Reasoning Frontiers › Reasoning-Capable Models: The Current Models

The period from late 2024 through early 2025 saw a rapid expansion in the number and variety of reasoning-capable models available to practitioners. Understanding the field requires attention to benchmark numbers, training choices, and architectural differences together.

OpenAI's o1 (September 2024) was the first publicly available model that demonstrated the test-time compute scaling paradigm at production quality. It arrived with a strong empirical record and essentially no documentation of how it was trained. Key characteristics of the o-series:

The hidden chain of thought is the defining feature. The internal reasoning ("thinking") process is not shown to users. This protects the novel reasoning strategies learned during RL training and prevents users from easily mimicking or gaming the reasoning process. From a user experience perspective, you submit a query and receive an answer after a variable delay, with no insight into what happened in between.

Extended context is necessary to support long reasoning traces. Models are trained to handle reasoning contexts of 8,000-32,000 tokens of internal thought before producing the final response. The final response itself may be relatively short, but the total token budget for a single query can be substantial.

Strong performance on formal reasoning was the initial demonstration of value. o1 substantially outperformed previous state-of-the-art on AIME 2024 (from roughly 12% to over 74%), on Codeforces competitive programming, and on PhD-level science questions (GPQA diamond). These gains were not marginal. They were large enough to suggest that a qualitatively different capability had emerged, not just incremental improvement.

Higher latency is the main practical limitation. The deliberation before answering introduces noticeable latency compared to direct-answer models: seconds to tens of seconds for complex problems rather than sub-second responses. For applications where response time matters (interactive assistants, real-time tools), this latency is a practical constraint.

The o3 model (January 2025) extended these capabilities significantly, achieving near-perfect scores on AIME 2025 and impressive results on ARC-AGI, a benchmark specifically designed to test novel reasoning not covered by training data. o3 represented a meaningful step up from o1, suggesting that the RL-based reasoning paradigm has not yet saturated.

DeepSeek-R1 (January 2025) is significant for two reasons: it matches or exceeds o1 performance on several benchmarks, and it is open-source, allowing researchers to study the training process directly. The publication of the DeepSeek-R1 technical report was a watershed moment for the field, revealing that world-class reasoning capabilities could be achieved with a transparent, reproducible training process.

The DeepSeek-R1 training process involves a carefully structured pipeline. Starting from DeepSeek-V3-Base, a capable pretrained model without RL, the team applied group relative policy optimization (GRPO) with outcome rewards only (correct/incorrect) and no process rewards initially. They then observed emergent reasoning behaviors, including self-verification and backtracking, appearing without explicit supervision. This behavior emerged without explicit instruction: nobody told the model to double-check its work. The behavior emerged because double-checking its work led to better outcomes in the training distribution, and the RL signal rewarded better outcomes. After the emergent RL phase, the team refined the model with small-scale supervised fine-tuning on high-quality reasoning traces, then applied further RL with a combination of accuracy rewards and format rewards.

DeepSeek-R1-Zero, the model trained with pure RL and no supervised warmup, is particularly interesting from a research perspective. It demonstrates that extended reasoning capabilities can emerge from RL alone, without a curated set of human-written reasoning traces to imitate. A model can discover how to reason, given only the incentive to be correct. This has implications for the scalability of the approach: if supervised warmup is not strictly necessary, the bottleneck for capability improvement shifts to reward signal quality and compute, both of which scale more straightforwardly than human-annotation-based supervision.

A particularly striking observation from the DeepSeek-R1-Zero training process was what the team called an "aha moment": a phase during pure RL training where the model spontaneously developed the behavior of re-examining and correcting earlier reasoning steps. Before this phase, the model would commit to its initial reasoning; after this phase, it would regularly revisit earlier conclusions. The fact that this behavior emerged from outcome-reward RL, with no explicit supervision or architectural change, suggests that the capacity for self-correction may be a general property of language models that can be elicited through RL rather than a capability that must be explicitly taught.

The broader significance of DeepSeek-R1 for the field is democratization. Before R1's release, strong reasoning models were available only from closed providers. After R1, any research group with sufficient compute could train a comparable model using a disclosed training procedure. This accelerated the pace of research on reasoning models considerably.

Google's Gemini 2.0 Flash Thinking and Gemini 2.5 Pro models incorporate similar extended reasoning capabilities. Unlike OpenAI's o-series, some Gemini Thinking variants expose the thinking traces to users, giving transparency into the model's deliberation process. You can read what the model "thought" before arriving at its answer, which aids in debugging and builds user trust, at the cost of longer responses and potential exploitation of the visible reasoning pattern.

The Gemini models demonstrate particularly strong multimodal reasoning, applying extended deliberation to problems that combine text, images, and structured data. This extends the test-time compute scaling paradigm beyond pure text to richer input modalities. A model reasoning about a diagram, a table, and a text description simultaneously can benefit from extended deliberation in the same way a text-only model benefits from it, suggesting that the core mechanism is not specific to text.

Alibaba's QwQ-32B (March 2025) is a 32-billion parameter open model that demonstrates strong reasoning performance, particularly notable given its parameter count relative to larger closed models. QwQ-32B uses extended chain-of-thought by default and achieves competitive results on AIME and MATH benchmarks. It is a demonstration that the parameter count required for strong reasoning performance is lower than the parameter counts of the closed frontier models, provided the training procedure is well-designed.

Smaller open reasoning models have also emerged, including various distilled versions of DeepSeek-R1. These distilled models, trained to imitate the reasoning traces of R1 using supervised fine-tuning, demonstrate that reasoning capabilities can be transferred to smaller models without full RL training. The distilled 7B and 14B models achieve surprisingly competitive performance on formal benchmarks, showing that the knowledge of how to reason, encoded in high-quality reasoning traces, is transferable through imitation learning. This has practical implications: organizations that cannot afford to run full RL training can still obtain meaningful reasoning improvements by fine-tuning on high-quality reasoning traces from a larger model.

The distillation result also raises a theoretical question. If a smaller model can learn to reason well by imitating a larger model's traces, does the smaller model understand what it is doing? Or is it learning a statistical pattern that happens to produce correct-looking reasoning without the underlying capability? This question connects to the broader debate about whether current models reason or merely simulate reasoning, which we address in the open questions section.