Specialized Capability Indexing For Coding And Reasoning Tasks

via “multi-domain reasoning task stratification”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Explicitly stratifies tasks by reasoning modality (algorithmic, arithmetic, logical, causal, spatial) rather than treating all hard tasks as monolithic, enabling domain-specific capability assessment. This structure allows researchers to correlate model architecture choices with specific reasoning strengths.

vs others: More analytically useful than generic hard task collections because stratification enables root-cause analysis of reasoning failures; more focused than full BIG-Bench which lacks explicit domain organization.

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

Compact 3B model balancing capability with edge deployment.

Unique: Instruction-tuned for chain-of-thought reasoning with 128K context enabling multi-step problem solving on edge devices — most 3B models lack explicit reasoning training or have limited context for complex reasoning chains

vs others: Enables local reasoning without cloud API calls (privacy, latency) while maintaining reasonable capability for simple-to-moderate problems; smaller than 7B+ reasoning models for faster edge inference

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 “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 “compact reasoning model for math, science, and coding”

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

Unique: Unlike larger models, QwQ 32B delivers competitive reasoning capabilities in a compact size, making it accessible for self-hosted applications.

vs others: QwQ 32B offers strong performance in reasoning tasks while requiring less computational power compared to larger models.

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 “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 “extended-chain-of-thought reasoning with compute allocation”

OpenAI's reasoning model with chain-of-thought problem solving.

Unique: Native integration of reasoning into the inference architecture with dynamic compute allocation based on problem difficulty, rather than fixed-budget or prompt-instructed reasoning. The model learns to allocate thinking tokens adaptively during training, enabling it to spend more compute on genuinely hard problems.

vs others: Outperforms GPT-4 and other models on reasoning-heavy benchmarks (83.3% on IMO, 89th percentile on Codeforces) because reasoning is baked into the model's weights and inference process, not bolted on via prompting or external tools.

via “capability boundary identification”

Subset of BIG-Bench where most models fail

Unique: The focus on identifying underperformance in reasoning tasks allows for a targeted approach to understanding model limitations, which is not common in other benchmarks.

vs others: Provides a clearer view of reasoning capabilities compared to broader benchmarks that do not focus on specific weaknesses.

via “reasoning-specialized model identification and separate ranking”

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: Identifies and separately ranks reasoning-specialized models (e.g., DeepSeek-R1, o1-mini) in dedicated leaderboard (reasonmodel.md) rather than mixing with general-purpose models. Recognizes that reasoning-specialized models have distinct performance profiles and enables category-specific comparison. Maintains separate ranking for models optimized for complex reasoning tasks.

vs others: Explicit reasoning-specialist categorization vs single global leaderboard (which obscures reasoning-specialization benefits) and dedicated reasoning evaluation vs general benchmarks

via “reasoning-model-support-with-extended-thinking”

Chat via OpenAI-Compatible API

Unique: Transparently supports reasoning models (o1, o3-mini, DeepSeek R1) with extended thinking capabilities, routing complex problems to models optimized for deep reasoning; handles different token accounting and response time characteristics

vs others: Enables access to state-of-the-art reasoning capabilities without custom integration; more cost-effective than running reasoning models locally; better for complex problems than standard fast models

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 “deep-reasoning-for-complex-queries”

Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based...

Unique: Allocates extended reasoning resources specifically for complex queries, using iterative search and synthesis rather than single-pass retrieval. The system explicitly reasons about query complexity and adjusts reasoning depth accordingly.

vs others: Deeper reasoning than standard search APIs, and more adaptive than fixed-depth reasoning systems that apply the same analysis to all queries.

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 “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 “agentic-code-reasoning-with-visible-traces”

Grok Code Fast 1 is a speedy and economical reasoning model that excels at agentic coding. With reasoning traces visible in the response, developers can steer Grok Code for high-quality...

Unique: Exposes reasoning traces as part of the response stream rather than hiding them, enabling developers to inspect intermediate decision-making and steer the model via follow-up prompts based on visible reasoning quality

vs others: Provides interpretable reasoning for code tasks at lower cost than o1/o3 models while maintaining faster inference speeds than full-chain reasoning models

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 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 “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 “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