Extended Reasoning Stem Problem Solving

via “reasoning and step-by-step problem decomposition”

text-generation model by undefined. 95,66,721 downloads.

Unique: Emergent chain-of-thought capability from instruction tuning on reasoning datasets; no explicit reasoning module or symbolic engine — reasoning emerges from learned token prediction patterns that favor intermediate explanation tokens, making it lightweight but probabilistic

vs others: Provides transparent reasoning comparable to GPT-4 on simple problems but with full local control; outperforms Mistral-7B on reasoning tasks due to instruction tuning, but lacks the formal verification and symbolic reasoning of specialized tools like Wolfram Alpha

via “advanced reasoning model for complex problem solving”

OpenAI's reasoning model with chain-of-thought problem solving.

Unique: This model uniquely combines chain-of-thought reasoning with a large context window for enhanced problem-solving capabilities.

vs others: It offers superior performance in reasoning tasks compared to traditional models by leveraging extended thinking time and context.

via “reasoning and step-by-step problem decomposition”

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...

Unique: Instruction-tuned on datasets containing explicit reasoning traces (e.g., math solutions with working, logic puzzles with step-by-step explanations), enabling the model to learn to generate intermediate reasoning as a learned behavior rather than relying on prompt engineering alone.

vs others: More reliable than base models at producing coherent reasoning chains; comparable to GPT-4 on standard benchmarks but with lower latency and cost, though may underperform on novel reasoning patterns not well-represented in training data.

via “reasoning and step-by-step problem solving”

Olmo 3.1 32B Instruct is a large-scale, 32-billion-parameter instruction-tuned language model engineered for high-performance conversational AI, multi-turn dialogue, and practical instruction following. As part of the Olmo 3.1 family, this...

Unique: Instruction-tuning on chain-of-thought datasets enables the model to generate coherent reasoning steps when prompted, without requiring explicit reasoning modules or external symbolic solvers — this implicit reasoning approach is more flexible than hard-coded reasoning systems but less precise than specialized solvers

vs others: More transparent reasoning than direct answer generation, but lower accuracy on specialized domains than models fine-tuned exclusively on reasoning tasks; better for educational use cases than production problem-solving

via “reasoning and step-by-step problem decomposition”

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Unique: MoE expert specialization enables dedicated reasoning experts that activate for complex reasoning tasks, while general-purpose experts handle simpler steps, optimizing compute allocation across reasoning complexity

vs others: Provides faster reasoning than Llama 3.1 8B (15-20% speedup) while maintaining comparable accuracy on grade-school math and logic puzzles, though underperforms specialized reasoning models like o1-mini on competition-level problems

via “complex reasoning and chain-of-thought decomposition”

Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning...

Unique: Command R7B's reasoning is optimized for RAG and tool-use contexts, where intermediate steps can reference retrieved documents or tool outputs, enabling grounded reasoning that combines external knowledge with logical inference

vs others: Outperforms GPT-4 on MATH and AIME benchmarks when combined with tool use for calculation, because it can delegate computation to tools rather than attempting symbolic math in-context

via “extended reasoning with chain-of-thought for complex visual tasks”

Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels...

Unique: Integrates extended reasoning directly into the model's forward pass for visual tasks, rather than using post-hoc prompting techniques like 'think step-by-step', enabling the model to allocate compute dynamically to reasoning-heavy visual problems

vs others: More reliable than prompt-based chain-of-thought for visual reasoning because reasoning is baked into model weights, not dependent on prompt engineering; produces more consistent intermediate steps for STEM tasks

via “reasoning and chain-of-thought task decomposition”

Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....

Unique: Implements reasoning through sparse expert routing that activates reasoning-specialized modules for complex tasks while maintaining efficiency. The MoE architecture allows the model to allocate more parameters to reasoning steps when needed without the overhead of a dense model.

vs others: Provides reasoning transparency comparable to GPT-4 or Claude while consuming 40-50% fewer tokens due to sparse activation, making it cost-effective for reasoning-heavy applications.

via “reasoning and step-by-step problem solving”

GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.

Unique: Instruction-tuned for chain-of-thought reasoning, generating intermediate steps explicitly rather than jumping to conclusions; trained on diverse reasoning tasks to apply reasoning patterns across math, logic, and code domains

vs others: More accurate on multi-step problems than direct answer generation because explicit reasoning reduces errors; more flexible than specialized solvers because it handles diverse problem types, though less accurate than domain-specific tools (calculators, debuggers)

via “reasoning and multi-step problem solving”

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

Unique: Mistral Nemo's instruction-tuning includes reasoning tasks and chain-of-thought examples, enabling it to generate explicit reasoning steps when prompted. The 128k context window enables longer reasoning chains than smaller-context models.

vs others: Reasoning capability is weaker than larger models (70B+) but sufficient for many reasoning tasks. Prompt-based chain-of-thought is more transparent than implicit reasoning but less efficient than specialized reasoning architectures.

via “logical reasoning and problem decomposition”

Grok 3 is the latest model from xAI. It's their flagship model that excels at enterprise use cases like data extraction, coding, and text summarization. Possesses deep domain knowledge in...

Unique: Implements explicit reasoning traces with tree-of-thought exploration that shows alternative reasoning paths, enabling users to understand and validate reasoning logic rather than just receiving final answers

vs others: Provides more transparent reasoning than GPT-4's implicit chain-of-thought, while maintaining better reasoning quality than specialized reasoning models through broader knowledge base

via “logical reasoning and problem-solving with step-by-step decomposition”

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...

Unique: Instruction-tuning explicitly optimizes for chain-of-thought reasoning patterns, enabling the model to articulate intermediate steps and self-correct. 70B scale provides sufficient capacity for multi-step reasoning without losing coherence.

vs others: Better reasoning transparency than smaller models and comparable to GPT-4 on many reasoning tasks at lower cost, though specialized reasoning models or symbolic solvers may outperform on highly constrained domains like formal mathematics.

via “reasoning chain decomposition and step-by-step problem solving”

Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.

Unique: Implements chain-of-thought reasoning through prompt-based guidance rather than architectural modifications, enabling flexible reasoning depth control without model retraining

vs others: More cost-effective than specialized reasoning models (o1) for moderate complexity problems; produces transparent reasoning vs black-box outputs; trades off reasoning depth vs cost and latency

via “reasoning and chain-of-thought decomposition”

Mistral Large 2 2411 is an update of [Mistral Large 2](/mistralai/mistral-large) released together with [Pixtral Large 2411](/mistralai/pixtral-large-2411) It provides a significant upgrade on the previous [Mistral Large 24.07](/mistralai/mistral-large-2407), with notable...

Unique: Mistral Large 2411 implements implicit chain-of-thought through training on reasoning-heavy datasets, enabling natural step-by-step decomposition without explicit prompting while maintaining efficiency through optimized token generation

vs others: Provides reasoning quality comparable to GPT-4 while maintaining lower latency and cost through more efficient token usage

via “reasoning-focused problem decomposition and chain-of-thought”

This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....

Unique: Trained specifically on chain-of-thought datasets to prioritize reasoning steps, using attention mechanisms that weight intermediate reasoning tokens higher than direct answers, enabling more transparent problem-solving

vs others: Comparable to GPT-4's reasoning on complex problems, while maintaining lower latency and cost; outperforms Llama 2 on multi-step reasoning due to larger parameter count and specialized training

via “logical reasoning and step-by-step problem decomposition”

Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini). Gemma models are well-suited for a variety of...

Unique: Gemma 2 27B learns chain-of-thought reasoning patterns implicitly through training on problems with step-by-step solutions, enabling multi-step reasoning without explicit symbolic reasoning modules or formal logic engines

vs others: More efficient than GPT-4 for routine reasoning tasks; more reliable than smaller models (7B) on multi-step problems due to increased parameter capacity and training on reasoning-focused data

via “mathematical-problem-solving-with-step-by-step-reasoning”

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context...

Unique: Implements explicit reasoning phase specifically optimized for mathematical decomposition, allowing the model to verify intermediate steps before producing final answers, rather than generating answers directly.

vs others: More reliable for complex math than GPT-4 due to explicit verification phase, and more transparent than o1 (which hides reasoning) by allowing users to request step-by-step explanations.

via “multi-step problem solving with extended context windows”

DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....

Unique: Achieves o1-level reasoning performance on multi-step problems through a 671B parameter model with mixture-of-experts efficiency, exposing full reasoning traces for validation. Unlike o1, the reasoning process is transparent and the model weights are open-source, enabling custom fine-tuning for domain-specific problem types.

vs others: Comparable to o1 on reasoning benchmarks but with transparent reasoning tokens and lower API costs, versus GPT-4 which lacks explicit reasoning and requires more prompt engineering for complex multi-step problems.

via “extended thinking for complex reasoning and problem-solving”

Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...

Unique: Allocates additional compute to internal reasoning before response generation using a gated reasoning mechanism, enabling exploration of multiple solution paths and self-validation without exposing intermediate reasoning, improving accuracy on complex tasks by 15-30% vs standard mode

vs others: More effective than explicit chain-of-thought prompting (which uses tokens in the output) and more efficient than ensemble approaches, with internal reasoning optimization that doesn't inflate output token counts while still improving solution quality

via “reasoning and multi-step problem decomposition”

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...

Unique: Instruction-tuned on chain-of-thought examples enabling the model to naturally decompose reasoning without requiring explicit prompting frameworks or external planning systems, with MoE architecture potentially routing complex reasoning to specialized parameter subsets

vs others: More natural reasoning flow than base models due to instruction-tuning, though may underperform specialized reasoning models (o1, DeepSeek-R1) on very complex mathematical or logical problems requiring extensive search