Why is OpenAI so much more efficient?

2026-06-30 · Source: Theo - t3․gg · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Advanced, extended

Summary

OpenAI's large language models demonstrate superior efficiency compared to competitors like Gemini and Fable, particularly in token usage for complex tasks such as code generation, as highlighted by the Deep SWE benchmark. GPT-55 medium achieved a high score with only 20K tokens, while Opus 4 8 used 50K tokens for a lower score, and Gemini models required 270K tokens for significantly lower performance. This efficiency stems from OpenAI's deliberate focus on optimizing internal "reasoning tokens"—the model's internal thought process before generating a final output. Leaked traces suggest OpenAI models employ a highly compressed, "Grug speak" style for this internal reasoning, drastically reducing token count and lowering operational costs, though this makes their internal processes opaque.

Key takeaway

For AI Engineers optimizing LLM deployment costs, prioritize reducing internal reasoning tokens. OpenAI's success demonstrates that highly compressed internal thought processes, even if unconventional, drastically cut overall token usage and operational expenses. You should investigate methods to minimize reasoning token generation within your models, as this yields exponential cost savings and allows more complex tasks within smaller context windows.

Key insights

OpenAI's LLM efficiency stems from highly compressed internal reasoning, drastically cutting token usage and operational costs.

Principles

• Efficiency focus drives cost reduction.
• Reasoning token count impacts overall cost exponentially.
• Internal reasoning style affects model performance.

Method

OpenAI trains models to use a highly compressed, "Grug speak" style for internal reasoning, distinct from final output, to minimize token consumption and maximize work within budget.

In practice

• Reduce reasoning tokens for cost savings.
• Optimize internal thought processes for efficiency.
• Consider token compaction for long histories.

Topics

• LLM Efficiency
• Token Optimization
• Reasoning Tokens
• OpenAI GPT-55
• Deep SWE Benchmark
• Grug Speak
• Context Management

Best for: NLP Engineer, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• Efficient and Reasoning AI at the ACL 2026 — LLM efficiency and reasoning advancements address forgetting, speed, and memory, while benchmark reliability remains a critical concern.
• Reduce Your AI API Bill By Up To 60% With These Open Source Projects — AI API cost reduction for coding agents demands targeted token efficiency, addressing specific causes like verbosity or unused tools, not just generic terseness.
• Researchers let AI Agents Optimize LLM Reasoning and Cut Tokens by 70% — AI agents can autonomously optimize LLM reasoning strategies, significantly reducing token usage while maintaining accuracy.
• Disable “Thinking,” Still Get Thousands of Tokens: What Instruct LLMs Are Doing — Many "Instruct" LLMs implicitly use significant reasoning budgets, skewing benchmark results and increasing inference costs.
• Thinking with Reasoning Skills: Fewer Tokens, More Accuracy — Distilling and retrieving reusable reasoning skills significantly improves LLM efficiency and performance.
• OpenAI just dropped GPT-5.5... (WOAH) — GPT 5.5 offers higher intelligence and token efficiency, especially for agentic coding and knowledge work, at a lower effective cost.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Theo - t3․gg.