Models

Kimi K2.5 is Moonshot AI’s proprietary multimodal model, offering cutting-edge visual coding abilities and supporting a self-directed agent swarm approach. Developed from Kimi K2 and further trained on around 15 trillion mixed visual and text tokens, it achieves excellent results in general reasoning, visual coding, and autonomous tool invocation.

As a SOTA 30B-class model, GLM-4.7-Flash provides a new option that balances efficiency and performance. It has been further optimized for agentic coding scenarios, enhancing coding abilities, long-term task planning, and tool integration, and has demonstrated leading results among open-source models of its size on multiple current public benchmark leaderboards.

GPT-5.2-Codex is an enhanced version of GPT-5.1-Codex, optimized for software engineering and coding tasks. Designed for both interactive sessions and longer, independent execution, it excels at building projects, developing features, debugging, large-scale refactoring, and code review. Compared to its predecessor, 5.2-Codex follows developer instructions more closely and delivers cleaner, higher-quality code. It integrates seamlessly with developer tools—CLI, IDEs, GitHub, and cloud platforms—and adapts its reasoning effort based on task complexity, providing quick responses for simple tasks and maintaining extended performance for larger projects. The model supports structured code reviews, detects critical flaws, validates behavior against tests, and handles multimodal inputs like images for UI work. It is specifically tailored for agentic coding applications.

MiniMax-M2.1 is a cutting-edge, lightweight large language model designed for coding, agentic workflows, and modern application development. With just 10 billion activated parameters, it offers a significant boost in real-world performance while ensuring low latency, high scalability, and cost-effectiveness. Compared to the previous version, M2.1 delivers more concise, clearer outputs and quicker response times. It excels in multilingual coding, achieving 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual, making it an adaptable engine for IDEs, coding tools, and a wide range of assistant applications.

GLM-4.7 is Z.AI’s newest flagship model, offering advancements in two main aspects: improved programming abilities and greater stability in multi-step reasoning and execution. It shows notable progress in handling complex agent tasks, while also providing more natural conversational experiences and enhanced front-end design.

MiMo-V2-Flash is an open-source foundational language model created by Xiaomi, featuring a Mixture-of-Experts architecture with 309 billion total parameters (15 billion active) and a hybrid attention mechanism. It supports hybrid-thinking, offers a 256K context window, and excels in reasoning, coding, and agent-based tasks. Ranking #1 among open-source models worldwide on SWE-bench Verified and Multilingual benchmarks, MiMo-V2-Flash matches the performance of Claude Sonnet 4.5 at just 3.5% of the cost. For best and fastest results when using agentic tools like Claude Code, Cline, or Roo Code, be sure to disable reasoning mode, as the model is extensively optimized for these scenarios.

Gemini 3 Flash Preview is a high-speed, cost-effective reasoning model built for agent-driven workflows, multi-turn conversation, and coding support. Offering near-Pro level performance in both reasoning and tool use, it stands out by delivering significantly lower latency than larger Gemini versions—making it ideal for interactive development, long-running agent loops, and collaborative programming. Compared to Gemini 2.5 Flash, it features notable improvements in reasoning ability, multimodal comprehension, and overall reliability. The model supports a 1M token context window and handles multimodal inputs—text, images, audio, video, and PDFs—with text-based output. Features like configurable reasoning levels, structured outputs, tool integration, and automatic context caching make it a strong choice for users seeking powerful agentic capabilities without the high cost or lag of more extensive models.

GPT-5.2 Chat (also known as Instant) is the fast and lightweight version of the 5.2 family, built for low-latency chatting while maintaining strong general intelligence. It leverages adaptive reasoning to focus more “thinking” on challenging queries, boosting accuracy in math, coding, and multi-step tasks without sacrificing speed in everyday conversations. The model is naturally warmer and more conversational, with improved instruction following and more stable short-form reasoning. GPT-5.2 Chat is ideal for high-throughput, interactive scenarios where quick response and consistency are more important than in-depth analysis.

GPT-5.2 Pro is OpenAI's most advanced model, featuring significant upgrades in agentic coding and long-context capabilities compared to GPT-5 Pro. It is specifically optimized for handling complex tasks that demand step-by-step reasoning, precise instruction following, and accuracy in critical scenarios. The model supports advanced test-time routing and sophisticated prompt understanding, including user cues like "think hard about this." Key improvements include reduced hallucination and sycophancy, along with stronger performance in coding, writing, and health-related tasks.

GPT-5.2 is the newest frontier-level model in the GPT-5 line, providing enhanced agentic abilities and better long-context performance than GPT-5.1. It employs adaptive reasoning to dynamically distribute computational resources, enabling quick responses to simple requests and deeper analysis for complex challenges. Designed for wide-ranging tasks, GPT-5.2 offers steady improvements in mathematics, programming, science, and tool usage, delivering more coherent long-form responses and increased reliability when using tools.

GPT-5.1-Codex-Max is OpenAI’s newest agentic coding model, created for extended, high-context software development tasks. Built on an enhanced 5.1 reasoning stack, it’s been trained with agentic workflows covering software engineering, mathematics, and research. GPT-5.1-Codex-Max offers faster performance, better reasoning abilities, and increased token efficiency throughout the development process.

DeepSeek-V3.2 is a large language model optimized for high computational efficiency and strong tool-use reasoning. It features DeepSeek Sparse Attention (DSA), a mechanism that lowers training and inference costs while maintaining quality in long-context tasks. A scalable reinforcement learning post-training framework further enhances reasoning, achieving performance comparable to GPT-5 and earning top results on the 2025 IMO and IOI. V3.2 also leverages large-scale agentic task synthesis to improve reasoning in practical tool-use scenarios, boosting its generalization and compliance in interactive environments.

Claude Opus 4.5 is Anthropic’s latest reasoning model, developed for advanced software engineering, complex agent workflows, and extended computer tasks. It features strong multimodal capabilities and performs competitively on real-world coding and reasoning benchmarks, with enhanced resilience against prompt injection. Optimized for efficiency at varying effort levels, it allows developers to balance speed, depth, and token usage according to their specific needs, thanks to a new parameter for controlling token efficiency. Opus 4.5 excels in advanced tool integration, contextual management, and multi-agent coordination, making it ideal for autonomous research, debugging, complex planning, and spreadsheet or browser manipulation. Compared to previous Opus generations, it delivers significant improvements in structured reasoning, long-duration task performance, execution reliability, and alignment, all while reducing token overhead.

Gemini 3 Pro Image Preview (Nano Banana Pro) is Google’s most advanced image generation and editing model, built on Gemini 3 Pro. Building on the original Nano Banana, it offers much improved multimodal reasoning, real-world grounding, and high-fidelity visual synthesis. The model produces context-rich visuals—from infographics and diagrams to cinematic composites—and can incorporate up-to-the-minute information through Search grounding. It leads the industry with sophisticated text rendering in images, handles consistent multi-image blending, and maintains accurate identity preservation for up to five subjects. Nano Banana Pro gives users fine-grained creative controls like localized edits, lighting and focus adjustments, camera transformations, 2K/4K output, and flexible aspect ratios. Tailored for professional design, product visualization, storyboarding, and complex compositions, it remains efficient for everyday image creation needs.

Gemini 3 Pro Preview is Google’s most advanced AI model, setting new records on leading benchmarks like LMArena (1501 Elo), GPQA Diamond (91.9%), and MathArena Apex (23.4%). With powerful reasoning and deep multimodal understanding across text, images, code, video, and audio, Gemini 3 Pro Preview delivers nuanced, context-aware responses and excels at complex problem-solving, scientific analysis, and creative coding tasks.

GPT-5.1-Codex-Mini is a more compact and faster variant of GPT-5.1-Codex.

GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It's designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5.1, Codex is more steerable, closely follows developer instructions, and produces cleaner, higher-quality code. Codex integrates into developer environments like the CLI, IDE extensions, GitHub, and cloud tasks. It adapts its reasoning dynamically—providing quick answers for small tasks and sustaining long, multi-hour runs for large projects. The model is trained for structured code reviews, identifying critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs like images or screenshots for UI development and integrates tools for search, dependency installation, and environment setup. Codex is specifically intended for agentic coding applications.

GPT-5.1 Chat (also known as Instant) is the fast, lightweight member of the 5.1 family, optimized for low-latency chat while retaining strong general intelligence. It uses adaptive reasoning to selectively “think” on harder queries, improving accuracy on math, coding, and multi-step tasks without slowing down typical conversations. The model is warmer and more conversational by default, with better instruction following and more stable short-form reasoning. GPT-5.1 Chat is designed for high-throughput, interactive workloads where responsiveness and consistency matter more than deep deliberation.

GPT-5.1 is the newest top-tier model in the GPT-5 series, featuring enhanced general reasoning, better instruction following, and a more natural conversational tone compared to GPT-5. With adaptive reasoning, it dynamically adjusts its computational effort—responding swiftly to simple queries and diving deeper into complex tasks. Explanations are now clearer and use less jargon, making challenging topics easier to grasp. Designed for a wide range of tasks, GPT-5.1 consistently improves performance in math, coding, and structured analysis, offering more cohesive long-form responses and more reliable tool usage. Its conversation style is warmer and more intuitive, yet still precise. GPT-5.1 stands as the main, fully capable successor to GPT-5.

Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model, extending the K2 series into agentic, long-horizon reasoning. Built on a trillion-parameter Mixture-of-Experts (MoE) architecture, it activates 32 billion parameters per forward pass and supports a 256k-token context window. Optimized for persistent step-by-step thought and dynamic tool use, it enables complex reasoning workflows and stable multi-agent behavior across 200–300 tool calls, setting new open-source records on HLE, BrowseComp, SWE-Multilingual, and LiveCodeBench. With MuonClip optimization and large-scale MoE architecture, it delivers strong reasoning depth and high inference efficiency for demanding agentic and analytical tasks.

MiniMax-M2 is a compact, efficient language model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It achieves near-frontier reasoning and tool use with low latency and deployment cost. The model excels in code generation, multi-file editing, compile-run-fix cycles, and automated test repair, showing strong results on SWE-Bench and Terminal-Bench. MiniMax-M2 performs well in agentic benchmarks like BrowseComp and GAIA, handling long-term planning, retrieval, and error recovery. With a small activation footprint, it delivers fast inference and high concurrency, making it ideal for developer tools, agents, and applications that demand cost-effective, responsive reasoning.

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, offering near-frontier intelligence with much lower cost and latency than larger Claude models. It matches Claude Sonnet 4’s performance in reasoning, coding, and computer-use tasks, making it ideal for real-time and large-scale applications. Haiku 4.5 introduces controllable reasoning depth, supports summarized or interleaved thought outputs, and enables tool-assisted workflows across coding, bash, web search, and computer-use tools. With over 73% on SWE-bench Verified, it stands among the top coding models while maintaining fast responsiveness for sub-agents, parallel execution, and scaled deployment.

Gemini 2.5 Flash Image, also known as "Nano Banana" is a state-of-the-art image generation model with strong contextual understanding. It supports image generation, editing, and multi-turn conversational interactions.

GLM-4.6 is the latest version in the GLM series, featuring a longer 200K token context window (up from 128K in GLM-4.5) for handling more complex tasks. It offers improved coding performance with higher benchmark scores and better real-world results, including visually enhanced front-end code generation. The model also delivers stronger reasoning, more effective tool use during inference, better integration within agent frameworks, and more refined, human-like writing style compared to GLM-4.5.

Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model, optimized for real-world agents and coding workflows. It achieves state-of-the-art results on coding benchmarks like SWE-bench Verified, with notable improvements in system design, code security, and following specifications. Designed for extended autonomous operation, the model maintains task continuity across sessions and offers fact-based progress tracking. Sonnet 4.5 features enhanced agentic abilities, such as improved tool orchestration, speculative parallel execution, and more efficient context and memory management. With better context tracking and awareness of token usage across tool calls, it excels in multi-context and long-running workflows. Key use cases include software engineering, cybersecurity, financial analysis, research agents, and other areas requiring sustained reasoning and tool use.

DeepSeek-V3.2-Exp is an experimental large language model from DeepSeek, serving as an intermediate step between V3.1 and future architectures. It features DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that enhances training and inference efficiency for long-context tasks while preserving high output quality.

Gemini 2.5 Flash-Lite Preview September 2025 Checkpoint is a lightweight, high-throughput model from the Gemini 2.5 family, focused on ultra-low latency and cost efficiency. It delivers even faster token generation, concise output, and improved performance on standard benchmarks compared to earlier Flash-Lite models, making it ideal for large-scale, real-time applications.

Gemini 2.5 Flash Preview September 2025 Checkpoint is Google’s high-performance model, built for advanced reasoning, code generation, mathematical tasks, and scientific applications. This version introduces faster, more efficient output and smarter tool use for complex, multi-step workflows.

GPT-5-Codex is a specialized version of GPT-5 tailored for software engineering and coding tasks. It is suitable for both interactive development sessions and the independent execution of complex engineering projects. The model is capable of building projects from scratch, developing new features, debugging, performing large-scale refactoring, and conducting code reviews. Compared to the standard GPT-5, Codex offers greater steerability, follows developer instructions more closely, and delivers cleaner, higher-quality code.

DeepSeek-V3.1 Terminus is an enhanced version of DeepSeek V3.1 that retains the original model’s capabilities while resolving user-reported issues, such as language consistency and agent functionality. The update further refines the model’s performance in coding and search agent tasks. This large-scale hybrid reasoning model (671B parameters, 37B active) supports both thinking and non-thinking modes. Building on the DeepSeek-V3 foundation, it incorporates a two-phase long-context training approach, allowing for up to 128K tokens, and adopts FP8 microscaling for more efficient inference.

GLM-4.5 is the latest flagship foundation model from Z.AI, specifically designed for agent-based applications. It utilizes a Mixture-of-Experts (MoE) architecture and supports context lengths of up to 128k tokens. GLM-4.5 offers significantly improved capabilities in reasoning, code generation, and agent alignment. It features a hybrid inference mode with two options: a "thinking mode," tailored for complex reasoning and tool usage, and a "non-thinking mode," optimized for instant responses.

Qwen3-Next-80B-A3B-Thinking is a reasoning-focused model that generates structured “thinking” traces by default. Suited for complex multi-step tasks like math proofs, code synthesis, logic, and agentic planning. Compared to earlier Qwen3 models, it’s more stable with long reasoning chains and scales efficiently during inference. Designed for agent frameworks, function calling, retrieval-based workflows, and benchmarks needing step-by-step solutions, it supports detailed completions and faster output through multi-token prediction. Runs only in thinking mode.

Gemini 2.5 Flash Image Preview is a state of the art image generation model with contextual understanding. It is capable of image generation, edits, and multi-turn conversations.

DeepSeek-V3.1 is a 671B-parameter hybrid reasoning model (37B active), supporting both "thinking" and "non-thinking" modes via prompt templates. It extends DeepSeek-V3 with two-phase long-context training (up to 128K tokens) and uses FP8 microscaling for efficient inference. The model excels in tool use, code generation, and reasoning, with performance comparable to DeepSeek-R1 but with faster responses. It supports structured tool calling, code agents, and search agents, making it ideal for research and agentic workflows. Successor to DeepSeek V3-0324, it delivers strong performance across diverse tasks.

A compact variant of GPT-5, designed for efficient handling of lighter-weight reasoning and conversational tasks. GPT-5 Mini retains the instruction-following and safety features of its larger counterpart, but with reduced latency and cost. It is the direct successor to OpenAI’s o4-mini model, making it ideal for scalable, cost-sensitive deployments.

GPT-5 Chat is tailored for advanced, natural, and context-aware conversations in enterprise environments. It leverages the latest advancements in OpenAI’s conversational AI, supporting multimodal and dynamic dialogue with enhanced context retention and user intent understanding.

The smallest and fastest member of the GPT-5 family, optimized for developer tools, rapid user interactions, and ultra-low latency environments. While it offers limited reasoning depth compared to larger models, GPT-5-Nano preserves essential instruction-following and safety mechanisms. It is the successor to GPT-4.1-nano and is best suited for real-time, cost-sensitive, or embedded applications.

A compact variant of GPT-5, designed for efficient handling of lighter-weight reasoning and conversational tasks. GPT-5 Mini retains the instruction-following and safety features of its larger counterpart, but with reduced latency and cost. It is the direct successor to OpenAI’s o4-mini model, making it ideal for scalable, cost-sensitive deployments.

OpenAI’s most advanced large language model, engineered for high-stakes applications requiring step-by-step reasoning, precise instruction following, and robust code generation. GPT-5 introduces major improvements in factual accuracy, user intent understanding, and hallucination reduction. It supports advanced prompt routing, user-specified intent (such as "think hard about this"), and is optimized for complex workflows in coding, writing, and health-related domains.

OpenAI’s 21B-parameter open-weight Mixture-of-Experts (MoE) model, released under the Apache 2.0 license. Features 3.6B active parameters per forward pass, optimized for low-latency inference and deployability on consumer or single-GPU hardware. Trained in OpenAI’s Harmony response format, it supports reasoning level configuration, fine-tuning, and agentic capabilities such as function calling and structured outputs.

An open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI, designed for high-reasoning, agentic, and general-purpose production use cases. Activates 5.1B parameters per forward pass and is optimized for single H100 GPU deployment with native MXFP4 quantization. Supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.

Claude Opus 4.1 is Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves high scores on SWE-bench Verified and excels in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for research, data analysis, and tool-assisted workflows.

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. Activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This "thinking-only" variant enhances structured logical reasoning, mathematics, science, and long-form generation, and is instruction-tuned for step-by-step reasoning, tool use, agentic workflows, and multilingual tasks.

Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. Optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. Features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts), and supports variable pricing based on context length.

Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. Optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. Supports a native 262K context length and delivers significant gains in knowledge coverage, long-context reasoning, and coding benchmarks.

Gemini 2.5 Flash-Lite is a streamlined reasoning model from the Gemini 2.5 family, designed for extremely low latency and cost-effectiveness. It delivers higher throughput, quicker token generation, and enhanced performance on standard benchmarks compared to previous Flash models.

Gemini 2.5 Flash is Google’s high-performance workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. Includes built-in "thinking" capabilities and is configurable through a "max tokens for reasoning" parameter for fine-tuned performance.

MiniMax-M1 is a large-scale, open-weight reasoning model with 456B total parameters and 45.9B active per token, leveraging a hybrid Mixture-of-Experts (MoE) architecture and a custom "lightning attention" mechanism. It supports context windows up to 1 million tokens and is optimized for long-context understanding, software engineering, agentic tool use, and mathematical reasoning. The model is trained via a custom reinforcement learning pipeline (CISPO) and demonstrates strong performance on FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench.

Gemini 2.0 Flash Lite is optimized for extremely fast response times and low cost, while maintaining the quality of larger models. Ideal for real-time and large-scale applications.

Gemini 2.5 Flash is Google’s high-performance workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. Includes built-in "thinking" capabilities and is configurable through a "max tokens for reasoning" parameter for fine-tuned performance.

Gemini 2.5 Pro is Google’s state-of-the-art AI model, designed for advanced reasoning, coding, mathematics, and scientific tasks. Employs “thinking” capabilities for nuanced context handling and achieves top-tier performance on multiple benchmarks, including first-place on the LMArena leaderboard.

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. Trained with large-scale reinforcement learning for chain-of-thought reasoning, it is optimized for math, science, programming, and other STEM tasks, consistently achieving PhD-level accuracy on industry benchmarks.

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model from Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens. Built for high efficiency and local or commercial deployment, it is instruction-tuned for multilingual chat, captioning, and image understanding.

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. Supports seamless switching between a "thinking" mode for tasks like math, coding, and logical inference, and a "non-thinking" mode for faster, general-purpose conversation. Demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects.

DeepSeek V3 is a 685B-parameter, mixture-of-experts model and the latest iteration of the flagship chat model family from the DeepSeek team. Succeeds the previous DeepSeek V3 model and demonstrates strong performance across a variety of tasks.

A well-rounded, powerful model from OpenAI, setting new standards in math, science, coding, and visual reasoning. Excels at technical writing and instruction-following, and is designed for multi-step problem solving across text, code, and images. BYOK is required for access.

A compact reasoning model in OpenAI’s o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. Supports tool use and demonstrates competitive reasoning and coding performance across benchmarks, outperforming its predecessor o3-mini and approaching o3 in some domains. Well-suited for high-throughput scenarios where latency or cost is critical.

Claude Opus 4.1 is Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves high scores on SWE-bench Verified and excels in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for research, data analysis, and tool-assisted workflows.

Claude Sonnet 4 is a next-generation model from Anthropic, significantly enhancing coding and reasoning capabilities over its predecessor. It achieves state-of-the-art performance on SWE-bench, balances capability and computational efficiency, and is optimized for both routine and complex software development projects. Key features include improved codebase navigation, reduced error rates, and increased reliability in following intricate instructions.

Llama Guard 4 is a multimodal content safety classifier derived from Llama 4 Scout, fine-tuned for both prompt and response classification. It supports content moderation for English and multiple languages, including mixed text-and-image prompts. The model is aligned with the MLCommons hazards taxonomy and is integrated into the Llama Moderations API for robust safety classification in text and images.

A cost-efficient language model from OpenAI, optimized for STEM reasoning tasks, especially in science, mathematics, and coding. Supports the `reasoning_effort` parameter for adjustable thinking time and features significant improvements over its predecessor, with better performance on complex questions and lower latency and cost.

Command A is an open-weights 111B parameter model from Cohere, featuring a 256k context window and optimized for agentic, multilingual, and coding use cases. It delivers high performance with minimal hardware costs, excelling in business-critical workflows that require advanced reasoning, tool use, and language understanding across multiple languages.

Gemini Flash 2.0 offers significantly faster time to first token (TTFT) compared to previous versions, while maintaining quality on par with larger models. Introduces enhancements in multimodal understanding, coding, complex instruction following, and function calling for robust agentic experiences.

Claude 3.7 Sonnet is an advanced large language model from Anthropic, featuring improved reasoning, coding, and problem-solving abilities. It introduces a hybrid reasoning approach, allowing users to choose between rapid responses and extended, step-by-step processing for complex tasks. The model excels in agentic workflows, front-end development, and full-stack updates, and offers an extended reasoning mode for enhanced accuracy in math, coding, and instruction-following.

Gemini Flash 2.0 offers significantly faster time to first token (TTFT) compared to previous versions, while maintaining quality on par with larger models. Introduces enhancements in multimodal understanding, coding, complex instruction following, and function calling for robust agentic experiences.