Configurable Extended Thinking And Reasoning Mode

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 “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 “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 “extended thinking and reasoning mode for complex problem-solving”

Anthropic's developer console for Claude API.

Unique: Provides access to Claude's internal reasoning process via thinking blocks, allowing developers to inspect and debug Claude's reasoning rather than only seeing final outputs

vs others: More transparent than black-box reasoning in other LLMs, and allows developers to tune reasoning effort via budget parameters

via “deep thinking mode for complex mathematical and logical reasoning”

Shanghai AI Lab's multilingual foundation model.

Unique: Implements hidden reasoning tokens that don't consume user-visible token budget, allowing extended thinking without inflating output length; trained with only 4 trillion tokens (vs 8T+ for competing models) through efficient reasoning-focused pretraining

vs others: More efficient reasoning than o1-preview (requires fewer total tokens) while maintaining comparable accuracy on math benchmarks; faster than Llama 3.1 with extended thinking due to optimized attention patterns

via “native chain-of-thought reasoning with extended thinking”

Google's most capable model with 1M context and native thinking.

Unique: Native thinking is baked into model architecture rather than achieved through prompt engineering; enables 94.3% accuracy on GPQA Diamond (scientific knowledge) without requiring explicit CoT prompting, and 77.1% on ARC-AGI-2 abstract reasoning puzzles

vs others: Outperforms GPT-4 and Claude 3.5 on reasoning benchmarks (GPQA 94.3% vs Sonnet 89.9%) because thinking is a first-class architectural feature, not a post-hoc prompt technique

via “thinking mode and plan mode execution for complex reasoning tasks”

Claude Code Guide - Setup, Commands, workflows, agents, skills & tips-n-tricks go from beginner to power user!

Unique: Natively exposes Claude's thinking and plan modes as first-class CLI features rather than wrapping them in generic prompting patterns. The architecture allows users to toggle these modes via flags (e.g., --thinking, --plan) without modifying prompts, preserving the original user intent while leveraging extended reasoning.

vs others: Direct access to Claude's native reasoning capabilities without intermediate abstraction; competitors typically require manual prompt engineering to achieve similar reasoning depth.

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 “deep-reasoning-mode-for-complex-problems”

Official Kimi Code plugin for VS Code

Unique: Provides toggle-able extended reasoning mode within VS Code IDE context, allowing developers to invoke deep thinking without leaving their editor or switching to separate reasoning tools

vs others: Similar to Claude's extended thinking or o1's reasoning, but integrated into VS Code workflow; less flexible than standalone reasoning tools but more convenient for in-editor problem solving

via “extended-thinking code reasoning for complex problem-solving”

The frontier coding agent.

Unique: Explicitly exposes extended thinking as a selectable mode ('deep') within the agent, allowing developers to opt-in to slower but more thorough reasoning for complex problems. This is distinct from tools that use extended thinking transparently or not at all.

vs others: Provides explicit control over reasoning depth (smart/rush/deep modes) whereas Copilot uses a single model per request, and Cursor requires separate configuration or prompting to trigger deeper reasoning.

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 “thinking/reasoning model control with advanced configuration”

An extension that integrates OpenAI/Ollama/Anthropic/Gemini API Providers into GitHub Copilot Chat

Unique: Provides configuration UI for reasoning model parameters rather than requiring manual API request crafting. Abstracts away the complexity of thinking model APIs while maintaining full control over reasoning behavior through per-model settings.

vs others: Unlike generic LLM chat tools that treat all models identically, this recognizes reasoning models as a distinct category and provides dedicated configuration options, reducing friction for advanced use cases.

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 “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 thinking reasoning with step-by-step problem decomposition”

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 native extended thinking as a first-class capability integrated into the model architecture, allowing transparent reasoning-before-response without requiring prompt engineering or external chain-of-thought frameworks. The thinking process is computationally budgeted and automatically triggered based on query complexity.

vs others: Provides reasoning capabilities comparable to o1 but with broader multimodal support (image/audio inputs) and lower per-token cost than specialized reasoning models, though with less user control over reasoning depth.

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 “hybrid-reasoning-with-explicit-thinking-mode”

DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context...

Unique: Implements user-controlled explicit thinking via prompt templates rather than always-on reasoning, allowing per-request cost-performance optimization. The 37B active parameter subset processes thinking tokens in a separate phase before final generation, unlike models that interleave reasoning throughout decoding.

vs others: Offers finer-grained reasoning control than OpenAI o1 (which always reasons) and better cost efficiency than Claude 3.5 Sonnet's extended thinking by letting developers opt-in only when needed.

via “reasoning-augmented text generation with explicit thinking mode”

Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math,...

Unique: Implements explicit thinking mode as a native architectural feature rather than prompt-engineering workaround, using token-level gating to separate reasoning computation from response generation within a single 8B parameter model

vs others: Achieves reasoning performance comparable to 70B+ models while maintaining 8B parameter efficiency through dedicated thinking tokens, unlike Llama or Mistral which require larger model sizes or external chain-of-thought prompting

via “long-context reasoning with extended thinking”

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

Unique: Implements internal chain-of-thought reasoning within a 200K token window using transformer attention mechanisms, allowing reasoning to occur before output generation without requiring explicit prompt engineering for step-by-step thinking

vs others: Outperforms GPT-4o and Claude 3.5 Sonnet on complex reasoning tasks by maintaining coherence across longer reasoning chains while keeping the 200K context window practical for real-world applications

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.