For most of 2023 and 2024, the answer to “which AI is best?” was straightforward: ChatGPT. OpenAI had the most capable model, the largest user base, and the strongest brand. Competitors existed, but they were playing catch-up.
In March 2026, that answer is more complicated. GPT-5.4 is a genuinely powerful model with capabilities its predecessors lacked — particularly its thinking mode, which makes chain-of-thought reasoning visible and inspectable. But the competition has closed the gap in meaningful ways, and “king” is no longer an obvious title to assign.
This is a detailed examination of GPT-5.4’s thinking mode: what it does, how it compares, and whether it is enough to maintain ChatGPT’s position at the top.
Key Takeaways
- GPT-5.4’s thinking mode enables transparent chain-of-thought reasoning, letting users see and verify the model’s reasoning process before accepting its conclusions.
- The feature is strongest in math, logic, multi-constraint problems, and coding tasks where step-by-step reasoning directly improves accuracy.
- Competing approaches — Claude’s Constitutional AI reasoning, DeepSeek R1’s cost-efficient reasoning, and Gemini 3.1 Pro’s multimodal reasoning — each offer advantages in specific domains.
- ChatGPT’s overall platform (SearchGPT, GPT Image, Operator, GPT Store) remains its strongest competitive advantage, even if the model itself is not universally superior.
What Thinking Mode Actually Does
GPT-5.4’s thinking mode is not just a UI feature — it reflects a fundamental change in how the model approaches problems. When activated, the model engages in explicit chain-of-thought reasoning: breaking complex questions into sub-problems, working through each step, and presenting the reasoning path alongside the final answer.
In practice, this looks like a structured process:
- Problem decomposition: The model identifies the key components of the question and any constraints that apply.
- Step-by-step reasoning: Each sub-problem is addressed sequentially, with intermediate conclusions that feed into subsequent steps.
- Self-correction: The model can identify errors in its own reasoning during the process and backtrack to correct them.
- Transparent output: The user sees both the reasoning chain and the final answer, allowing them to verify the logic.
This is distinct from how earlier GPT models (and most LLMs) typically operate, where the model generates a final answer in a single pass without exposing intermediate reasoning. Thinking mode trades speed for accuracy and transparency — responses take longer, but they are more reliable for complex tasks.
Where Thinking Mode Excels
Mathematical and Logical Reasoning
This is thinking mode’s strongest domain. Problems that require multiple logical steps — algebraic proofs, optimization problems, statistical analysis, formal logic — benefit directly from explicit chain-of-thought processing.
The improvement over non-thinking-mode responses is measurable. For multi-step math problems, thinking mode significantly reduces the kind of errors that occur when a model tries to jump directly to an answer: sign errors, dropped variables, incorrect application of formulas. By working through each step explicitly, the model catches mistakes it would otherwise make.
Multi-Constraint Decision Making
When a question involves multiple competing constraints — “recommend a laptop under $1,500 that is good for video editing, has at least 32GB RAM, weighs under 4 pounds, and has a 15+ inch screen” — thinking mode systematically evaluates each constraint rather than pattern-matching to a common answer.
This is particularly useful for professional tasks like vendor evaluation, project planning, or risk assessment, where decisions involve balancing multiple factors that may conflict.
Code Generation and Debugging
Thinking mode improves coding tasks by making the model plan its approach before writing code. Instead of generating a function and hoping it works, the model outlines the algorithm, identifies edge cases, and structures the implementation — then writes the code.
For debugging, thinking mode is especially valuable. The model can trace through code execution step by step, identify where the logic deviates from the expected behavior, and explain the root cause before proposing a fix.
Complex Writing Tasks
For writing that requires structural planning — research reports, multi-section articles, strategic analyses — thinking mode helps the model organize its approach before generating content. The result is more coherent structure and better argumentation, particularly for pieces that need to sustain a complex argument across thousands of words.
Where Thinking Mode Falls Short
Simple Tasks
For straightforward questions — “what is the capital of France?” or “write a friendly email declining a meeting invitation” — thinking mode adds latency without meaningful quality improvement. The overhead of explicit reasoning is wasted on tasks that do not require multi-step logic.
Creative Writing
Thinking mode’s systematic approach can work against creative tasks. Creative writing often benefits from associative leaps, unexpected connections, and intuitive flow — qualities that a step-by-step reasoning process can suppress. Users who valued GPT-4o’s creative warmth (the personality whose retirement sparked the “#Keep4o” movement on February 13, 2026) may find thinking mode even further from the spontaneous, personality-rich output they prefer.
Speed-Sensitive Applications
Thinking mode responses take noticeably longer than standard responses. For applications where response time matters — real-time customer support, interactive tutoring, rapid brainstorming — the latency cost may outweigh the accuracy benefit.
Acknowledging Uncertainty
GPT-5.4’s thinking mode tends toward decisive conclusions. While the reasoning chain is transparent, the model does not always adequately flag genuine uncertainty or present competing interpretations. It works through a problem and arrives at an answer — but for truly ambiguous problems, presenting multiple valid conclusions would sometimes be more honest than presenting one confident one.
How Competitors Approach Reasoning
Claude Opus 4.6: Constitutional Reasoning
Anthropic’s approach to reasoning is philosophically different from OpenAI’s. Claude’s Constitutional AI framework means its reasoning is shaped by explicit principles about honesty, harmlessness, and helpfulness. In practice, Claude Opus 4.6 (priced at $5/$25 per million tokens) tends to:
- Acknowledge uncertainty more readily than GPT-5.4
- Present multiple perspectives on genuinely ambiguous questions
- Refuse to generate confident-sounding but unsupported conclusions
- Engage more substantively with edge cases and counterarguments
Claude Sonnet 4.6, released February 17, 2026 at $3/$15 per million tokens with a 1M token context window (beta), brings near-Opus reasoning quality at a lower price point. For tasks where reasoning depth matters more than decisive answers, Claude’s approach produces outputs that are often more trustworthy — even if less immediately actionable.
DeepSeek R1: Efficient Reasoning
DeepSeek R1, released in January 2025, is a dedicated reasoning model that demonstrated strong performance on mathematical and logical reasoning benchmarks. What makes DeepSeek’s approach notable is not just the quality but the economics: DeepSeek-V3.2 offers strong reasoning at $0.28/$0.42 per million tokens.
For applications that need consistent reasoning at scale — automated code review, bulk document analysis, systematic data processing — DeepSeek’s pricing makes reasoning-intensive workflows economically viable in ways that frontier pricing does not.
Gemini 3.1 Pro: Multimodal Reasoning
Google’s Gemini 3.1 Pro, released February 19, 2026, brings reasoning capabilities that extend beyond text. Gemini can reason across text, images, video, and code simultaneously — analyzing a chart while discussing the data it represents, or reasoning about a code screenshot alongside its documentation.
For tasks that involve multiple modalities — analyzing financial reports with embedded charts, debugging code from screenshots, understanding complex diagrams — Gemini’s multimodal reasoning is a genuine differentiator that text-only thinking mode cannot match.
Grok 4.20 Beta: Real-Time Reasoning
Grok 4.20 Beta, released in February 2026, combines reasoning with real-time access to X (Twitter) data. For tasks that require reasoning about current events, social sentiment, or trending topics, Grok can incorporate live information that other models access only through separate search tools.
The Platform Advantage
Even if GPT-5.4’s thinking mode were clearly inferior to all competitors (it is not), ChatGPT would still maintain a significant competitive position because of its platform.
No other AI product offers the combination of:
- SearchGPT for real-time web information
- GPT Image (which replaced DALL-E 3 in March 2025) for visual creation
- Operator for autonomous web task execution
- Code interpreter for data analysis and computation
- GPT Store for specialized applications
This bundling creates convenience that individual model quality cannot easily overcome. A user would need to use multiple separate tools to replicate what ChatGPT offers in one interface.
ChatGPT Plus at $20/month and Team at $25-30 per seat per month provide access to all these capabilities, making the per-feature cost relatively low compared to assembling equivalent functionality from separate products.
Is ChatGPT Still the King?
The honest answer: it depends on what you mean by “king.”
If king means “most widely used AI platform” — yes, unambiguously. ChatGPT’s user base, brand recognition, and platform breadth are unmatched.
If king means “best model for reasoning” — it depends on the task. GPT-5.4 thinking mode is excellent for math, logic, and structured problems. Claude Opus 4.6 is stronger for nuanced analysis and honest uncertainty. DeepSeek offers the best reasoning-per-dollar.
If king means “best overall AI experience” — for most users, probably still yes. The platform integration (search + image + code + agents) creates a complete experience that no competitor fully matches. But for specific professional needs, specialized alternatives are often better.
The era of a single, clear “best AI” is over. What replaced it is a landscape where different tools lead in different dimensions — and the professionals who produce the best work use the right tool for each task.
How to Use GPT-5.4 Thinking Mode Today
GPT-5.4 thinking mode is available to all ChatGPT users. For maximum benefit, enable it for complex reasoning tasks — multi-step problems, code debugging, strategic analysis — and disable it for simple queries where speed matters more than reasoning depth.
For professionals who want to compare GPT-5.4’s reasoning against Claude or DeepSeek, Flowith provides a practical way to do this. Flowith’s canvas-based workspace lets you send the same prompt to multiple frontier models and compare their reasoning approaches side by side. You can see where GPT-5.4’s thinking mode produces a more decisive answer, where Claude provides more nuanced analysis, and where DeepSeek delivers comparable quality at a fraction of the cost — all in a persistent visual workspace that maintains context across sessions.
This multi-model comparison is especially useful for high-stakes reasoning tasks where you want to cross-validate conclusions across different AI approaches before making a decision.
The Bottom Line
GPT-5.4’s thinking mode is a genuine advancement. It makes AI reasoning more transparent, more reliable for complex tasks, and more trustworthy for professional use. It is one of the strongest reasoning implementations available in any commercial AI product.
But it is not categorically better than every alternative. The competition in early 2026 — Claude’s principled reasoning, DeepSeek’s efficient reasoning, Gemini’s multimodal reasoning, Grok’s real-time reasoning — has made “best” a contextual judgment rather than an absolute one.
ChatGPT remains the king of AI platforms. Whether it remains the king of AI reasoning is a question with a different answer depending on who is asking and what problem they need solved.
References
- Wikipedia, “GPT-4o” — Edited March 7, 2026. Documents GPT-5 release (Aug 2025), GPT-5.1/5.2/5.4 succession, GPT-4o retirement (Feb 13, 2026), “#Keep4o” movement, and sycophancy rollback (Apr 2025).
- OpenAI, “ChatGPT” — Verified March 2026. Product page documenting GPT-5.4 capabilities, thinking mode, SearchGPT, GPT Image, and Operator.
- Anthropic, “Introducing Claude Sonnet 4.6” — Feb 17, 2026. Sonnet 4.6 capabilities, 1M context beta, pricing at $3/$15 per MTok.
- Anthropic, “Plans & Pricing” — Verified March 2026. Opus 4.6 at $5/$25 per MTok.
- DeepSeek, “Models & Pricing” — Verified March 2026. DeepSeek-V3.2 at $0.28/$0.42 per MTok, 128K context.
- Google, “Introducing Gemini 3.1 Pro” — Feb 19, 2026. Gemini 3.1 Pro multimodal capabilities.
- OpenAI, “Pricing” — Verified March 2026. ChatGPT Plus at $20/month, Team at $25-30/seat/month.
- Ars Technica, Ryan Whitwam, “ChatGPT users hate GPT-5’s ‘overworked secretary’ energy, miss their GPT-4o buddy” — Aug 8, 2025. User reception context for GPT-5’s initial release.