Jan 2025 • 6 min read
Chain-of-Thought Prompting: The 2025 Reality Check
Understanding when chain-of-thought prompting helps—and when it doesn't—with modern LLMs.
What is Chain-of-Thought Prompting?
Chain of thought (CoT) is a prompt engineering technique that enhances the output of large language models (LLMs), particularly for complex tasks involving multistep reasoning. It essentially involves adding "Let's think step by step" to the original prompt.
Basic Prompt:
"What is 15% of 280?"
CoT Prompt:
"What is 15% of 280? Let's think step by step."
The 2025 Reality: Diminishing Returns
Recent research from 2025 reveals that modern AI models show diminishing returns from Chain-of-Thought prompting, with gains rarely worth the time cost. A June 2025 report found that effectiveness varies significantly by model type and task.
Key Findings from 2025 Research
- Non-reasoning models: Show modest average improvements but increased variability in answers
- Reasoning models: Gain only marginal benefits despite substantial time costs (20-80% increase)
- Simple tasks: Adding CoT prompts can sometimes reduce performance by overcomplicating the reasoning process
CoT as a "Mirage"
Research from August 2025 reveals that CoT reasoning is a brittle mirage that vanishes when pushed beyond training distributions. This challenges the assumption that CoT universally improves LLM performance.
When CoT Actually Helps
Model Size Matters
CoT only yields performance gains when used with models of ∼100B parameters or larger. Smaller models wrote illogical chains of thought, which led to worse accuracy than standard prompting.
Complex Reasoning Tasks
CoT still provides value for genuinely complex multi-step reasoning:
- Multi-step mathematical word problems
- Logical deduction requiring multiple premises
- Planning tasks with interdependent steps
- Code debugging requiring systematic analysis
When to Skip CoT
With Reasoning Models
Many models perform CoT-like reasoning by default, even without explicit instructions. Adding "think step-by-step" is often redundant with models like GPT-4, Claude 3.5, and Gemini 2.0.
Simple Tasks
For straightforward questions, CoT adds latency without improving accuracy. The model already knows the answer—asking it to show work just wastes time.
Latency-Sensitive Applications
CoT significantly increases response time (20-80% slower). If speed matters more than marginal accuracy gains, skip it.
CoT Variants and Techniques
Zero-Shot CoT
Simply add "Let's think step by step" or "Think through this carefully." No examples needed. Works surprisingly well with large models.
Few-Shot CoT
Provide examples of step-by-step reasoning. The model learns the pattern and applies it to new problems. More reliable but requires crafting good examples.
Self-Consistency
Generate multiple CoT reasoning paths and select the most common answer. Improves accuracy but multiplies API costs.
Tree of Thoughts
Explore multiple reasoning branches in parallel. The model evaluates different approaches before committing. Powerful but expensive.
Practical Guidelines for 2025
Test Before Deploying
Don't assume CoT helps. A/B test with and without CoT on your specific use case. Measure both accuracy and latency.
Use Selectively
Apply CoT only to tasks that genuinely need multi-step reasoning. For simple queries, use standard prompts.
Consider the Cost
CoT generates more tokens (longer responses). This increases API costs and latency. Balance accuracy gains against these costs.
Alternative Approaches
Better Prompts
Often, clearer instructions work better than CoT. Specify exactly what you want, provide context, and give examples.
Better Models
Upgrading from GPT-3.5 to GPT-4 or Claude 3.5 often provides larger accuracy gains than adding CoT to the weaker model.
Structured Outputs
Request responses in JSON or other structured formats. This can improve reliability without the overhead of explicit reasoning steps.
The Bottom Line
Chain-of-thought prompting was revolutionary when introduced in 2022. By 2025, modern LLMs have internalized much of this capability. CoT still has its place for genuinely complex reasoning tasks with large models, but it's no longer the universal solution it once seemed.
Test empirically, use selectively, and always consider the latency and cost tradeoffs. Sometimes the simplest prompt is the best prompt.
Sources
This article was generated with the assistance of AI technology and reviewed for accuracy and relevance.