Multimodal Reasoning With Extended Thinking For Stem And Mathematical Problem Solving

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

DeepSeek's 236B MoE model specialized for code.

Unique: Trained on 6 trillion tokens including mathematical reasoning datasets and code-based solutions, enabling both symbolic reasoning and code generation for mathematical problems in a single model without separate math-specific components

vs others: Provides integrated mathematical reasoning and code generation (unlike Copilot which focuses on code) while maintaining open-source weights and supporting local deployment

via “deep thinking mode for complex mathematical and logical reasoning”

Shanghai AI Lab's multilingual foundation model.

Unique: Implements hidden reasoning tokens that don't consume user-visible token budget, allowing extended thinking without inflating output length; trained with only 4 trillion tokens (vs 8T+ for competing models) through efficient reasoning-focused pretraining

vs others: More efficient reasoning than o1-preview (requires fewer total tokens) while maintaining comparable accuracy on math benchmarks; faster than Llama 3.1 with extended thinking due to optimized attention patterns

via “mathematical reasoning with math benchmark 80+ and structured problem-solving”

Alibaba's 72B open model trained on 18T tokens.

Unique: Integrates three distinct reasoning paradigms (CoT for symbolic reasoning, PoT for code-based computation, TIR for external tool orchestration) within single 72B dense model, enabling flexible problem-solving strategies without model switching. 128K context window allows full problem histories and solution verification within single inference call.

vs others: Outperforms Llama 2 70B (significantly lower math performance) and matches Llama 3 70B on general benchmarks while offering specialized math reasoning patterns; Qwen2.5-Math 72B variant provides deeper specialization but general-purpose 72B enables seamless math-to-code-to-text transitions without model switching.

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

text-generation model by undefined. 1,37,84,608 downloads.

Unique: Qwen2.5-7B-Instruct includes explicit training on mathematical reasoning datasets (including GSM8K, MATH, and proprietary datasets) with emphasis on showing intermediate steps and justifying answers. The instruction-tuning includes prompts that encourage the model to 'think step by step' and 'show your work', which are known to improve mathematical reasoning through in-context learning effects.

vs others: Outperforms base Qwen2.5-7B on mathematical reasoning benchmarks by 15-20% due to instruction-tuning; more accessible than specialized math models (like Minerva) for general-purpose deployment

via “multimodal reasoning with cross-modal attention”

Google's fast multimodal model with 1M context.

Unique: Uses cross-modal attention to reason across text, image, video, and audio simultaneously in a single forward pass, rather than processing modalities separately and combining results post-hoc

vs others: More coherent reasoning than sequential modality processing because attention mechanisms can identify relationships between modalities; enables more complex reasoning tasks than single-modality models

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 “mathematical-problem-solving-with-symbolic-reasoning”

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Unique: Leverages extended internal reasoning to explore multiple mathematical approaches and verify symbolic manipulations before responding, providing higher confidence in mathematical correctness than models without reasoning capabilities.

vs others: Exceeds GPT-4 and Claude on complex mathematics by using internal reasoning to validate symbolic steps, reducing hallucinated solutions and improving explanation quality for educational use cases.

via “extended thinking reasoning with step-by-step problem decomposition”

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Unique: Implements native extended thinking as a first-class capability integrated into the model architecture, allowing transparent reasoning-before-response without requiring prompt engineering or external chain-of-thought frameworks. The thinking process is computationally budgeted and automatically triggered based on query complexity.

vs others: Provides reasoning capabilities comparable to o1 but with broader multimodal support (image/audio inputs) and lower per-token cost than specialized reasoning models, though with less user control over reasoning depth.

via “extended-reasoning-with-thinking-tokens”

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Unique: Uses hidden thinking tokens that consume inference budget but remain invisible to users, enabling internal verification and multi-path exploration without exposing intermediate steps — distinct from chain-of-thought which exposes all reasoning to the user

vs others: Provides higher accuracy on complex reasoning tasks than standard LLMs while maintaining clean output formatting, though at higher latency and token cost than models without extended thinking capabilities

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 “mathematical reasoning and symbolic computation”

Qwen3-Max-Thinking is the flagship reasoning model in the Qwen3 series, designed for high-stakes cognitive tasks that require deep, multi-step reasoning. By significantly scaling model capacity and reinforcement learning compute, it...

Unique: Combines extended reasoning with mathematical domain knowledge to enable transparent, step-by-step mathematical problem-solving. Uses thinking tokens to represent intermediate mathematical steps and verification, making mathematical reasoning auditable and debuggable.

vs others: Provides better mathematical reasoning transparency than general-purpose LLMs while maintaining broader applicability than specialized mathematical AI systems, though with lower precision than dedicated computer algebra systems.

via “mathematical reasoning and symbolic computation”

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 on mathematical datasets with chain-of-thought reasoning to prioritize step-by-step problem solving, using attention mechanisms that track variable relationships and equation transformations

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

via “multimodal chain-of-thought reasoning”

* ⭐ 03/2023: [PaLM-E: An Embodied Multimodal Language Model (PaLM-E)](https://arxiv.org/abs/2303.03378)

Unique: Interleaves visual references with textual reasoning steps in a unified sequence, rather than generating reasoning text separately from visual analysis, enabling tighter visual-linguistic reasoning coupling

vs others: More interpretable than end-to-end visual reasoning because it exposes intermediate steps; more grounded than text-only chain-of-thought because it references visual content explicitly

via “long-context reasoning with extended thinking”

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

Unique: Implements internal chain-of-thought reasoning within a 200K token window using transformer attention mechanisms, allowing reasoning to occur before output generation without requiring explicit prompt engineering for step-by-step thinking

vs others: Outperforms GPT-4o and Claude 3.5 Sonnet on complex reasoning tasks by maintaining coherence across longer reasoning chains while keeping the 200K context window practical for real-world applications

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 “multimodal reasoning with extended thinking for stem and mathematical problem-solving”

Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....

Unique: Unifies visual and textual reasoning through a single 235B parameter model with explicit thinking tokens, rather than treating vision and language as separate processing streams. The architecture uses a shared transformer backbone with vision-language fusion at intermediate layers, allowing mathematical reasoning to operate directly over visual features (e.g., reasoning about graph structure while reading axis labels).

vs others: Outperforms GPT-4V and Claude 3.5 Sonnet on STEM benchmarks (MATH-Vision, SciQA) because thinking tokens enable explicit symbolic reasoning over visual content, whereas competitors rely on implicit visual understanding without intermediate reasoning artifacts.

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 “mathematical reasoning and 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: Uses thinking tokens to work through mathematical reasoning before responding, similar to specialized math reasoning models but integrated into a general-purpose model with 1M context. This enables the model to solve problems while maintaining awareness of previous mathematical context or related problems in the conversation.

vs others: Provides reasoning-enhanced math solving comparable to specialized models like Wolfram Alpha but with natural language understanding and 1M context, enabling integration with broader problem-solving workflows

via “mathematical reasoning and symbolic computation”

Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...

Unique: Combines sparse MoE routing with instruction fine-tuning specifically optimized for mathematical reasoning, allowing different experts to specialize in algebra, calculus, statistics, and logic domains while maintaining unified instruction-following interface.

vs others: Outperforms GPT-3.5 on mathematical reasoning benchmarks while being significantly cheaper, though slightly behind GPT-4 on advanced symbolic manipulation tasks.

via “mathematical reasoning and symbolic computation”

Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...

Unique: Qwen3-235B-A22B integrates thinking mode specifically optimized for mathematical reasoning, allowing the model to allocate compute budget to step-by-step derivations before committing to final answers, improving accuracy on complex problems

vs others: Stronger mathematical reasoning than smaller models (7B-13B) due to scale, while thinking mode provides accuracy improvements comparable to or exceeding prompting techniques like 'chain-of-thought' in dense models