Reasoning Capability Evaluation

via “sustained multi-step reasoning”

Anthropic's 2026 flagship — strongest Claude for agents, long-horizon coding, and tool orchestration.

Unique: Combines advanced reasoning capabilities with a user-friendly interface, making complex logical tasks accessible.

vs others: More reliable than simpler models that lack depth in reasoning capabilities.

via “logical deduction task evaluation”

Zero-shot LLM evaluation for reasoning tasks.

Unique: Provides unified evaluation framework for both symbolic logic and natural language reasoning puzzles in zero-shot setting, with answer verification that can handle both formal symbolic validation and semantic similarity-based matching for natural language conclusions

vs others: More specialized than general reasoning benchmarks; focuses specifically on logical deduction without few-shot examples, enabling cleaner measurement of foundational logical capability vs. pattern-matching from examples

via “solution step extraction and intermediate reasoning evaluation”

12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.

Unique: Preserves solution steps as first-class data throughout the evaluation pipeline, enabling evaluation of intermediate reasoning quality rather than just final answers. This supports emerging research on chain-of-thought prompting and interpretable AI reasoning.

vs others: More comprehensive than final-answer-only evaluation because it assesses reasoning quality and interpretability, but requires more manual annotation and is harder to automate than simple answer verification.

via “logical deduction and inference evaluation”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Isolates formal logical reasoning as a distinct capability by presenting logic problems in natural language with few-shot examples, testing whether models can apply logical rules consistently without explicit training. This approach measures logical inference generalization.

vs others: More focused on formal logical reasoning than general reasoning benchmarks; more accessible than formal logic verification because it uses natural language rather than symbolic logic notation.

via “reasoning and chain-of-thought inference”

Ultra-fast LLM API on custom LPU hardware — 500+ tok/s, Llama/Mixtral, OpenAI-compatible.

Unique: Reasoning runs on LPU hardware, potentially offering faster intermediate step generation than GPU-based reasoning models. Integrated into the same OpenAI-compatible endpoint, allowing reasoning to be triggered without separate API calls or model switching.

vs others: Faster reasoning inference than OpenAI o1 or Claude due to LPU acceleration; simpler integration than building custom chain-of-thought frameworks because reasoning is native to the model.

via “reasoning and multi-step problem solving”

Microsoft's 3.8B model with 128K context for edge deployment.

Unique: Achieves 69% MMLU reasoning performance in a 3.8B model through synthetic training data specifically designed for reasoning patterns, significantly outperforming typical SLMs on reasoning benchmarks despite extreme parameter efficiency

vs others: Delivers reasoning capability in 3.8B parameters (vs. Mistral 7B, Llama 3.2 1B which don't emphasize reasoning) while remaining mobile-deployable, trading some accuracy for extreme efficiency and edge compatibility

via “cross-model reasoning capability comparison”

7.8K science questions testing genuine reasoning, not just recall.

Unique: Provides a reasoning-specific evaluation surface (Challenge set curated to exclude shallow-method-solvable questions) that isolates reasoning capability from retrieval capability, enabling cleaner comparison of how different models approach reasoning tasks. Domain stratification further enables analysis of whether reasoning capability is uniform or domain-specific.

vs others: More suitable for reasoning-focused comparison than generic QA benchmarks because Challenge set explicitly filters out retrieval-solvable questions; more fine-grained than single-metric leaderboards because it supports domain and difficulty stratification

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 “benchmark-validated reasoning performance on standardized datasets”

Alibaba's 32B reasoning model with chain-of-thought.

Unique: Provides documented benchmark results on standardized reasoning datasets (AIME 79.5%, MATH-500 96.4%) enabling quantitative performance validation, with explicit comparison claims against larger models

vs others: Demonstrates competitive reasoning performance on standardized benchmarks comparable to much larger models, providing quantitative evidence of reasoning capability for evaluation and comparison purposes

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

via “reasoning-chain-evaluation-via-glider-model”

Enterprise LLM evaluation for hallucination and safety.

Unique: GLIDER is a specialized model trained to evaluate reasoning chain quality, providing step-by-step reasoning assessment rather than just overall output quality. Integrated into Patronus's evaluation platform for correlation with other metrics (hallucination, toxicity).

vs others: Provides specialized reasoning evaluation via GLIDER model, whereas general LLM evaluation requires custom prompting of GPT-4 or other models to assess reasoning quality, with less consistency and higher latency.

via “logical reasoning and argument analysis”

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

Unique: Qwen2.5-7B-Instruct includes instruction-tuning on formal logic datasets and argument analysis tasks, enabling the model to identify common logical fallacies (ad hominem, straw man, begging the question) and evaluate argument validity. The model learns to explain reasoning transparently, showing why an argument is valid or invalid.

vs others: More accessible than specialized logic systems while maintaining reasonable accuracy for common logical tasks; better at explaining reasoning than base models due to instruction-tuning

via “dynamic reasoning assessment”

Multi-turn chat conversations for dialogue quality evaluation

Unique: Focuses on dynamic reasoning through a carefully curated set of conversations that require logical deduction and follow-up interactions.

vs others: More comprehensive in assessing reasoning than static benchmarks that do not account for conversational context.

via “evaluation metric formulation”

Abstraction and reasoning corpus for general intelligence

Unique: The evaluation metrics are specifically tailored to assess abstract reasoning capabilities, unlike generic metrics that may not reflect reasoning depth.

vs others: Offers more nuanced evaluation than traditional benchmarks like accuracy, which may not fully capture reasoning abilities.

via “commonsense reasoning evaluation”

Commonsense NLI with adversarial context mining

Unique: Utilizes adversarially filtered questions to create plausible distractors, ensuring a more robust evaluation of reasoning capabilities compared to traditional benchmarks.

vs others: More challenging than standard commonsense benchmarks due to its focus on plausible distractors, making it a better test for true understanding.

Subset of BIG-Bench where most models fail

Unique: The curation of tasks specifically targeting reasoning limits rather than general performance allows for a more focused evaluation of model capabilities.

vs others: More targeted than generic benchmarks, as it specifically identifies and tests reasoning weaknesses in models.

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 “specialized capability indexing for coding and reasoning tasks”

Artificial Analysis provides objective benchmarks & information to help choose AI models and hosting providers.

Unique: Separates model evaluation by task domain (coding, reasoning, agentic) rather than treating all models as general-purpose, recognizing that a model's strength in one domain doesn't guarantee strength in another. The reasoning capability indicator provides a quick filter for models suitable for complex reasoning tasks.

vs others: More targeted than general leaderboards because it isolates performance on specific task types; more practical for specialists than one-size-fits-all rankings; more discoverable than searching individual benchmark papers because indices are pre-computed and filterable.

via “reasoning and chain-of-thought 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: Reasoning capability emerges from instruction-tuning on datasets containing reasoning examples, not explicit reasoning modules or symbolic reasoning engines. The model learns to generate plausible reasoning chains through imitation, making it flexible but not formally verifiable.

vs others: Provides comparable chain-of-thought quality to GPT-4 on most reasoning tasks while using 3x fewer active parameters, though may require more explicit prompting to trigger reasoning compared to larger models.

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