Algorithmic Reasoning And Complexity Assessment

via “algorithmic reasoning task evaluation”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Isolates algorithmic reasoning as a distinct capability by presenting algorithm problems in natural language with few-shot examples, testing whether models can learn algorithmic patterns without explicit training. This approach measures algorithmic reasoning generalization rather than memorization.

vs others: More focused on algorithmic reasoning than general reasoning benchmarks; more accessible than formal algorithm verification tasks because it uses natural language rather than pseudocode or formal logic.

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

Google's open-weight model family from 1B to 27B parameters.

Unique: 27B variant achieves reasoning performance competitive with much larger models (70B+) through optimized training on reasoning-heavy datasets and learned chain-of-thought patterns, without requiring external reasoning engines or symbolic solvers

vs others: Outperforms Llama 2 70B on math and coding reasoning benchmarks while being 2.6x smaller, and matches Mistral 7B on reasoning tasks while offering superior code generation quality

10K coding problems across 3 difficulty levels with test suites.

Unique: Explicitly sources problems from competitive programming platforms (AtCoder, Codeforces, Kattis) where algorithmic rigor and time/memory limits enforce genuine complexity requirements, rather than using toy problems that can be solved with naive approaches

vs others: Tests genuine algorithmic reasoning rather than API knowledge; problems cannot be solved by simple pattern matching or memorization, requiring models to understand data structures, complexity analysis, and algorithm selection

via “adaptive-thinking-complexity-aware-reasoning”

Anthropic's most intelligent model, best-in-class for coding and agentic tasks.

Unique: Implements learned complexity routing that estimates problem difficulty from input tokens alone, without requiring explicit user hints or metadata. This is distinct from static reasoning budgets (o1, o1-mini) by dynamically allocating compute per-request based on inferred task characteristics, reducing wasted reasoning on trivial queries.

vs others: More efficient than fixed-reasoning-budget competitors by automatically scaling reasoning effort to task complexity, and more transparent than black-box reasoning models by still exposing thinking tokens when needed for debugging.

via “mathematical reasoning and logic problem evaluation with specialized scoring”

ReLE评测：中文AI大模型能力评测（持续更新）：目前已囊括374个大模型，覆盖chatgpt、gpt-5.4、谷歌gemini-3.1-pro、Claude-4.6、文心ERNIE-X1.1、ERNIE-5.0、qwen3.6-max、qwen3.6-plus、百川、讯飞星火、商汤senseChat等商用模型， 以及step3.5-flash、kimi-k2.6、ernie4.5、MiniMax-M2.7、deepseek-v4、Qwen3.6、llama4、智谱GLM-5.1、MiMo-V2、LongCat、gemma4、mistral等开源大模型。不仅提供排行榜，也提供规模超200万的大

Unique: Evaluates mathematical reasoning with 1-5 quality scale for reasoning steps rather than binary correctness, enabling partial credit for correct methodology with computational errors. Combines final answer accuracy with reasoning quality assessment to capture mathematical thinking capability. Includes multi-step reasoning problems and logical inference tasks beyond simple arithmetic.

vs others: More nuanced mathematical assessment than MMLU (binary correctness) and captures reasoning quality vs answer-only evaluation

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 “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 “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 “code-generation-and-debugging-with-reasoning”

ERNIE-4.5-21B-A3B-Thinking is Baidu's upgraded lightweight MoE model, refined to boost reasoning depth and quality for top-tier performance in logical puzzles, math, science, coding, text generation, and expert-level academic benchmarks.

Unique: Integrates reasoning-based algorithm verification with code generation through A3B branching, allowing the model to explore multiple implementation approaches and select the most algorithmically sound one before generating final code. This differs from pattern-matching-only code generators by explicitly reasoning about correctness.

vs others: Produces more algorithmically correct code than GitHub Copilot for complex algorithmic problems while explaining reasoning; however, less specialized than domain-specific code models and requires more context for optimal results

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 “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 “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 “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 “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.

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

The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently...

Unique: Applies extended reasoning to mathematical problem-solving, enabling explicit step-by-step verification and error-checking within the reasoning phase. Unlike standard LLMs that may skip steps or make calculation errors, o3-pro's reasoning allows it to catch and correct mistakes before output.

vs others: Achieves 90%+ accuracy on AIME and MATH benchmarks compared to 50-70% for GPT-4, due to reasoning-enabled verification and multi-path exploration.

via “extended-reasoning-chain-of-thought-generation”

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

Unique: Uses large-scale reinforcement learning (not just supervised fine-tuning) to train the model to dynamically allocate internal computation time based on problem difficulty, with an opaque but learned reasoning process that explores multiple solution paths before responding. This differs from standard models that apply fixed computation per token.

vs others: Outperforms GPT-4 and Claude on math, coding, and formal reasoning benchmarks by 10-30% due to learned reasoning allocation, but trades latency and cost for accuracy on hard problems.

via “adaptive deep thinking with chain-of-thought reasoning”

Seed 1.6 is a general-purpose model released by the ByteDance Seed team. It incorporates multimodal capabilities and adaptive deep thinking with a 256K context window.

Unique: Implements adaptive reasoning allocation that dynamically scales internal computation based on query complexity, rather than applying uniform reasoning depth to all inputs — this reduces latency for simple queries while preserving accuracy for hard problems

vs others: More efficient than OpenAI o1 (which applies heavy reasoning to all queries) because it adapts reasoning depth, and more transparent than standard LLMs by exposing reasoning mechanisms for complex problems

via “complex reasoning and problem-solving”