Multi Turn Conversational Reasoning With Search Context

via “conversational context persistence with multi-turn reasoning”

Advanced AI research agent with deep web search.

Unique: Uses conversation embeddings to detect topic continuity and avoid redundant searches — if a prior turn already covered a subtopic, agent skips re-searching it. Includes explicit context summarization to manage token limits in long conversations.

vs others: More sophisticated than ChatGPT's context handling because it uses semantic similarity to detect when prior searches are still relevant. More efficient than naive context concatenation by summarizing old turns.

via “multi-turn conversation with reasoning context preservation”

Cost-efficient reasoning model with configurable effort levels.

Unique: Preserves full reasoning context across conversation turns within the 200K window, enabling iterative refinement of reasoning rather than treating each query as isolated, which is essential for interactive problem-solving.

vs others: Better than o1 for multi-turn reasoning because the larger context window (200K vs 128K) accommodates longer conversation histories; more natural than stateless APIs because reasoning context is preserved across turns.

via “multi-turn conversational reasoning with search context”

Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro) For enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries wit...

Unique: Maintains semantic understanding of conversation intent across turns while triggering fresh web searches for each message, using dialogue context to disambiguate search queries and avoid redundant searches for repeated topics. Implements turn-level search relevance filtering to avoid polluting context with stale results from earlier turns.

vs others: More coherent than stateless search APIs because it tracks conversation intent across turns, and more current than standard LLMs because each turn gets fresh search results rather than relying on training data or a single initial search.

via “multi-turn-context-aware-search”

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: Implements context-aware query expansion where the model reformulates user queries using conversation history before executing searches, rather than searching raw user input. This enables implicit context passing without explicit user specification.

vs others: More natural than systems requiring explicit context specification in each query, and maintains coherence better than stateless search APIs that treat each query independently.

via “multi-turn conversation with persistent reasoning context”

Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro) Sonar Reasoning Pro is a premier reasoning model powered by DeepSeek R1 with Chain of Thought (CoT). Designed for...

Unique: Preserves the full reasoning trace and search history across turns, allowing the model to reference 'as I found earlier' and avoid redundant searches. This is implemented via explicit context window management rather than external memory stores.

vs others: More efficient than stateless APIs that require re-prompting with full context, but less persistent than systems with external knowledge bases or vector stores for long-term memory.

via “multi-turn conversational reasoning with context retention”

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 efficient context windowing that preserves semantic coherence across 20+ turn conversations without explicit summarization, using attention-based relevance weighting rather than naive truncation

vs others: Maintains conversation quality longer than Claude without requiring explicit summary injection, while offering lower latency than GPT-4 through OpenRouter's inference optimization

via “multi-turn conversational reasoning with state preservation”

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 uses a hierarchical attention mechanism that weights recent messages more heavily than older ones, allowing it to maintain coherence across 20+ turn conversations without explicit summarization

vs others: Maintains conversation quality longer than GPT-3.5 Turbo before context degradation, and requires less aggressive summarization than Llama 2 due to better long-context attention

via “multi-turn conversational reasoning with context retention”

Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...

Unique: Reasoning context is preserved across turns as part of the conversation history, enabling the model to reference and refine its own reasoning steps — this differs from standard chat models that treat reasoning as ephemeral

vs others: Enables iterative reasoning refinement that GPT-4 cannot do without explicit re-prompting, while maintaining lower latency than o1 for follow-up turns since reasoning context is cached

via “multi-turn conversational reasoning with extended context windows”

Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...

Unique: 200K token context window with constitutional AI alignment enables coherent reasoning across document-length inputs without external RAG, using native transformer attention rather than retrieval-augmented fallbacks

vs others: Larger context window than GPT-4 Turbo (128K) and maintains reasoning quality across full context length, outperforming alternatives that degrade with extended contexts

via “multi-turn conversational reasoning with context preservation”

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: 141B parameter scale with optimized attention patterns enables tracking complex multi-turn reasoning without explicit memory augmentation, using pure transformer architecture rather than hybrid memory-retrieval systems

vs others: Larger parameter count than GPT-3.5 and comparable to GPT-4 enables deeper reasoning within conversation context, while remaining faster and cheaper than GPT-4 Turbo for most dialogue tasks

via “multi-turn conversational reasoning with extended context”

Mistral Large 2 2411 is an update of [Mistral Large 2](/mistralai/mistral-large) released together with [Pixtral Large 2411](/mistralai/pixtral-large-2411) It provides a significant upgrade on the previous [Mistral Large 24.07](/mistralai/mistral-large-2407), with notable...

Unique: Mistral Large 2411 uses optimized transformer architecture with efficient attention patterns specifically tuned for 32K context, achieving lower latency than competitors on long-context tasks through architectural improvements over the 24.07 version

vs others: Provides better cost-to-performance ratio than GPT-4 for multi-turn conversations while maintaining comparable reasoning quality with lower API costs

via “multi-turn-conversation-with-persistent-reasoning-context”

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: Applies reasoning across conversation turns while maintaining implicit context about previous reasoning, allowing the model to avoid re-deriving conclusions. This differs from stateless reasoning where each query is independent.

vs others: Enables more natural iterative reasoning conversations than standard models because it learns to build on previous reasoning, but costs more due to accumulated context and reasoning tokens.

via “conversational reasoning with multi-turn context preservation”

DeepSeek R1 is here: 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 in an inference pass....

Unique: Maintains reasoning coherence across multi-turn conversations with explicit references to previous reasoning steps, enabling iterative refinement of solutions. The 671B parameter model with sparse activation efficiently processes long conversation histories while preserving reasoning quality.

vs others: More transparent than o1 on multi-turn reasoning (which doesn't expose intermediate steps) and more capable than GPT-4 on complex iterative problem-solving due to explicit reasoning visibility.

via “multi-turn conversational reasoning with context window management”

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...

Unique: Leverages MoE architecture to maintain coherent multi-turn reasoning with selective expert activation — experts specializing in dialogue coherence and context tracking are preferentially routed for conversation continuation, versus dense models that apply uniform attention across all parameters

vs others: Maintains conversation quality comparable to larger dense models while using 3.6B active parameters, reducing inference cost per turn versus GPT-3.5 or Llama 2 70B for long-running conversations

via “multi-turn conversational reasoning with state management”

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

Unique: Combines sparse attention over conversation history with full-sequence reasoning, allowing the model to selectively focus on relevant prior turns rather than equally weighting all history. This reduces noise from early conversation turns while maintaining coherence.

vs others: Handles longer conversation histories (100+ turns) more efficiently than GPT-4 due to sparse attention, reducing per-turn latency and token costs while maintaining context awareness comparable to dense-attention models.

via “multi-turn conversation with persistent reasoning context”

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 each turn while maintaining conversation context, enabling the model to reference and build on previous reasoning without explicit context engineering. Unlike stateless APIs, o3-pro's reasoning is conversation-aware, allowing iterative refinement.

vs others: Enables deeper reasoning across conversation turns than GPT-4 or Claude because thinking is applied per-turn, though at higher cost due to full history re-processing.

via “conversational-research-with-follow-up-refinement”

Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers...

Unique: Maintains conversational context across turns and refines searches based on follow-up questions, enabling iterative exploration rather than single-shot research

vs others: More interactive than single-turn research; better context maintenance than naive multi-turn systems that treat each turn independently

via “multi-turn conversation with context preservation and reasoning continuity”

Cogito v2.1 671B MoE represents one of the strongest open models globally, matching performance of frontier closed and open models. This model is trained using self play with reinforcement learning...

Unique: Uses MoE routing to efficiently manage growing context windows across turns, and self-play RL training to optimize recognition of when and how to reference previous reasoning. The model learns to explicitly acknowledge context dependencies and build reasoning chains across multiple exchanges rather than treating each turn independently.

vs others: Maintains reasoning continuity more effectively than stateless models like GPT-3.5, while the MoE architecture handles context growth more efficiently than dense models, making it suitable for extended problem-solving sessions without excessive latency growth.

via “conversational context management with turn-level reasoning”

DeepSeek V3.1 Nex-N1 is the flagship release of the Nex-N1 series — a post-trained model designed to highlight agent autonomy, tool use, and real-world productivity. Nex-N1 demonstrates competitive performance across...

Unique: Nex-N1 post-trained with emphasis on turn-level reasoning and explicit context tracking; maintains awareness of information flow and dependencies across conversation turns

vs others: Produces more contextually coherent responses than base models in long conversations because training emphasized explicit context management patterns

via “multi-turn-conversation-with-stateful-reasoning”

GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...

Unique: Maintains reasoning state across turns through extended context window and adaptive reasoning allocation, enabling more coherent long-form conversations than fixed-budget models

vs others: Better multi-turn coherence than GPT-4 Turbo due to improved reasoning allocation, and more natural dialogue than Claude 3.5 Sonnet for complex reasoning chains