Alternatives to MAI-Code-1-Flash
Compare MAI-Code-1-Flash alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to MAI-Code-1-Flash in 2026. Compare features, ratings, user reviews, pricing, and more from MAI-Code-1-Flash competitors and alternatives in order to make an informed decision for your business.
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1
GitHub Copilot
GitHub
GitHub Copilot is an AI-powered development assistant designed to accelerate software workflows from the editor to the enterprise. It works directly inside popular IDEs, terminals, and GitHub itself to help developers write, understand, and improve code faster. Copilot supports multiple leading large language models, allowing users to optimize for speed, accuracy, or cost. Developers can use Copilot to complete code, explain concepts, propose edits, and validate files in real time. It also enables agent-based workflows where Copilot can autonomously handle issues, write code, and create pull requests. With seamless integration across tools, Copilot keeps developers focused without breaking their flow. GitHub Copilot is built to scale from individual developers to large organizations with enterprise-grade controls.Starting Price: $10 per month -
2
BLACKBOX AI
BLACKBOX AI
BLACKBOX AI is an advanced AI-powered platform designed to accelerate coding, app development, and deep research tasks. It features an AI Coding Agent that supports real-time voice interaction, GPU acceleration, and remote parallel task execution. Users can convert Figma designs into functional code and transform images into web applications with minimal coding effort. The platform enables screen sharing within IDEs like VSCode and offers mobile access to coding agents. BLACKBOX AI also supports integration with GitHub repositories for streamlined remote workflows. Its capabilities extend to website design, app building with PDF context, and image generation and editing.Starting Price: Free -
3
Grok 4.6
SpaceXAI
Grok 4.6 is an xAI model designed for long-running agents, ambitious interactive projects, visual work, coding, research, and knowledge workflows. The model builds on Grok 4.5 with stronger support for multi-step tasks that require sustained reasoning across codebases, information analysis, application development, and work artifact creation. Grok 4.6 can help turn broad product ideas into working first versions by researching domains, structuring applications, implementing core interactions, and refining results through feedback. It is trained across agentic tasks such as knowledge work, general coding, kernel optimization, web development, computer-aided design, and other technical environments. The model is available in Cursor, Grok Build, the xAI API, and partners such as OpenRouter, Vercel, and Cloudflare. Built for developers, builders, and teams working on complex projects, Grok 4.6 helps accelerate coding, agentic workflows, visual applications, and technical execution.Starting Price: $2 per 1M tokens (input) -
4
MiMo-V2.6-Flash
Xiaomi Technology
MiMo-V2.6-Flash is an open-source, natively omnimodal AI model from Xiaomi MiMo designed to balance intelligence, efficiency, and cost. The model supports coding, general agent workflows, visual reasoning, computer use, automation, and multimodal creative tasks. Its capabilities extend beyond software engineering into frontend design, presentation creation, 3D modeling, interactive world generation, video production, and embodied simulation. Xiaomi trained MiMo-V2.6-Flash with large-scale reinforcement learning across coding, general agent, visual, and cybersecurity tasks, completing roughly 750,000 training trajectories. The model is positioned as the more cost-efficient member of the MiMo-V2.6 family while retaining strong performance across software engineering, tool use, automation, and visual coding benchmarks. MiMo-V2.6-Flash is available through MiMo Desktop, AI Studio, MiMo Code, the Xiaomi MiMo API Platform, OpenRouter, and the open-source MiMo-V2.6 release.Starting Price: Free -
5
Gemini 3.6 Flash
Google
Gemini 3.6 Flash is Google’s newest Flash model built for efficient, reliable, production-scale AI agents. The model improves on Gemini 3.5 Flash with stronger coding, knowledge work, multimodal performance, computer use, and agentic workflow execution. Gemini 3.6 Flash is designed to use fewer output tokens, take fewer reasoning steps, reduce unnecessary tool calls, and lower the cost of complex AI tasks. It supports document parsing, chart analysis, data analysis, report drafting, code migrations, visual understanding, and multi-agent orchestration. The model is available through the Gemini API, Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise Agent Platform, Gemini Enterprise app, and the Gemini app. Built for developers and enterprises, Gemini 3.6 Flash helps teams build faster, lower-cost, and more capable AI agents across coding, analysis, productivity, and multimodal workloads.Starting Price: $1.50 per 1M tokens (input) -
6
Gemini 3.7 Flash
Google
Gemini 3.7 Flash is Google’s most intelligent workhorse model yet for coding and agents, delivering substantial improvements across software engineering, knowledge work, web development, and complex business workflows. It shows stronger performance in debugging and issue resolution, higher first-pass code accuracy, and improved generation of production-ready code. For web development, the model creates more functional layouts and feature-complete applications in fewer prompts, with strong design adherence when working from screenshots, images, or complete design systems. In knowledge-dense fields such as finance, law, and biosciences, it provides improved reasoning, accuracy, and complex-document understanding. Gemini 3.7 Flash also performs more effectively on real-world workflow automation and multimodal tasks, supporting use cases ranging from interactive web experiences and data stories to robotics and dynamically generated 3D content.Starting Price: $0.75 per 1M tokens (input) -
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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. -
8
GLM-5.3
Z.ai
GLM-5.3 is Z.ai’s frontier coding model designed for complex software engineering, long-horizon agent tasks, and advanced post-training research. The model uses the same base model as GLM-5.2, with improvements coming from scaled post-training across more environments, more diverse tasks, and larger compute investment. GLM-5.3 delivers stronger coding performance, better task ownership, improved benchmark results, and greater efficiency across realistic development workflows. It is built to handle complex coding tasks, production-style engineering work, research environments, automation tasks, and agentic workflows that require multi-step execution. The model also shows emergent cyber capabilities in vulnerability discovery and exploitation-chain reasoning, with safety evaluation and hardening planned before open-weight release.Starting Price: Free -
9
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 -
10
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) -
11
Kimi K2.7 Code
Moonshot AI
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.Starting Price: Free -
12
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) -
13
Muse Spark 1.2
Meta
Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.Starting Price: $1.25 per 1M tokens (input) -
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Gemini 3.5 Flash Cyber
Google
Gemini 3.5 Flash Cyber is a specialized cyber-focused model built on Gemini 3.5 Flash and fine-tuned to find, validate, and fix cybersecurity vulnerabilities efficiently at scale. It is designed for defensive security workflows where organizations need to identify critical weaknesses faster and generate reliable patches before those issues can be exploited. Flash’s combination of performance and efficiency makes it a strong foundation for scanning code, reasoning about security flaws, validating whether findings are real, and proposing targeted remediations across large software environments. Within CodeMender, multiple Gemini 3.5 Flash Cyber agents work together and combine their findings into a single report, helping the system investigate vulnerabilities from different angles and improve the quality of the final result. This coordinated agent setup delivers competitive frontier performance on CyberGym, a benchmark for evaluating cybersecurity capabilities. -
15
GLM-5.3-Flash
Z.ai
GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.Starting Price: $0.15 per 1M tokens (input) -
16
Claude Haiku 4.5
Anthropic
Anthropic has launched Claude Haiku 4.5, its latest small-language model designed to deliver near-frontier performance at significantly lower cost. The model provides similar coding and reasoning quality as the company’s mid-tier Sonnet 4, yet it runs at roughly one-third of the cost and more than twice the speed. In benchmarks cited by Anthropic, Haiku 4.5 meets or exceeds Sonnet 4’s performance in key tasks such as code generation and multi-step “computer use” workflows. It is optimized for real-time, low-latency scenarios such as chat assistants, customer service agents, and pair-programming support. Haiku 4.5 is made available via the Claude API under the identifier “claude-haiku-4-5” and supports large-scale deployments where cost, responsiveness, and near-frontier intelligence matter. Claude Haiku 4.5 is available now on Claude Code and our apps. Its efficiency means you can accomplish more within your usage limits while maintaining premium model performance.Starting Price: $1 per million input tokens -
17
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. -
18
Grok Build
SpaceXAI
Grok Build is an AI-powered command-line development environment designed to help developers build, manage, and automate software projects more efficiently. The platform provides a fast and flicker-free CLI experience that supports planning, coding, reviewing, and coordinating tasks across multiple AI-powered agents. Grok Build can adapt to different workflows and user preferences through customizable skills and interface enhancements. Developers can use the platform to architect complex projects with plan viewers, subagents, and parallel task execution capabilities. The system also includes marketplaces that allow teams to share workflows, capabilities, and productivity tools across projects. Grok Build supports interactive coding assistance, interface refinement suggestions, and contextual prompts that help streamline development processes.Starting Price: Free -
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Grok Code Fast 1
SpaceXAI
Grok Code Fast 1 is a high-speed, economical reasoning model designed specifically for agentic coding workflows. Unlike traditional models that can feel slow in tool-based loops, it delivers near-instant responses, excelling in everyday software development tasks. Built from scratch with a programming-rich corpus and refined on real-world pull requests, it supports languages like TypeScript, Python, Java, Rust, C++, and Go. Developers can use it for everything from zero-to-one project building to precise bug fixes and codebase Q&A. With optimized inference and caching techniques, it achieves impressive responsiveness and a 90%+ cache hit rate when integrated with partners like GitHub Copilot, Cursor, and Cline. Offered at just $0.20 per million input tokens and $1.50 per million output tokens, Grok Code Fast 1 strikes a strong balance between speed, performance, and affordability.Starting Price: $0.20 per million input tokens -
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Gemini 3.1 Flash-Lite
Google
Gemini 3.1 Flash-Lite is Google’s fastest and most cost-efficient model in the Gemini 3 series, designed for high-volume developer workloads. It delivers strong performance at scale while maintaining affordability, with pricing set at $0.25 per million input tokens and $1.50 per million output tokens. The model significantly improves speed, offering a 2.5x faster time to first answer token and a 45% increase in output speed compared to Gemini 2.5 Flash. Despite its lower cost tier, it achieves high benchmark results, including an Elo score of 1432 and strong performance across reasoning and multimodal evaluations. Gemini 3.1 Flash-Lite supports adaptive “thinking levels,” allowing developers to control how much reasoning power is used for different tasks. It is suitable for large-scale applications such as translation, content moderation, user interface generation, and simulation building. -
21
Laguna XS 2.1
Poolside
Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets. -
22
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 -
23
MAI-Code-1.1-Flash
Microsoft AI
MAI-Code-1.1-Flash is a small, efficient coding model designed to help engineering teams write better code faster. Now in production in GitHub Copilot and built into VS Code, it focuses on real-world developer workflows, with particular improvements for command-line tasks and .NET development based on developer feedback. Compared with the version introduced at Microsoft Build in June, the model produces higher-quality code while using fewer tokens and streaming responses faster. Microsoft reports a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks. Production results also showed a 4% increase in code survival and a 9% increase in return visits. In GitHub Copilot, tokens stream 25% faster and the model uses 25% fewer tokens to complete a task, aiming to deliver faster answers, less waiting, and more useful work from every token. Its gains come from improved training and serving efficiency, with optimization centered on real-world use. -
24
MAI-Thinking-1
Microsoft AI
MAI-Thinking-1 is Microsoft AI’s reasoning model, built for complex problems that matter most, with competitive reasoning and strong software engineering performance in its weight class. It is a 35B-active, approximately 1T-total-parameter sparse Mixture of Experts model, giving it a smaller inference footprint than much larger models while still matching leading models on key software engineering benchmarks. Microsoft trained MAI-Thinking-1 from the ground up on enterprise-grade, clean, commercially licensed data, without distillation from third-party models, so its capabilities are learned rather than inherited. The model is part of Microsoft AI’s Hill-Climbing Machine, a co-designed development pipeline built to make every component of model development continually and reliably improve over time. MAI-Thinking-1 is designed for agentic coding environments where models must read code, edit files, run tests, observe failures, and recover from intermediate mistakes. -
25
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 1.3
Meta
Muse Spark 1.3 is an AI model with improved performance across agentic and coding tasks, designed to be smarter and more practical for real-world work. It sustains longer-horizon tasks by collaborating with users and managing multiple workflows in a single, long thread. Given an open-ended objective, it uses tools to build context across messy or conflicting sources, correct gaps in its plan, track what it has learned, and produce a final deliverable. It asks clarifying questions when prompts are ambiguous, requests help when stuck, and confirms before taking consequential actions. The model follows complex, long-form instructions more reliably, preserving detailed requirements across multi-step tasks without dropping constraints or drifting from the requested workflow. Improved multitasking allows it to map incoming prompts to the correct task even when users interrupt or redirect previous requests.Starting Price: $1.25 per 1M tokens (input) -
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Microsoft Frontier Tuning
Microsoft AI
Microsoft Frontier Tuning lets organizations customize one or more of Microsoft’s top MAI models around their unique business needs, trained safely within their own secure environment instead of relying on a generic AI model. The process starts by defining the task and what success looks like, then feeding in data, workflows, and expertise from Microsoft 365 and beyond. Performance is improved through training and iterative optimization, then deployed in Microsoft Foundry or Copilot, where the model can continue improving from real usage. Microsoft Frontier Tuning is designed to create models that know the organization’s work, terms, context, processes, and expertise while keeping data private and secure inside the customer’s environment. It gives teams more control over the model, avoids vendor lock-in, and helps them squeeze more value from every dollar spent by delivering frontier performance with superior token efficiency. -
28
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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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. -
30
GPT-5.1-Codex-Max
OpenAI
GPT-5.1-Codex-Max is the high-capability variant of the GPT-5.1-Codex series designed specifically for software engineering and agentic code workflows. It builds on the base GPT-5.1 architecture with a focus on long-horizon tasks such as full project generation, large-scale refactoring, and autonomous multi-step bug and test management. It introduces adaptive reasoning, meaning the system dynamically allocates more compute for complex problems and less for simpler ones, to improve efficiency and output quality. It also supports tool use (IDE-integrated workflows, version control, CI/CD pipelines) and offers higher fidelity in code review, debugging, and agentic behavior than general-purpose models. Alongside Max, there are lighter variants such as Codex-Mini for cost-sensitive or scale use-cases. The GPT-5.1-Codex family is available in developer previews, including via integrations like GitHub Copilot. -
31
StarCoder
BigCode
StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.Starting Price: Free -
32
GPT‑5-Codex
OpenAI
GPT-5-Codex is a version of GPT-5 further optimized for agentic coding within Codex, focusing on real-world software engineering tasks (building full projects from scratch, adding features & tests, debugging, large-scale refactors, and code reviews). Codex now moves faster, is more reliable, and works better in real-time across your development environments, whether in terminal/CLI, IDE extension, via the web, in GitHub, or even on mobile. GPT-5-Codex is the default model for cloud tasks and code review; developers can also opt to use it locally via Codex CLI or the IDE extension. It dynamically adjusts how much “reasoning time” it spends depending on task complexity; small, well-defined tasks are fast and snappy; more complex ones (refactors, large feature work) get more sustained effort. Code review is stronger; it catches critical bugs before shipping. -
33
Gemini 3 Flash
Google
Gemini 3 Flash is Google’s latest AI model built to deliver frontier intelligence with exceptional speed and efficiency. It combines Pro-level reasoning with Flash-level latency, making advanced AI more accessible and affordable. The model excels in complex reasoning, multimodal understanding, and agentic workflows while using fewer tokens for everyday tasks. Gemini 3 Flash is designed to scale across consumer apps, developer tools, and enterprise platforms. It supports rapid coding, data analysis, video understanding, and interactive application development. By balancing performance, cost, and speed, Gemini 3 Flash redefines what fast AI can achieve. -
34
MAI-Cyber-1-Flash
Microsoft
MAI-Cyber-1-Flash is Microsoft AI’s compact, code-heavy security model for finding vulnerabilities in complex codebases. Derived from the MAI-Thinking-1 lineage and built from scratch on high-quality data, it is deeply integrated into MDASH, Microsoft’s multi-agent vulnerability identification and remediation harness. MDASH uses more than 100 expert-tuned agents and multiple leading models to find, validate, and remediate software vulnerabilities, while MAI-Cyber-1-Flash efficiently handles up to 90% of tasks. Exceptionally difficult cases can be routed to larger models such as GPT-5.4, creating a well-tuned multi-model system that selects the right model for each task. Together, MDASH and MAI-Cyber-1-Flash achieved 96% on CyberGym, outperforming Mythos, Gemini, and GPT-based alternatives in reasoning over large codebases to identify vulnerabilities. -
35
Claude Opus 4.1
Anthropic
Claude Opus 4.1 is an incremental upgrade to Claude Opus 4 that boosts coding, agentic reasoning, and data-analysis performance without changing deployment complexity. It raises coding accuracy to 74.5 percent on SWE-bench Verified and sharpens in-depth research and detailed tracking for agentic search tasks. GitHub reports notable gains in multi-file code refactoring, while Rakuten Group highlights its precision in pinpointing exact corrections within large codebases without introducing bugs. Independent benchmarks show about a one-standard-deviation improvement on junior developer tests compared to Opus 4, mirroring major leaps seen in prior Claude releases. -
36
Gemini 3.5 Flash-Lite
Google
Gemini 3.5 Flash-Lite is Google’s fastest model in the Gemini 3.5 series, designed for low-latency tasks and high-throughput developer workflows such as agentic search, document processing, coding, and large-scale data analysis. It delivers 350 output tokens per second and significantly improves on previous Flash-Lite generations in both quality and agentic performance. Developers can configure its thinking level to match the workload: minimal or low thinking supports fast execution for high-volume tasks, while higher thinking levels enable more complex, multi-step subagent workflows. Built-in computer-use capabilities allow the model to interact reliably with digital environments across supported surfaces. Gemini 3.5 Flash-Lite also advances coding, long-context understanding, and real-world task execution, outperforming Gemini 3.1 Flash-Lite across key evaluations and even surpassing Gemini 3 Flash on several agentic and software-engineering benchmarks.Starting Price: $0.30 per 1M input tokens -
37
Visual Studio
Microsoft
Microsoft Visual Studio is the industry-leading integrated development environment (IDE) for building modern applications across desktop, mobile, cloud, and web. It empowers developers to write, refactor, debug, test, and deploy software faster with intelligent assistance powered by GitHub Copilot and AI-driven workflows. With Agent Mode, developers can automate repetitive coding tasks, optimize performance, and receive contextual help directly in the IDE. The suite includes Visual Studio 2022, the comprehensive IDE for .NET and C++ development on Windows, and Visual Studio Code, the lightweight, cross-platform editor supporting JavaScript, Python, and dozens of other languages. Visual Studio integrates seamlessly with Azure, GitHub, and CI/CD pipelines, enabling teams to collaborate and ship code efficiently. Trusted by millions worldwide, Visual Studio provides the tools and intelligence developers need to build reliable, scalable, and secure applications from concept to release.Starting Price: $45/user/month -
38
GPT-5.1 Instant
OpenAI
GPT-5.1 Instant is a high-performance AI model designed for everyday users that combines speed, responsiveness, and improved conversational warmth. The model uses adaptive reasoning to instantly select how much computation is required for a task, allowing it to deliver fast answers without sacrificing understanding. It emphasizes stronger instruction-following, enabling users to give precise directions and expect consistent compliance. The model also introduces richer personality controls so chat tone can be set to Default, Friendly, Professional, Candid, Quirky, or Efficient, with experiments in deeper voice modulation. Its core value is to make interactions feel more natural and less robotic while preserving high intelligence across writing, coding, analysis, and reasoning. GPT-5.1 Instant routes user requests automatically from the base interface, with the system choosing whether this variant or the deeper “Thinking” model is applied. -
39
SWE-1.5
Cognition
SWE-1.5 is the latest agent-model release by Cognition, purpose-built for software engineering and characterized by a “frontier-size” architecture comprising hundreds of billions of parameters and optimized end-to-end (model, inference engine, and agent harness) for both speed and intelligence. It achieves near-state-of-the-art coding performance and sets a new benchmark in latency, delivering inference speeds up to 950 tokens/second, roughly six times faster than its predecessor Haiku 4.5 and thirteen times faster than Sonnet 4.5. The model was trained using extensive reinforcement learning in realistic coding-agent environments with multi-turn workflows, unit tests, quality rubrics, and browser-based agentic execution; it also benefits from tightly integrated software tooling and high-throughput hardware (including thousands of GB200 NVL72 chips and a custom hypervisor infrastructure). -
40
Ling 3.0 Flash
Ant Group
Ling 3.0 Flash is a next-generation efficient language model designed for long-horizon agent workflows, combining fast response, low activation, and stable tool use. It uses a Mixture-of-Experts architecture with 124 billion total parameters and 5.1 billion activated parameters per token, providing capability while keeping inference efficient. The model supports a native 256K context window that can be extended up to 1 million tokens, with reliable retrieval across information placed at the beginning, middle, or end of long contexts. Compared with the previous Flash model, Ling 3.0 Flash improves stability on extended tasks, tool-calling accuracy, instruction following, compatibility with agent harnesses, and coding performance. Its optimized spatial understanding can construct physical scene grids and reason about relative positions, while hybrid reasoning improves success rates across tasks of varying difficulty. -
41
Grok 4.1 Fast
SpaceXAI
Grok 4.1 Fast is an xAI model designed to deliver advanced tool-calling capabilities with a massive 2-million-token context window. It excels at complex real-world tasks such as customer support, finance, troubleshooting, and dynamic agent workflows. The model pairs seamlessly with the new Agent Tools API, which enables real-time web search, X search, file retrieval, and secure code execution. This combination gives developers the power to build fully autonomous, production-grade agents that plan, reason, and use tools effectively. Grok 4.1 Fast is trained with long-horizon reinforcement learning, ensuring stable multi-turn accuracy even across extremely long prompts. With its speed, cost-efficiency, and high benchmark scores, it sets a new standard for scalable enterprise-grade AI agents. -
42
Qwen3-Coder
Qwen
Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning, scaling test‑case generation for diverse coding challenges, and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and environment variables.Starting Price: Free -
43
Gemini 3.8 Flash
Google
Gemini 3.8 Flash is Google’s most intelligent Flash workhorse model, delivering significant improvements over 3.7 Flash across software engineering, agentic tasks, and critical multi-step reasoning in specialized domains. Built for long-horizon coding and autonomous agents, it can solve complex engineering problems end to end and delivers the dependability required for critical enterprise autonomy across specialized knowledge domains. The model shows stronger performance in quantitative and professional fields that require advanced analysis and reporting, as well as multi-step reasoning across STEM, humanities, and professional subjects. Its gains stem from a core design choice: Gemini 3.8 Flash works harder on complex tasks, executing additional reasoning steps and calling tools iteratively to maximize performance. At higher effort levels, it may use more tokens to pursue stronger results, while developers can select lower effort levels. -
44
Gemini 4
Google
Gemini 4 is Google’s next-generation Gemini model family currently in development after the release of Gemini 3.6 Flash and Gemini 3.5 Flash-Lite. Google has confirmed that pre-training for Gemini 4 has begun, positioning it as the company’s most ambitious model training effort yet. The model is expected to advance Google’s frontier AI work across reasoning, coding, multimodal understanding, agentic workflows, and enterprise AI use cases. Because Gemini 4 has not been publicly released yet, official pricing, model cards, benchmarks, API details, and availability have not been published. Gemini 4 follows Google’s broader Gemini strategy of building models for developers, enterprises, consumer apps, and AI-powered products across Google’s ecosystem. Built for the next stage of AI agents and intelligent applications, Gemini 4 is likely to become a major foundation for future Google AI products once it becomes available. -
45
GitHub Copilot CLI
GitHub
GitHub Copilot CLI brings the core capabilities of the Copilot coding assistant into your terminal, enabling you to write, debug, refactor, and understand code via natural language directly in the command line. It works locally and in sync with your GitHub workflow, granting the ability to access repositories, issues, and pull requests through conversational commands while staying authenticated with your GitHub account. The tool operates as an agent in your terminal; you can ask it to autonomously create or modify files, execute commands, implement new features, fix bugs, prototype, and adjust codebases based on your specifications. Deep GitHub integration ensures context awareness (e.g., code history, branches, project layout), and the CLI experience is optimized to reduce context switching between your editor and terminal. The system supports iterative collaboration, allowing you to fine-tune or reissue commands as the project evolves.Starting Price: Free -
46
DeepSeek -V4.1-Flash
DeepSeek
DeepSeek-V4.1-Flash is a fast, versatile AI model designed for demanding coding, agentic, creative, and spatial reasoning workloads. Building on DeepSeek-V4-Flash, it emphasizes high-speed generation while maintaining strong performance on complex tasks, reaching more than 400 tokens per second and peaking at around 427 tokens per second in reported tests. The model can tackle advanced programming challenges, generate interactive 3D environments, create voxel-based designs, and reason about spatially complex scenes and simulations. Demonstrations include Minecraft-style worlds, classical Chinese gardens, racing environments, dungeon navigation, exploded camera views, and other applications requiring both code generation and an understanding of spatial relationships. Its capabilities make it suitable for rapid prototyping, game development, 3D workflows, architecture, research, and other technical or creative projects where iteration speed matters. -
47
North Mini Code
Cohere
North Mini Code is Cohere’s first agentic coding model for developers and the inaugural member of its next generation of powerful models. Small, efficient, and open-source, it is built for the sovereign developer ecosystem and designed to deliver strong software development performance without requiring extensive hardware. North Mini Code is a mixture-of-experts model with 30B total parameters and 3B active parameters, giving developers access to agentic coding capabilities in a compact and efficient form. The model is optimized for code generation, agentic software engineering, and terminal tasks, with a 256K total context length and up to 64K maximum generation. It is built for real-world developer workflows, including understanding and orchestrating sub-agents, mapping system architecture, running code reviews, and supporting coding agents that need to reason through complex software tasks. -
48
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 -
49
GPT-5.1-Codex
OpenAI
GPT-5.1-Codex is a specialized version of the GPT-5.1 model built for software engineering and agentic coding workflows. It is optimized for both interactive development sessions and long-horizon, autonomous execution of complex engineering tasks, such as building projects from scratch, developing features, debugging, performing large-scale refactoring, and code review. It supports tool-use, integrates naturally with developer environments, and adapts reasoning effort dynamically, moving quickly on simple tasks while spending more time on deep ones. The model is described as producing cleaner and higher-quality code outputs compared to general models, with closer adherence to developer instructions and fewer hallucinations. GPT-5.1-Codex is available via the Responses API route (rather than a standard chat API) and comes in variants including “mini” for cost-sensitive usage and “max” for the highest capability.Starting Price: $1.25 per input -
50
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
