Thinking Models And Extended Reasoning Support

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 “thinking-models-and-extended-reasoning-support”

Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

Unique: Thinking token handling is integrated into the inference pipeline, not a post-processing step. KV cache management accounts for thinking token overhead, preventing OOM errors when reasoning tokens exceed output tokens by orders of magnitude.

vs others: More transparent than OpenAI's o1 API because thinking tokens are accessible for debugging; more flexible than vLLM because it supports arbitrary thinking token formats without requiring model-specific parsing

via “reasoning-and-extended-thinking-support”

Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]

Unique: Implements provider-agnostic reasoning support by translating reasoning parameters to provider-native formats (OpenAI o1 reasoning, Claude extended thinking), with cost tracking for expensive reasoning tokens and access to reasoning traces for analysis

vs others: Abstracts provider differences in reasoning features, enabling applications to use reasoning models across providers without provider-specific code

via “extended thinking with user-controlled reasoning effort”

Anthropic's balanced model for production workloads.

Unique: Implements hybrid reasoning with both user-controlled extended thinking and automatic adaptive thinking, allowing fine-grained effort control via API parameters rather than binary on/off toggle. This dual-mode approach enables cost optimization by letting developers choose reasoning depth per-request while maintaining automatic reasoning for complex queries.

vs others: Offers more granular reasoning control than GPT-4o's reasoning mode (which lacks effort parameters) and lower cost than o1 models while maintaining competitive reasoning performance on complex tasks.

via “doctoral-level scientific reasoning and analysis”

OpenAI's most powerful reasoning model for complex problems.

Unique: Applies extended reasoning to scientific problem-solving with domain-specific reasoning about physical laws, chemical reactions, biological systems, and interdisciplinary connections — reasoning depth enables synthesis across domains rather than isolated problem-solving

vs others: Handles doctoral-level science questions with reasoning that integrates domain knowledge and explores competing explanations, outperforming GPT-4 on complex scientific reasoning by allocating more compute to understanding problem structure and constraints

via “reasoning model support with extended thinking”

An VS Code ChatGPT Copilot Extension

Unique: Treats reasoning models as first-class providers in the provider selection UI, allowing users to switch to o1/o3/DeepSeek R1 with the same configuration flow as standard models. Handles provider-specific restrictions (no system prompts, limited tool calling) transparently.

vs others: Provides access to reasoning models within the editor without separate tools or workflows, though reasoning models themselves are slower and more expensive than standard models, making them suitable only for complex problems.

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

via “systematic reasoning support”

Provide systematic thinking, mental models, and debugging approaches to enhance problem-solving capabilities. Enable structured reasoning and decision-making support for complex problems. Facilitate integration with MCP-compatible clients for advanced cognitive workflows.

Unique: Utilizes a modular reasoning framework that allows for dynamic adjustment of mental models based on user input, enhancing adaptability.

vs others: More flexible than traditional reasoning tools as it allows for real-time adjustments to mental models based on user feedback.

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

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 internalized thinking as part of the inference architecture rather than exposing chain-of-thought tokens, allowing the model to reason without token overhead while maintaining response quality. Uses adaptive computation allocation to balance reasoning depth with response latency based on problem complexity.

vs others: Provides reasoning benefits of extended chain-of-thought without the token cost and latency of explicit reasoning tokens, differentiating it from models like o1 that expose reasoning in the output stream.

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 “reasoning and step-by-step problem decomposition”

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...

Unique: Instruction-tuned on datasets containing explicit reasoning traces (e.g., math solutions with working, logic puzzles with step-by-step explanations), enabling the model to learn to generate intermediate reasoning as a learned behavior rather than relying on prompt engineering alone.

vs others: More reliable than base models at producing coherent reasoning chains; comparable to GPT-4 on standard benchmarks but with lower latency and cost, though may underperform on novel reasoning patterns not well-represented in training data.

via “extended reasoning with native thinking mode”

Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...

Unique: Integrates reasoning as a first-class inference primitive rather than a prompt engineering technique, using an internal thinking phase that explores solution spaces before output generation, with separate token accounting for transparency

vs others: Provides more reliable reasoning than prompt-based CoT approaches (like o1-preview) while maintaining faster inference than full-chain reasoning models, with explicit visibility into thinking token usage

via “reasoning and multi-step problem solving”

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

Unique: Mistral Nemo's instruction-tuning includes reasoning tasks and chain-of-thought examples, enabling it to generate explicit reasoning steps when prompted. The 128k context window enables longer reasoning chains than smaller-context models.

vs others: Reasoning capability is weaker than larger models (70B+) but sufficient for many reasoning tasks. Prompt-based chain-of-thought is more transparent than implicit reasoning but less efficient than specialized reasoning architectures.

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 “extended-chain-of-thought-generation”

Hermes 4 70B is a hybrid reasoning model from Nous Research, built on Meta-Llama-3.1-70B. It introduces the same hybrid mode as the larger 405B release, allowing the model to either...

Unique: Combines 70B parameter scale with process-reward modeling to maintain reasoning coherence across 10+ step chains, whereas smaller models typically degrade after 3-4 steps due to context drift and accumulated errors

vs others: Produces more reliable multi-step reasoning than GPT-3.5 while being more cost-effective than GPT-4 for reasoning tasks, with explicit step visibility that proprietary models don't expose

via “extended-chain-of-thought reasoning with explicit thinking tokens”

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: Uses dedicated thinking token architecture with RL-optimized allocation strategy, allowing the model to dynamically determine reasoning depth per query rather than applying fixed reasoning budgets like some competitors. Separates internal deliberation from output generation at the token level, enabling transparent reasoning traces.

vs others: Provides deeper, more transparent reasoning than standard LLMs while maintaining faster inference than some reasoning-specialized models by using learned heuristics to allocate thinking compute only when needed.

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 “extended reasoning with implicit chain-of-thought”

Grok 4 is xAI's latest reasoning model with a 256k context window. It supports parallel tool calling, structured outputs, and both image and text inputs. Note that reasoning is not...

Unique: Implicit reasoning allocation based on problem complexity, with reasoning traces integrated into output without explicit token budget management, contrasting with OpenAI's explicit reasoning token approach

vs others: More transparent reasoning than GPT-4o (which hides reasoning) but less controllable than o1 (which offers explicit reasoning token budgets); better for exploratory reasoning where depth is problem-dependent

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

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: Uses proprietary A3B (Adaptive Attention-Based Branching) mechanism that dynamically allocates compute across reasoning paths rather than fixed-depth chains, enabling adaptive reasoning depth based on problem complexity. This differs from static chain-of-thought approaches by treating reasoning as a branching tree with learned pruning heuristics.

vs others: Outperforms GPT-4 and Claude on mathematical reasoning benchmarks while maintaining 21B parameter efficiency through MoE architecture, making it faster and cheaper for reasoning-heavy workloads than larger closed-source 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