Reasoning And Multi Step Problem Solving

via “reasoning and complex task decomposition”

Mistral's 12B model with 128K context window.

Unique: Trained explicitly for reasoning tasks with extended 128K context enabling multi-step reasoning chains and complex problem decomposition, though specific reasoning techniques not disclosed

vs others: Larger context window (128K vs 32K in Mistral 7B) enables longer reasoning chains without truncation, improving reasoning quality for complex multi-step problems

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 “multi-step mathematical reasoning benchmark evaluation”

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Unique: Uses linguistically diverse, human-authored grade school problems (not synthetic) that require genuine multi-step reasoning with basic arithmetic, combined with a standardized answer extraction format (#### delimiter) that enables reproducible evaluation across heterogeneous model outputs

vs others: More challenging than simple arithmetic benchmarks (requires 2-8 reasoning steps) yet more accessible than advanced math benchmarks, making it ideal for measuring practical reasoning improvements in production models

via “iterative multi-step reasoning”

Break down complex problems into adjustable, multi-step reasoning. Plan, revise, and branch your approach while preserving context and filtering irrelevant details. Iterate toward a confident, verified solution when the scope is uncertain or evolving.

Unique: Utilizes a context-preserving architecture that allows for dynamic branching and filtering of irrelevant information, which is not commonly found in traditional reasoning tools.

vs others: More flexible than static reasoning frameworks, as it allows for real-time adjustments based on evolving problem contexts.

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 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 “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 “reasoning and multi-step problem solving”

The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...

Unique: Sparse MoE routing activates reasoning-specialized experts when processing complex queries, enabling efficient multi-step reasoning without full model computation. Linear attention mechanisms allow maintaining long reasoning chains without quadratic memory overhead.

vs others: Provides more efficient reasoning than dense models through expert specialization, while maintaining reasoning quality comparable to specialized reasoning models like o1 through planning-aware expert activation.

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 “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 “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 “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 “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 and chain-of-thought problem solving”

Meta's Llama 3.1 — high-quality text generation and reasoning

Unique: Explicitly trained for chain-of-thought reasoning across all three variants, with the 405B model claiming state-of-the-art performance. Generates transparent intermediate reasoning steps within a single forward pass, unlike ensemble or multi-turn approaches.

vs others: Provides transparent reasoning comparable to Claude 3.5 Sonnet and GPT-4o, but runs locally without API calls. Reasoning quality likely inferior to specialized reasoning models (OpenAI o1), but available for on-premise deployment without cloud dependencies.

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 “reasoning and chain-of-thought decomposition”

GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...

Unique: Learns chain-of-thought patterns from training data rather than using explicit prompting tricks, enabling more natural and flexible reasoning decomposition that adapts to problem complexity without manual prompt engineering

vs others: More reliable reasoning than GPT-3.5 Turbo and comparable to GPT-4o on hard problems, while maintaining lower latency through architectural efficiency rather than brute-force scaling

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-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 “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.