Reasoning Model
A type of large language model that performs chain-of-thought reasoning before producing an answer. Such models are well-suited to mathematical derivation, code generation, and complex analysis, and typically consume five to twenty times more tokens than standard models.
Core Facts
Reasoning models perform better in information synthesis tasks requiring multi-step logical deduction, but have higher latency and should be selected appropriately based on task characteristics.
Reasoning models, represented by OpenAI o1, DeepSeek-R1, and QwQ, have chain-of-thought expansion as their core mechanism, generating a large number of intermediate reasoning steps before producing a final answer.
High-intensity reasoning configurations typically entail higher costs and longer response times, trading response speed for logical rigor.
Timeline (last 90 days)
The original statement mentions "we deploy voice models physically close to reasoning models," i.e., deploying voice models physically close to reasoning models.
For general-purpose models, COT needs to be written explicitly into the prompt; for reasoning models, COT has been internalized as implicit thinking and requires no user guidance.
For math-proof tasks, a reasoning model can be told directly "prove the Pythagorean theorem," whereas a general-purpose model needs "please derive the Pythagorean theorem in three steps."
The root cause of unstable output quality is often writing the SOP too narrowly; the correct approach is to give principles rather than lock down steps—write strict steps for stages with objective right-or-wrong standards, and write principle frameworks for stages that depend on judgment.
Reason management relies on internal derivation, allowing the model to derive scenarios that were not written down, and the inference cost is roughly independent of the number of scenarios.
The more powerful the model, the better it is at finding rule loopholes and transferring reasons, so betting on rules is logically in contradiction with the evolution of model capabilities.
Extended reasoning models silently generate 4,000 to 12,000 hidden chain-of-thought tokens per API call, and these tokens are billed at the high output-token rate.
The end-to-end latency of the standard non-reasoning model and the default unbounded reasoning model is 1.15 seconds and 14.2 seconds, respectively.
Math, code and structured data extraction are suitable for reasoning models because they naturally come with checkers, whereas open-ended writing, chitchat and subjective recommendations are not, because right and wrong cannot be automatically verified.
CoT prompting, without changing the model architecture, acts as an implicit working memory buffer by explicitly writing the intermediate reasoning process into the output sequence, thereby improving reasoning depth; but its limitation is that once the reasoning chain is generated it cannot be modified, and errors propagate and amplify along the chain.
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All Facts (20)
Reasoning models perform better in information synthesis tasks requiring multi-step logical deduction, but have higher latency and should be selected appropriately based on task characteristics.
90%VerifiedReasoning models, represented by OpenAI o1, DeepSeek-R1, and QwQ, have chain-of-thought expansion as their core mechanism, generating a large number of intermediate reasoning steps before producing a final answer.
90%VerifiedHigh-intensity reasoning configurations typically entail higher costs and longer response times, trading response speed for logical rigor.
90%VerifiedChain of Thought能显著提升模型在数学推理、逻辑判断等需要多步推导的任务上的表现
90%VerifiedThe o-series models combine chain-of-thought reasoning with reinforcement learning, with the model generating internal thinking steps before producing an answer.
85%VerifiedThe new generation of voice interaction has shifted to an end-to-end multimodal model architecture that understands and generates directly at the speech signal level, compressing latency and preserving paralinguistic information.
80%Verified推理能力基于思维链（Chain-of-Thought, CoT）技术，模型在给出最终答案前生成内部逐步推导过程
80%VerifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.
75%VerifiedThe reasoning paradigm is essentially the application of reinforcement learning to the post-training stage of language models, allowing the model to autonomously explore reasoning paths and receive reward feedback based on answer correctness, first publicly demonstrated in the o1 series of models.
75%VerifiedMax mode raises the lower bound of reasoning tokens rather than the upper bound, forcing the model to reach a certain amount of reasoning before it is considered complete, which often harms performance.
70%VerifiedThe internal chain of thought in reasoning models generates a large number of output tokens, making the actual cost of completing complex tasks often far higher than what a per-token price comparison would suggest.
70%VerifiedResearch has found that the chain-of-thought reasoning generated by models does not fully correspond to their actual internal computation processes, and may provide post-hoc explanations unrelated to the true decision-making path.
70%VerifiedThinking tokens for reasoning models are typically billed, and setting a token budget cap can prevent runaway costs from a single request.
65%VerifiedChain-of-Thought（思维链）提示技术通过强制模型先输出中间步骤来部分缓解自回归模型在逻辑推理中的缺陷
65%VerifiedReasoning models, represented by OpenAI's o series, DeepSeek-R1, and the Kimi K series, perform a chain-of-thought process before outputting, decomposing problems into multiple steps, performing self-verification, and deriving answers incrementally.
65%Verified思考模型在输出前会经历内部链式推理（Chain-of-Thought）过程，在数学证明、代码调试、复杂逻辑推理等任务上表现优于标准模型，代价是延迟更高、计算成本更大
65%VerifiedReasoning tasks require a strong logical chain while summarization tasks demand concise language ability, and using the same model for both not only wastes computing power but may also not yield the best results.
65%VerifiedModern reasoning models generally use RLHF or its variant RLAIF for alignment training, where the model learns during training when to stop reasoning and when to output an answer.
65%UnverifiedPaths for improving model intelligence include increasing training and inference computation, optimizing reasoning architectures, and strengthening chain-of-thought training.
70%UnverifiedReasoning models have surpassed a large number of human experts in math competitions, programming, and logical reasoning.
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