Linguistically Diverse Problem Corpus With Controlled Reasoning Complexity

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

Unique: Human-authored problems with explicit step-count constraints (2-8 steps) and linguistic diversity ensure that models cannot solve problems through surface-level pattern matching or memorization, forcing evaluation of genuine multi-step reasoning capability

vs others: More challenging than synthetic or template-based benchmarks (human authorship prevents exploitable patterns) and more stable than crowdsourced datasets (controlled authorship ensures consistency), but smaller than web-scraped math problem collections

via “reasoning and multi-step problem decomposition”

TII's 180B model trained on curated RefinedWeb data.

Unique: Achieves strong reasoning performance through scale (180B parameters) and data quality (3.5T meticulously-cleaned RefinedWeb tokens) rather than specialized reasoning fine-tuning, enabling emergent reasoning capabilities across diverse domains without task-specific training.

vs others: Larger parameter count than reasoning-specialized models like Llama 2 70B enables better few-shot reasoning, but lacks explicit chain-of-thought fine-tuning that models like GPT-4 or Claude employ, potentially requiring more sophisticated prompting to achieve comparable reasoning quality.

via “multi-domain instruction-following with chain-of-thought reasoning”

Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.

Unique: Trained on diverse instruction-following datasets with explicit reasoning supervision, enabling transparent multi-step problem decomposition across code, math, and analysis domains without requiring external reasoning frameworks or prompt templates

vs others: Provides reasoning transparency comparable to o1-preview at lower cost and latency, while maintaining broader domain coverage than specialized models; outperforms Llama 3.1 on instruction-following consistency due to targeted training on reasoning-heavy tasks

via “multilingual reasoning across 100+ languages with unified tokenization”

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

Unique: Uses a single unified tokenizer and shared MoE expert pool for 100+ languages rather than language-specific experts or separate tokenizers, enabling true cross-lingual reasoning where experts learn language-agnostic reasoning patterns. This contrasts with models that have language-specific expert subgroups.

vs others: Supports more languages than GPT-4 with unified reasoning (no language-specific degradation) and faster inference than separate language-specific models through shared expert routing

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

|[GitHub](https://github.com/meta-llama/llama3) | Free |

Unique: Instruction-tuned specifically on reasoning-focused datasets with explicit step-by-step annotations, enabling the model to naturally generate transparent reasoning traces without requiring special prompting techniques. The 70B parameter scale allows for nuanced reasoning across diverse domains while maintaining interpretability of intermediate steps.

vs others: More transparent and auditable reasoning than models optimized purely for answer accuracy, with reasoning traces that can be validated and debugged by domain experts, though less specialized than dedicated symbolic reasoning systems or theorem provers.

via “multi-domain complex problem solving with mathematical and logical reasoning”

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) 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...

Unique: Trained via reinforcement learning to dynamically allocate reasoning effort based on problem complexity, using sparse activation (37B active of 671B total) to route computation efficiently. This contrasts with fixed-depth reasoning in standard LLMs and enables o1-level performance on diverse problem types without proportional computational overhead.

vs others: Matches o1's reasoning quality on complex problems while being open-source and exposing reasoning tokens, versus GPT-4 which lacks systematic reasoning depth and o1 which hides the reasoning process entirely.

via “complex reasoning and multi-step problem decomposition”

WizardLM 2 — advanced instruction-following and reasoning

Unique: Instruction-tuned specifically for complex reasoning tasks via supervised fine-tuning on reasoning-heavy datasets, enabling implicit chain-of-thought without explicit prompt engineering; 8x22B MoE variant routes complex reasoning through specialized expert pathways for improved reasoning quality

vs others: Provides reasoning capabilities comparable to GPT-3.5-turbo or Claude-2 while remaining fully open-source and locally deployable, avoiding cloud API costs and latency for reasoning-intensive workloads

via “complex-query-answering-with-reasoning”

Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks. Launch video: https://youtu.be/Gc82AXLa0Rg?si=4RLn6WBz33qT--B7

Unique: Applies extended reasoning to open-ended question answering, enabling the model to decompose complex questions, explore multiple reasoning paths, and synthesize coherent answers that account for nuance and trade-offs. This goes beyond retrieval-based QA by enabling inference and reasoning.

vs others: Outperforms standard LLMs on complex, multi-faceted questions because reasoning tokens allow exploration of implications and trade-offs; more thorough than simple retrieval systems because it can reason beyond stored facts.