How o3, Gemini, and DeepSeek Shaped Modern Reasoning Models

A standard language model commits to each word as it generates, with no built-in way to pause and check its logic or revise a step it got wrong. That design works well for drafting text and recalling facts, and it breaks down on problems where the answer depends on a chain of dependent steps. One flawed step early in a proof or a calculation carries through to a wrong result, and the model has no mechanism to notice.

This is why a system that scored well on knowledge benchmarks could still miss questions from a grade-school math competition, where the difficulty lives in the sequence of steps that the facts must pass through. The limitation was structural, tied to how the model produced an answer in a single pass. Closing that gap required a different way of generating a response.

Reasoning models addressed the problem by letting a system think before it answers, working through intermediate steps the way a person sketches a solution before committing to it. Three models pushed this shift further than any others in a single year.

This article explains what sets a reasoning model apart, how o3, Gemini, and DeepSeek each advanced the approach, and what the durable lesson is for you as an AI professional.

What Makes a Reasoning Model Different

A reasoning model, sometimes called a large reasoning model (LRM), generates a sequence of intermediate steps before it produces a final answer. It spends additional computation at the moment of answering to work through a problem, where a standard model returns its best single-pass response immediately.

Two ideas make this work together:

• Chain-of-thought. The model produces a series of reasoning steps that break a hard problem into smaller ones, so a difficult question becomes a sequence of manageable moves toward the answer.
• Test-time compute. The system invests more processing at inference to explore and check those steps, spending real effort at the moment of answering on top of what it learned in training.

A model equipped with both can catch its own mistakes and try alternative paths, arriving at answers that a single pass would miss.

The training method matters as much as the mechanism. These models learn to reason through reinforcement learning on verifiable tasks, where the model is rewarded for reaching a correct answer on problems whose correctness can be checked, such as a math result or code that passes its tests. Because the reward depends only on the final answer being correct, the model is free to discover its own path to it. That freedom is what produces the emergent behaviors these systems are known for, including backtracking when a line of reasoning fails and checking an answer against the constraints of the question before it settles on a result.

You can see how this fits the broader skill set in this overview of AI engineering skills that matter beyond coding.

o3 and the Power of Test-Time Compute

OpenAI’s o3, released in April 2025, demonstrated how far test-time compute could push performance. The model was trained with reinforcement learning to use what OpenAI calls a private chain of thought, planning and reasoning through a problem before answering. Its results made the case that spending compute at inference, on top of the compute used during training, enables capability that scale alone had not reached.

The benchmark that captured attention was ARC-AGI, a test of abstract reasoning designed to resist memorization. o3 scored 87.5 percent on it, against roughly 5 percent for the earlier GPT-4o, a jump that signaled test-time scaling was a real path forward. On SWE-Bench Verified, a set of genuine software engineering tasks, OpenAI reported that o3 reached 71.7 percent accuracy, more than twenty points above its predecessor.

o3 advanced the field in a second way that proved just as important as the scores. It was the first model in its series that could use tools agentically inside its own reasoning, calling on web search and code execution, along with image analysis, mid-thought. This gave a reasoning model the ability to gather what it needed while it worked, extending it past the text it started with. A model that can run a calculation or look up a fact partway through a proof is far harder to challenge than one confined to what it already knows, and o3 made that combination practical at the frontier.

Gemini and Parallel Thinking

Google’s Gemini 2.5 Deep Think, previewed at Google I/O in May 2025, advanced reasoning along a different axis. Where a standard chain of thought follows one line of reasoning, Deep Think uses what Google calls parallel thinking, generating several independent lines of reasoning at once and then comparing and combining them before it answers.

The payoff showed up on the hardest problems. A research version of the model reached a gold-medal standard at the 2025 International Mathematical Olympiad, and Google reported that the publicly released version still reached bronze-level performance on the same benchmark while running fast enough for daily use. The model was built with reinforcement learning techniques that reward the use of these extended, branching reasoning paths.

Parallel thinking contributed a distinct idea to the field. It showed that reasoning quality on difficult problems can rise when a model explores multiple hypotheses in parallel, a structure closer to how a person weighs competing approaches before settling on one. Combined with a context window of up to one million tokens, this made the model effective on long, involved problems that a single reasoning chain would struggle to hold together.

DeepSeek and Open Reasoning

DeepSeek-R1, released in January 2025 by the Hangzhou-based firm DeepSeek, advanced the field less through a benchmark record than through access. It was the first widely available open-weight model to show that strong reasoning can emerge from reinforcement learning on verifiable tasks, without the supervised fine-tuning that was assumed to be necessary. DeepSeek published the method in its technical report and released the weights under a permissive MIT license.

The cost figures reshaped expectations across the industry. DeepSeek disclosed that the reinforcement learning stage behind R1 cost roughly 294,000 US dollars, a small fraction of what the leading proprietary efforts were understood to spend. The release was widely described as a turning point, since it showed that advanced reasoning was reproducible outside the best-funded laboratories.

Here, openness was the key contribution. By putting a capable reasoning model and its method in public hands, DeepSeek let researchers and companies study the technique and build on it directly. Teams that could never train such a model from scratch could now fine-tune the open weights for their own domains or run the model on their own hardware. That accelerated the spread of reasoning methods far faster than a closed release could have, and it pressured every other lab to compete on both quality and cost.

How the Three Models Compare

Each model reached deliberation by a different route, and the contrast makes the shared foundation clearer. The table below sets them side by side on the points that distinguish them.

Model	Developer	Core Innovation	Access Model
o3	OpenAI	Test-time compute with agentic tool use inside reasoning	Proprietary, via API and ChatGPT
Gemini 2.5 Deep Think	Google DeepMind	Parallel thinking across multiple reasoning paths	Proprietary, via Gemini app and API
DeepSeek-R1	DeepSeek	Open reasoning from reinforcement learning at low cost	Open weights, MIT license

The common thread runs underneath the differences. All three spend compute at inference to deliberate, and all three were trained with reinforcement learning that rewards correct reasoning. Each treats the reasoning process itself as the product, giving it a central place that older models hid. They arrived at that shared method from three directions, which is what makes the convergence significant.

Where These Models Stand in 2026

The pace of this field is its own lesson. Each of these three models has already been successful. OpenAI announced that o3 would retire from ChatGPT in August 2026 as its GPT-5 generation took over, though it remains available through the API. DeepSeek released its V4 generation in April 2026, and the original R1 now lives on mainly as open weights for research and self-hosting. Gemini advanced into its 3 series, carrying Deep Think forward into more capable models.

The techniques, however, outlasted the specific models. These methods are now standard equipment in advanced systems, from test-time compute to parallel reasoning and reinforcement learning from verifiable rewards. o3, Gemini Deep Think, and DeepSeek-R1 matter less as products you would deploy today and more as the models that established how reasoning is built, a foundation their successors build on directly.

What This Means for AI Professionals

The rapid turnover carries a clear implication for your career. A specific model can dominate a benchmark one quarter and be retired the next, so betting your expertise on any single system is a fragile strategy. The reasoning paradigm these models introduced is what endures, and understanding it deeply is what keeps your skills current across generations.

That means knowing how chain-of-thought and test-time compute change what a model can do, and how reinforcement learning from verifiable rewards produces reasoning. It also means being able to evaluate whether a reasoning model’s output can be trusted. These concepts transfer from o3 to its successors and to whatever arrives next, which is why they reward study more than any single product does. You can see how this fits the wider set of responsibilities in this look at AI engineer and ML engineer roles.

This is also the logic behind grounding a credential in fundamentals. The Artificial Intelligence Engineer (AiE®) certification is built around the core of machine learning and system design, keeping its distance from any single toolchain, which is the knowledge that stays useful as individual models turn over. In a field that replaces its leading systems within months, a grasp of the underlying methods is the asset that holds its value.

Conclusion

o3, Gemini, and DeepSeek advanced reasoning models along three different lines. Test-time compute and parallel thinking each pushed the field forward, and open access widened who could contribute, so together they turned deliberation into a standard capability of leading-edge AI. Each has since been succeeded, yet the methods they introduced establish foundational methods for reasoning systems.

For an AI professional, the takeaway is to learn the paradigm that underlies these systems, since the leaderboard itself is never settled for long. The models will keep turning over, and the principles of how a machine reasons through a problem will carry your expertise from one generation to the next.