, are still unable to close the loop, but are caught up in unstoppable identity verification, false hypotheses and self-doubt.

2 More Thinking Hurts work statistics from April 2026 indicate that the average number of tokens ejected in a single generation by a long-range model has reached about eight times that of the traditional model, and that the computational energy and cost of generating 8,000 words is more than 16 times that of generating 500 words.[1-3]

3 As bulk computing resources are consumed in meaningless drifting delays, and enterprises and developers fall into heavy rational pricing mudslides when adopting LLM services, the strongest pain in the industry's rational model is the severe rift between the nominal API benchmark and the actual landing bill, resulting in the "Pricing Reversal" phenomenon.[1-2]

1 The industry is accustomed to estimating deployment costs based on a single price per million Token tokens released by manufacturers, but in the long-range logic model, the uncontrolled derivation of sequence length directly disrupts this evaluation logic.

2 Researchers from institutions such as Stanford University and CMU, in their May 2026 "Pricing Reversal" study, matched the pricing of LLM APIs to the actual cost of reasoning, finding that 32% of the "cheaper" model combinations at nominal prices outweighed the total cost of actual workloads.

3 As an example of the MMLU-Pro high difficulty test benchmark, some designations for single-priced models that are very low (80 percent cheaper than front-end flagship models) rational lightweight models generate more than 60,000 thought words to solve single-choice puzzles, while large front-end models require only 25 words to directly hit the core solution.

4 On top of the stress of the Token bill, the long thinking chain should have led to the expectation of better responses, and the "overthinking" issue has led to the reversal of the quality of LLM responses.[1-3]

1 More Thinking Hurts data shows that as the length of thought increases, the driving trajectory of reversing the correct answer to the wrong conclusion "negative flips" often escalates dramatically.

2 Based on the "Flip Events" statistics in LLM logical tracking, the ratio of negative to negative flips to right corrections crosses the 1.0 loss critical point when the thought sequence reaches about 7,000 words and rapidly worsens to 7.55 when it reaches 16,000 words.

In addition, in the SWE-bench Verified software engineering interaction, the problem of "overthinking" is further demonstrated as a serious "analysis paralysis", which makes it extremely easy for the model to depict the wrong modeling assumptions as true and eventually fall into a dead cycle before the maximum step limit is exhausted.[1-4]

1 Long-range logic models tend to repeat self-expression in internal and external contexts, drastically delaying the use of real tools to verify code execution results, and their over-thinking models tend to score nearly three times higher than conventional models.

Why is it so hard to come to terms with the long rationale behind the return of an LLM?

1, recent work suggests that models that cannot stop thinking instinctively exhibit slow activation of termination markers such as </think> or EOS, but their underlying logic lies in inference-period search traps, reinforced credit assignment fallacies, inherent self-correcting bias vulnerabilities, and the systemic flaws that make up the synchronous dynamics of decomposition.