Alternatives to Kimi K2.7 Code
Compare Kimi K2.7 Code alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Kimi K2.7 Code in 2026. Compare features, ratings, user reviews, pricing, and more from Kimi K2.7 Code competitors and alternatives in order to make an informed decision for your business.
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Sakana Fugu
Sakana AI
Sakana Fugu is an AI model and multi-agent AI system delivered through a single OpenAI-compatible API. The platform dynamically orchestrates a pool of powerful models to solve complex tasks without requiring users to manually choose models, assign roles, or design agent workflows. Fugu learns how to assemble and coordinate agents for coding, reasoning, research, cybersecurity, scientific analysis, and other quality-critical work. Users can choose between Fugu for balanced performance and latency or Fugu Ultra for harder, high-stakes tasks that need deeper expert coordination. The platform also allows users to control which models or providers can participate in the agent pool to support privacy, compliance, and organizational requirements. Sakana Fugu helps teams access collective AI intelligence through one endpoint while reducing single-vendor dependency and improving performance on complex multi-step workflows.Starting Price: $20/month -
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Sakana Fugu Ultra
Sakana AI
Sakana Fugu Ultra is the higher-performance version of Sakana Fugu, built to coordinate a deeper pool of expert AI agents for demanding, high-stakes tasks. The model operates through a single OpenAI-compatible API while dynamically orchestrating multiple powerful models behind the scenes. It is designed to maximize answer quality for complex workflows such as coding, code review, paper reproduction, cybersecurity analysis, scientific reasoning, patent investigation, and autonomous research. Fugu Ultra uses learned orchestration techniques to assemble, route, and coordinate agents instead of relying on hand-designed workflows or a single frontier model. Users can access advanced multi-agent intelligence without manually managing separate models, prompts, or collaboration patterns. Sakana Fugu Ultra is built for teams that need stronger performance, deeper reasoning, and more reliable results on difficult multi-step problems.Starting Price: $20 per month -
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Seed2.1 Pro
ByteDance
Seed2.1 Pro is a next-generation AI productivity model built to handle complex, real-world work across general agents, code engineering, and multimodal understanding. It reliably executes multi-step tasks for high-value office work and everyday consultation, including project planning, file processing, research, tool use, spreadsheet analysis, lesson-plan slide generation, and industry report creation across tools and environments. In software development workflows, Seed2.1 Pro strengthens end-to-end delivery by improving requirement understanding, architecture design, coding, debugging, implementation, and validation. Its agent capabilities are designed to make steady progress on difficult tasks and return practical, verifiable results rather than isolated responses. The model also advances knowledge, reasoning, visual understanding, spatial reasoning, and long-context processing, giving agents a stronger foundation for complex decision-making and execution. -
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Seed2.1 Turbo
ByteDance
Seed2.1 Turbo is a next-generation AI productivity model designed to execute complex real-world tasks with strong general-agent, coding, and multimodal capabilities. It goes beyond one-off answers by carrying multi-step workflows toward defined goals and producing practical, usable outcomes across tools, environments, and interaction modes. For professional work and everyday consultation, it can support project planning, document and file processing, information analysis, solution design, content planning, tool use, and results consolidation. It also handles teaching, office, and research scenarios such as generating lesson-plan slides, analyzing complex spreadsheets, and producing industry reports. In software engineering, Seed2.1 Turbo supports end-to-end delivery across requirement analysis, feature implementation, bug fixing, environment setup, terminal usage, and result validation, while understanding codebase architecture, dependencies, and business logic to coordinate changes. -
5
SWE-1.6
Cognition
SWE-1.6 is an engineering–focused AI model developed by Cognition and integrated into the Devin (Windsurf) environment, designed to optimize both raw intelligence and what the company calls “model UX,” or the overall feel and efficiency of interacting with an AI agent. It represents a new iteration in the SWE model family, improving performance on benchmarks such as SWE-Bench Pro by over 10% compared to SWE-1.5 while maintaining similar underlying capabilities. It was trained from scratch to jointly improve reasoning quality and user experience, addressing issues observed in earlier versions such as overthinking simple problems, taking too many steps, looping in repetitive reasoning, and relying excessively on terminal commands instead of specialized tools. SWE-1.6 introduces behavioral improvements such as more frequent parallel tool usage, faster context retrieval, and reduced need for user input, resulting in smoother and more efficient workflows. -
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SWE-1.7
Cognition
SWE-1.7 is Cognition’s frontier software engineering model designed to deliver high intelligence at a lower rollout cost. The model is optimized for long-horizon agentic coding tasks, including debugging, feature implementation, codebase exploration, migrations, terminal workflows, and multilingual software engineering. SWE-1.7 was trained from a Kimi K2.7 base using large-scale reinforcement learning improvements across infrastructure, data quality, training stability, self-compaction, and long-running task execution. It is built to explore codebases thoroughly, probe edge cases, identify hidden requirements, and produce more complete end-to-end solutions. The model is available in Devin across web, desktop, and CLI through Cerebras at very high serving speeds. SWE-1.7 is positioned for developers and engineering teams that need cost-efficient frontier-level coding intelligence for complex real-world software work.Starting Price: $20/month -
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Ornith-1.0
DeepReinforce
Ornith-1.0 is a self-improving family of models built specially for agentic coding tasks. It spans the full spectrum from compact 9B Dense models suitable for edge device deployment to 397B MoE frontier-scale models optimized for maximum performance, with variants including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. Built on top of pretrained Gemma 4 and Qwen 3.5, Ornith-1.0 achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks. Its key innovation is a self-improving training framework that learns to generate both solution rollouts and the task-specific scaffolds that guide those rollouts. Instead of relying on fixed, human-designed harnesses, Ornith-1.0 treats the scaffold as a learnable object that co-evolves with the policy, allowing the model to jointly optimize the orchestration and the final solution.Starting Price: Free -
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Muse Spark 1.1
Meta
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.Starting Price: $1.25 per 1M tokens (input) -
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Nemotron 3
NVIDIA
NVIDIA Nemotron 3 is a family of open large language models developed by NVIDIA to power advanced reasoning, conversational AI, and autonomous AI agents. The Nemotron 3 series includes three models designed for different scales of AI workloads while maintaining high efficiency and accuracy. These models focus on “agentic AI” capabilities, meaning they can perform multi-step reasoning, coordinate with tools, and operate as components within multi-agent systems used in automation, research, and enterprise applications. The architecture uses a hybrid mixture-of-experts (MoE) design combined with transformer-based techniques, allowing the model to activate only a subset of parameters for each task, which improves performance while reducing computational cost. Nemotron 3 models are built to deliver strong reasoning, conversational, and planning abilities while maintaining high throughput for large-scale deployment. -
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Nemotron 3 Super
NVIDIA
Nemotron-3 Super is part of NVIDIA’s Nemotron 3 family of open models designed to enable advanced agentic AI systems that can reason, plan, and execute multi-step workflows across complex environments. The model introduces a hybrid Mamba-Transformer Mixture-of-Experts architecture that combines the efficiency of state-space Mamba layers with the contextual understanding of transformer attention, allowing it to process long sequences and complex reasoning tasks with high accuracy and throughput. This architecture activates only a subset of model parameters for each token, improving computational efficiency while maintaining strong reasoning capabilities and enabling scalable inference for large workloads. Nemotron-3 Super contains roughly 120 billion parameters with around 12 billion active during inference, accelerating multi-step reasoning and collaborative agent interactions across large contexts. -
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Nemotron 3 Ultra
NVIDIA
Nemotron 3 Nano is a compact, open large language model in NVIDIA’s Nemotron 3 family, designed for efficient agentic reasoning, conversational AI, and coding tasks. It uses a hybrid Mixture-of-Experts Mamba-Transformer architecture that activates only a small subset of parameters per token, enabling low-latency inference while maintaining strong accuracy and reasoning performance. It has approximately 31.6 billion total parameters with around 3.2 billion active (3.6 billion including embeddings), allowing it to achieve higher accuracy than previous Nemotron 2 Nano while using less computation per forward pass. Nemotron 3 Nano supports long-context processing of up to one million tokens, enabling it to handle large documents, multi-step workflows, and extended reasoning chains in a single pass. It is designed for high-throughput, real-time execution, excelling in multi-turn conversations, tool calling, and agent-based workflows where tasks require planning, reasoning, and more. -
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Qwen3.7-Max
Alibaba
Qwen3.7-Max is Qwen’s latest proprietary model designed for the agent era, built to be a versatile agent foundation that is equally capable of writing and debugging code, automating office workflows, and sustaining autonomous browser sessions over long horizons. It reaches frontier-level coding performance, with stronger results across software engineering, terminal tasks, GUI grounding, web browsing, and agentic tool use. Qwen3.7-Max is designed to reduce the gap between model intelligence and real agent execution by supporting planning, long-context reasoning, reliable function calling, and multi-step task completion across complex workflows. It also strengthens multimodal and document-oriented work through Qwen Studio, which supports chatbot interaction, image and video understanding, image generation, document processing, presentation generation, coding assistance, deep research, and web development.Starting Price: Free -
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Ring 2.6
Ant Group
Ring is a trillion-parameter thinking model from Ant Group, designed for real-world Agent workflows. It uses the same Mixture of Experts architecture as Ling, activating about 63B parameters per inference, and focuses on coding agents, tool use, multi-tool collaboration, engineering development, research analysis, and long-horizon task execution. Rather than only pursuing “smarter” results, Ring is built to consistently complete complex tasks at reasonable cost, balancing quality, speed, and execution efficiency in production environments. Ring-2.6-1T introduces an adjustable Reasoning Effort mechanism with high and xhigh reasoning intensity levels, using adaptive reasoning budget allocation based on task complexity. High mode is designed for high-frequency Agent workflows, lower token cost, faster multi-step execution, multi-turn interaction, tool collaboration, and task decomposition.Starting Price: $0.0028 per 1M tokens -
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Nex-N2-Pro
Nex-AGI
Nex-N2-Pro is an open source agentic model with Agentic Thinking, built for real-world productivity scenarios where reasoning must turn into executable, verifiable, and iterable action. Rather than treating reasoning, tool use, and environment execution as separate capabilities, Nex-N2 unifies them through a framework that connects requirement understanding, task planning, code implementation, environmental feedback, evaluation, and debugging, and continuous iteration into a single closed loop. Its thinking paradigm is unified across search, coding, and agentic tool calling, following a consistent structure of goal decomposition, state tracking, strategy adjustment, and self-verification, which is especially useful in mixed tasks such as coding workflows that include searches and tool calls. Adaptive Thinking lets the model decide when to think and how deeply, executing simple actions quickly while reasoning more thoroughly on critical decisions to allocate resources efficiently.Starting Price: Free -
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Nex-N2-mini
Nex-AGI
Nex-N2-mini is an open source agentic model with Agentic Thinking, built for real-world productivity scenarios where fast instruction following, real-time tool execution, and cost-effective large-scale deployment matter. As part of the Nex-N2 family, it is designed to turn thinking into actions that are executable, verifiable, and iterable, rather than treating reasoning, tool use, and environment execution as separate capabilities. Nex-N2-mini uses the same unified Agentic Thinking framework as Nex-N2-Pro, connecting requirement understanding, task planning, code implementation, environmental feedback, evaluation, debugging, and continuous iteration into one closed loop. Its thinking paradigm stays consistent across search, coding, and agentic tool calling, following goal decomposition, state tracking, strategy adjustment, and self-verification, which is especially useful in mixed tasks where coding is interleaved with searches and tool calls.Starting Price: Free -
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SubQ 1.1 Small
Subquadratic
SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information. -
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Claude Fable 5
Anthropic
Claude Fable 5 is an advanced AI model from Anthropic designed to assist with software engineering, research, knowledge work, vision tasks, and complex reasoning. Built on the Mythos-class architecture, it delivers significantly improved performance across coding, analysis, and long-context workflows. The model can handle extended autonomous tasks while maintaining focus and consistency over large amounts of information. Claude Fable 5 integrates advanced reasoning, multimodal understanding, and memory capabilities to support professional and enterprise use cases. Anthropic has implemented specialized safeguards that automatically route certain high-risk cybersecurity, biology, chemistry, and model distillation requests to a different model. Claude Fable 5 helps organizations and professionals accelerate complex work while maintaining strong safety and governance controls.Starting Price: $10 per 1 million (input) -
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Claude Mythos 5
Anthropic
Claude Mythos 5 is Anthropic’s most advanced restricted-access AI model, designed for trusted cyberdefenders, infrastructure providers, and select research organizations. It uses the same underlying model as Claude Fable 5 but provides lifted safeguards in approved areas for specialized high-trust use cases. The model delivers exceptional capabilities in cybersecurity, software engineering, scientific research, long-context reasoning, vision, and autonomous task execution. Anthropic initially deployed Claude Mythos 5 through Project Glasswing in collaboration with the U.S. government to help protect critical software and infrastructure. The model also shows strong potential in life sciences, including protein design, molecular biology hypothesis generation, and genomics research. Claude Mythos 5 is built for organizations that need frontier AI capabilities under controlled, trusted-access conditions.Starting Price: $10 per 1 million (input) -
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Claude Opus 4.6
Anthropic
Claude Opus 4.6 is an advanced AI model developed by Anthropic, designed for high-level reasoning, coding, and knowledge work tasks. It introduces significant improvements in coding, debugging, and code review capabilities. The model can handle long, complex workflows and sustain agentic tasks with greater reliability. It features a 1 million token context window in beta, enabling it to process and retain large amounts of information. Claude Opus 4.6 is optimized for tasks such as financial analysis, research, and document creation. It also integrates with tools like Excel and PowerPoint for enhanced productivity. Overall, it is a state-of-the-art AI model built for complex, real-world professional applications. -
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Claude Opus 4.7
Anthropic
Claude Opus 4.7 is the latest Anthropic AI model release designed to significantly improve performance in advanced software engineering and complex problem-solving tasks. It builds upon the previous Opus 4.6 model by delivering stronger results on difficult coding challenges and long-running workflows. The model is known for its ability to follow instructions precisely and verify its own outputs for greater reliability. It also introduces enhanced multimodal capabilities, particularly in processing high-resolution images with improved accuracy. Opus 4.7 supports more detailed visual tasks such as analyzing dense screenshots and extracting data from complex diagrams. In professional settings, it produces higher-quality outputs including documents, presentations, and user interfaces. The model includes updated safety features that detect and block high-risk cybersecurity-related requests.Starting Price: $5 per million tokens (input) -
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Claude Opus 4.8
Anthropic
Claude Opus 4.8 is a powerful AI model from Anthropic designed to deliver stronger coding, reasoning, agentic workflows, and advanced collaboration capabilities for developers, enterprises, and AI-powered productivity tasks. The model builds on Claude Opus 4.7 with improvements across coding benchmarks, practical knowledge work, alignment, and reliability while maintaining the same pricing structure. Claude Opus 4.8 introduces enhanced honesty and reasoning behavior, making it less likely to generate unsupported claims or overlook flaws during complex tasks such as software development and agent execution. The release also includes new features such as effort control settings, fast mode for lower-cost high-speed processing, and dynamic workflows in Claude Code that allow the system to coordinate hundreds of parallel subagents for large-scale tasks.Starting Price: $5 per 1M (input) -
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Claude Sonnet 4.6
Anthropic
Claude Sonnet 4.6 is Anthropic’s most advanced Sonnet model to date, delivering significant upgrades across coding, computer use, long-context reasoning, agent planning, and knowledge work. It introduces a 1 million token context window in beta, allowing users to analyze entire codebases, lengthy contracts, or large research collections in a single session. The model demonstrates major improvements in instruction following, consistency, and reduced hallucinations compared to previous Sonnet versions. In developer testing, users strongly preferred Sonnet 4.6 over Sonnet 4.5 and even favored it over Opus 4.5 in many coding scenarios. Its enhanced computer-use capabilities enable it to interact with real software interfaces similarly to a human, improving automation for legacy systems without APIs. Sonnet 4.6 also performs strongly on major benchmarks, approaching Opus-level intelligence at a more accessible price point. -
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Claude Sonnet 5
Anthropic
Claude Sonnet 5 is Anthropic's latest AI model, designed to deliver stronger agentic capabilities for coding, reasoning, tool use, and knowledge work while maintaining the efficiency of the Sonnet family. The model can independently plan tasks, use external tools such as browsers and terminals, and complete complex workflows that previously required larger AI models. Sonnet 5 significantly improves upon Claude Sonnet 4.6 with better reasoning, coding performance, reduced hallucinations, stronger safety behavior, and more effective autonomous task execution. It is available across Claude plans and through the Claude API with OpenAI-style developer access for application integration. Anthropic also introduced lower introductory API pricing, making Sonnet 5 a cost-effective option for developers building AI-powered products. By combining advanced agentic capabilities with improved safety and competitive pricing, Claude Sonnet 5 helps developers build more capable AI applications.Starting Price: $2 per 1M tokens (input) -
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Bonsai 27B
PrismML
Bonsai 27B is the new multimodal flagship of the Bonsai family and the first 27B-class model to run on a phone. Based on Qwen3.6 27B, it brings a new capability tier to local devices: multi-step reasoning, structured tool calls, vision tasks, and computer-use agentic loops that stay coherent across many steps. Bonsai 27B comes in two variants. Ternary Bonsai 27B uses ternary weights with FP16 group-wise scaling, giving 1.71 effective bits per weight and a 5.9 GB footprint for the quality-oriented laptop-class version. 1-bit Bonsai 27B uses binary weights with the same group-wise scaling, giving 1.125 effective bits per weight and a 3.9 GB footprint that fits within the memory budget of an iPhone 17 Pro. Both variants run end-to-end across the language network, embeddings, attention, MLPs, and LM head with no higher-precision escape hatches. They are multimodal, with a compact 4-bit vision tower, so on-device workflows can understand screenshots, documents, and camera input. -
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Big Pickle
OpenCode Zen
Big Pickle is an AI model available through OpenCode Zen, a curated model provider focused on coding-agent workflows. The model is designed for text-based input, reasoning tasks, function calling, and developer workflows that require long-context understanding. Big Pickle supports a large context window, making it useful for working across bigger codebases, project files, technical prompts, and multi-step coding tasks. It can be accessed through OpenCode Zen using an OpenAI-compatible API format, allowing developers to integrate it into agentic coding tools and automation workflows. The model is positioned as a free or low-cost option within OpenCode’s coding-agent ecosystem. Big Pickle helps developers experiment with AI-assisted coding, reasoning, tool use, and long-context automation without relying only on premium frontier models.Starting Price: Free -
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Composer 2.5
Cursor
Composer 2.5 is the latest AI coding model released by Cursor, offering major improvements in intelligence, collaboration, and long-task performance compared to Composer 2. The model is designed to follow complex instructions more accurately while providing a smoother and more natural user experience during coding sessions. Cursor enhanced Composer 2.5 through larger-scale training, more advanced reinforcement learning environments, and improved behavioral tuning focused on communication and effort calibration. The model uses targeted reinforcement learning with textual feedback to correct specific mistakes during training, helping it avoid issues like invalid tool calls or poor coding behavior. Composer 2.5 was also trained using significantly more synthetic coding tasks, enabling it to handle increasingly difficult programming challenges and real-world development scenarios.Starting Price: $0.50/M input -
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ERNIE 5.1
Baidu
ERNIE 5.1 is Baidu’s latest large language model designed to deliver advanced reasoning, agentic AI capabilities, creative writing, and world knowledge performance while operating with significantly improved efficiency. The model builds on the foundation of ERNIE 5.0 while reducing total parameters and training costs, allowing it to achieve flagship-level intelligence at a fraction of the computational expense of comparable models. ERNIE 5.1 performs strongly across international benchmarks for reasoning, search, knowledge, and agentic tasks, ranking among the top global AI models and leading among Chinese-developed models on multiple leaderboards. The platform introduces a new fully asynchronous reinforcement learning infrastructure that improves training efficiency, scalability, and stability for complex long-horizon AI tasks. ERNIE 5.1 also features advanced creative writing capabilities. -
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GLM-5.2
Zhipu AI
GLM-5.2 is an advanced AI foundation model designed to support complex reasoning, coding, and long-range agentic tasks. It helps developers, teams, and organizations build intelligent systems that can understand instructions, solve technical problems, and assist with demanding workflows. The model is especially useful for software engineering, automation, research, and productivity-focused applications. GLM-5.2 is built to handle large amounts of context, making it suitable for projects that require deeper understanding across extended conversations, documents, or codebases. Its mixture-of-experts design helps balance strong performance with more efficient model operation. GLM-5.2 gives businesses and developers a powerful AI tool for creating smarter applications, improving technical workflows, and supporting advanced digital experiences.Starting Price: Free -
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GPT-5.5
OpenAI
GPT-5.5 is an advanced AI model designed to handle complex, real-world tasks with greater autonomy and efficiency. It quickly understands user intent and can execute multi-step workflows such as coding, research, data analysis, and document creation with minimal guidance. Instead of requiring step-by-step instructions, GPT-5.5 plans tasks, uses tools, evaluates outputs, and continues working until completion. It excels in knowledge work, software development, and analytical problem-solving, helping users move from idea to execution faster. The model is built to operate across tools and environments, making it highly effective for modern digital workflows. With strong reasoning and persistence, GPT-5.5 enables individuals and teams to complete demanding work more efficiently and accurately.Starting Price: $5 per 1M tokens (input) -
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GPT-5.5 Pro
OpenAI
GPT-5.5 Pro is an advanced AI model designed to handle complex, real-world work with greater autonomy and efficiency. It understands user intent quickly and can execute multi-step tasks such as coding, research, data analysis, and document creation with minimal guidance. The model is built to plan, use tools, and refine its outputs until tasks are complete. It excels in knowledge work, software development, and analytical problem-solving. With strong reasoning and persistence, GPT-5.5 Pro can manage long-running workflows across tools and systems. It delivers high-quality results while maintaining speed and efficiency. Overall, it enables individuals and teams to complete demanding tasks faster and more accurately.Starting Price: $30 per 1M tokens (input) -
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GPT-5.6 Luna
OpenAI
GPT-5.6 Luna is the fast and affordable model in OpenAI’s GPT-5.6 series, built to bring strong capability to users and developers who need practical intelligence with lower overhead. In the new GPT-5.6 naming system, the number identifies the model generation, while Sol, Terra, and Luna identify durable capability tiers that can advance on their own cadence, giving people and developers clearer choices across intelligence, speed, and cost. Luna sits alongside Sol, the flagship model, and Terra, the balanced model for everyday work, as part of a family designed for broader access to next-generation AI. During the limited preview, GPT-5.6 models are initially available through the API and Codex to a select group of trusted partners and organizations, with plans for broader availability in ChatGPT, Codex, and the API. OpenAI developed GPT-5.6 Sol, Terra, and Luna with its most robust safeguards to date, with configurations matched to each model’s capabilities.Starting Price: $1 per 1M tokens (input) -
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GPT-5.6 Sol
OpenAI
GPT-5.6 Sol is a next-generation OpenAI model designed for advanced reasoning, coding, agentic workflows, biology analysis, cybersecurity support, and complex knowledge work. It is part of the GPT-5.6 model family alongside Terra and Luna, with Sol positioned as the flagship model for the most demanding tasks. The model introduces a new max reasoning effort for deeper thinking and an ultra mode that uses subagents to accelerate complex work beyond a single-agent approach. GPT-5.6 Sol shows strong performance in command-line coding workflows, long-horizon security tasks, genomics analysis, vulnerability research, debugging, patch development, and defensive testing. OpenAI pairs the model’s stronger capabilities with layered safeguards, real-time misuse classifiers, account-level review, automated red-teaming, and enterprise controls for sensitive workflows. GPT-5.6 Sol helps developers, enterprises, researchers, and security teams complete sophisticated technical work.Starting Price: $5 per 1M tokens (input) -
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GPT-5.6 Terra
OpenAI
GPT-5.6 Terra is a balanced model in the GPT-5.6 series designed for everyday work, coding, agentic workflows, cybersecurity support, biology analysis, and enterprise automation. It sits between GPT-5.6 Sol, the flagship model, and GPT-5.6 Luna, the faster and lower-cost option. Terra is positioned to deliver competitive performance to GPT-5.5 while being significantly cheaper to run. The model supports improved reasoning, coding, tool coordination, long-horizon workflows, and legitimate defensive security work. It is part of a model family built with layered safeguards, including trained refusals, real-time misuse classifiers, account-level review, differentiated access, monitoring, and continued red-team testing. GPT-5.6 Terra helps developers, enterprises, and technical teams access strong AI capabilities with a more practical balance of intelligence, speed, and cost.Starting Price: $2.50 per 1M tokens (input) -
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Gemma 4
Google
Gemma 4 is an AI model introduced by Google and built on the Gemini architecture to deliver improved performance and flexibility. The model is designed to run efficiently on a single GPU or TPU, making it more accessible to developers and researchers. Gemma 4 enhances capabilities in natural language understanding and text generation, supporting a wide range of AI-driven applications. Its architecture allows it to handle complex tasks while maintaining efficient resource usage. Developers can use the model to build applications that rely on advanced language processing and automation. The design emphasizes scalability so that it can support both smaller projects and larger AI systems. By combining efficiency with powerful language capabilities, Gemma 4 helps advance the development of modern AI solutions.Starting Price: Free -
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Grok 4.3
SpaceXAI
Grok 4.3 is the latest iteration of xAI’s Grok model, designed to deliver improved reasoning, real-time information access, and advanced task automation. It builds on earlier Grok 4 models by enhancing performance in complex problem-solving, coding, and analytical workflows. The model is integrated with real-time web and X (formerly Twitter) data, allowing it to provide up-to-date insights and answers. Grok 4.3 supports multimodal capabilities, enabling it to work with text, images, and other data types. It operates within the SuperGrok Heavy tier, offering access to more powerful compute and advanced features. The model is designed to handle long-context tasks and multi-step reasoning with greater accuracy. It also supports tool use and integrations, enabling it to interact with external systems and automate workflows. Overall, Grok 4.3 is positioned as a high-performance AI assistant for real-time, data-driven tasks. -
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Grok 4.5
SpaceXAI
Grok 4.5 is SpaceXAI’s advanced AI model built for coding, agentic tasks, engineering work, and knowledge-intensive productivity. The model is trained on coding, science, engineering, and math data, with reinforcement learning focused on multi-step software engineering and technical workflows. It is designed to handle real-world development tasks such as debugging, Rust and C/C++ work, terminal tasks, long-running agentic rollouts, and end-to-end app creation from a single prompt. Grok 4.5 is also built for fast serving, token efficiency, and lower-cost execution, with pricing based on input and output token usage. Beyond coding, the model supports business productivity tasks in Grok Build, including Excel modeling, PowerPoint diagram creation, Word writing, and research-assisted office workflows. Available through Grok Build, Cursor, and the SpaceXAI API console, Grok 4.5 gives developers and teams a high-performance model for building software, automating work, and more.Starting Price: $2 per million input tokens -
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Grok Build 0.1
SpaceXAI
Grok Build 0.1 is a specialized AI coding model from xAI designed for agentic software engineering workflows and multi-step development tasks. The model is optimized to help coding agents perform actions such as planning, debugging, implementing changes, and iterating on code rather than simply generating one-time code responses. It supports both text and image inputs while producing text-based outputs, making it useful for analyzing code, screenshots, and technical documentation. Grok Build 0.1 includes support for tool use, structured outputs, function calling, and large-context reasoning capabilities. With a context window of up to 256,000 tokens, the model can process large codebases and complex projects within a single workflow. The platform is built for developers and engineering teams seeking faster and more capable AI-assisted software development.Starting Price: $1 per 1M tokens (input) -
38
DeepSeek-V4
DeepSeek
DeepSeek-V4 is a next-generation open-source language model designed for high-performance reasoning, coding, and long-context intelligence. It introduces a powerful architecture with up to one million token context length, enabling seamless handling of large datasets and complex multi-step workflows. The model comes in two variants: DeepSeek-V4-Pro for maximum performance and DeepSeek-V4-Flash for efficiency and speed. DeepSeek-V4-Pro features 1.6 trillion total parameters with 49 billion activated, delivering near state-of-the-art performance comparable to leading closed-source models. It excels in agentic coding, mathematical reasoning, and world knowledge tasks. The model integrates advanced attention mechanisms, including token-wise compression and sparse attention, significantly reducing compute and memory costs. It is also optimized for AI agents, supporting tool use and multi-step workflows.Starting Price: Free -
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DeepSeek-V4-Pro
DeepSeek
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.Starting Price: Free -
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Inkling
Thinking Machines Lab
Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.Starting Price: Free -
41
Gemini 3.5 Flash
Google
Gemini 3.5 Flash is Google’s latest frontier AI model designed to combine advanced intelligence, high-speed performance, and agentic workflow execution for developers, enterprises, and everyday users. Built as part of the Gemini 3.5 family, the model excels at coding, long-horizon reasoning, multimodal understanding, and complex multi-step automation tasks while delivering significantly faster output speeds than many competing frontier models. Gemini 3.5 Flash powers AI agents capable of planning, executing, and managing workflows such as application development, codebase maintenance, data analysis, and financial document preparation through the Antigravity harness. The model also supports rich multimodal experiences by generating interactive graphics, dynamic web interfaces, animations, and advanced visual content. Gemini 3.5 Flash is integrated across Google products including the Gemini app, Google Search AI Mode, Google Antigravity, Google AI Studio, Android Studio, and more.Starting Price: $1.50 per 1M tokens (input) -
42
Gemini 3.5 Pro
Google
Gemini 3.5 Pro is Google’s anticipated next-generation Pro model in the Gemini 3.5 series, designed for advanced reasoning, coding, multimodal understanding, and agentic workflows. It is expected to build on Google’s Gemini 3 family with stronger performance for complex tasks that require planning, context handling, tool use, and deep problem solving. The model is aimed at users who need more power than faster Flash models for demanding development, research, automation, and enterprise AI use cases. Gemini 3.5 Pro is expected to support sophisticated workflows across text, code, files, multimodal inputs, and connected tools. Developers and organizations will likely use it through Google’s AI platforms for building assistants, agents, coding tools, analysis systems, and productivity applications. As an upcoming Pro-tier model, Gemini 3.5 Pro is positioned for high-value workloads where accuracy, reasoning quality, and advanced task execution matter more than maximum speed. -
43
Hy3
Tencent
Hy3 preview is Tencent Hy’s most intelligent model in the Hy series to date, built as a 295B-parameter Mixture-of-Experts model with 21B activated parameters, 3.8B MTP layer parameters, and support for up to a 256K token context window. As the first model trained on Tencent Hy’s rebuilt infrastructure, Hy3 preview is designed to improve real-world usability across complex reasoning, instruction following, context learning, coding, agent capabilities, and overall inference performance. It integrates both fast and slow thinking capabilities, allowing direct responses for simpler tasks and deeper reasoning for complex math, coding, and reasoning work. The model is built around well-rounded capabilities across long-context understanding, instruction following, tool use, and agent workflows, with evaluation focused not only on standard benchmarks but also on authentic business and development scenarios.Starting Price: Free -
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Kimi K2.6
Moonshot AI
Kimi K2.6 is a next-generation agentic AI model developed by Moonshot AI, designed to push forward real-world execution, coding, and multi-step reasoning beyond earlier K2 and K2.5 versions. It builds on a Mixture-of-Experts architecture and the multimodal, agent-first foundation of the Kimi series, combining language understanding, coding, and tool use into a single system capable of planning and executing complex workflows. It introduces deeper reasoning capabilities and significantly improved agent planning, allowing it to break down tasks, coordinate tools, and handle multi-file or multi-step problems with greater accuracy and efficiency. It supports advanced tool calling with high reliability, enabling integration with external systems such as web search or APIs, and includes built-in validation mechanisms to ensure correct execution formats.Starting Price: Free -
45
Kimi K3
Moonshot AI
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.Starting Price: $3 per 1M tokens (input) -
46
LongCat-2.0
LongCat
LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding. -
47
Ling 2.6
Ant Group
Ling 2.6 is a general-purpose large language model series independently developed and open-sourced by Ant Group, built on a Mixture of Experts architecture and designed for inference efficiency, long context modeling, training technology, and AI Agent collaborative reasoning. Ling’s MoE architecture routes each token to activate only the most relevant expert subnetworks, compressing actual computation to a minimal fraction while maintaining large-scale model capacity. The Ling 2.6 series further advances long-sequence modeling, with Ling-2.6-1T supporting up to a 1M native context window and the official API exposing a 256K context window, while Ling-2.6-flash provides a native 256K context window capable of processing approximately 200,000 characters of long-form input. The models are designed for reliable long-range information retrieval, with no noticeable degradation whether information appears at the beginning, middle, or end of the context.Starting Price: $0.0028 per 1M tokens -
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Ling 2.6 Flash
Ant Group
Ling 2.6 Flash is the latest cost-effective model in the Ling series, built on a Mixture of Experts architecture with 104B total parameters and 7.4B activated parameters. It is designed to achieve an optimal balance between inference performance and compute cost, making it suitable for general-purpose scenarios where strong reasoning capability, high throughput, and efficient deployment matter. Ling’s MoE architecture routes each token to activate only the most relevant expert subnetworks, compressing actual computation to a minimal fraction while maintaining large-scale model capacity. Ling 2.6 Flash provides a native 256K context window and can process approximately 200,000 characters of long-form input, with reliable long-range information retrieval whether key information appears at the beginning, middle, or end of the context. Its aggregate benchmark performance is comparable to or exceeds 40B-class Dense models.Starting Price: $0.00037 per 1M tokens -
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MiMo-V2.5-Pro
Xiaomi Technology
Xiaomi MiMo-V2.5-Pro is an advanced open-source AI model designed to handle complex, long-horizon tasks with strong agentic capabilities. It features a Mixture-of-Experts architecture with over one trillion parameters and a large context window of up to one million tokens. The model is built to perform sophisticated reasoning, coding, and problem-solving across extended workflows. It demonstrates high performance on benchmark tests related to software engineering, reasoning, and general intelligence. MiMo-V2.5-Pro can autonomously complete complex projects, such as building full software systems or optimizing engineering designs. It uses hybrid attention mechanisms to balance efficiency and performance across long contexts. The model is also optimized for token efficiency, reducing computational cost while maintaining strong results. By combining scalability, efficiency, and advanced reasoning, MiMo-V2.5-Pro represents a major step forward in open-source AI models. -
50
MiniMax M3
MiniMax
MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.Starting Price: Free