Best LLMs for Coding
Models with strong code-generation capabilities for autocompletion, refactoring, and agentic coding.
179 models
Gemini 4 Argon
Google's top-tier model anchoring the Gemini 4 generation, larger than the previous Pro line and built for complex workloads, coding, and cybersecurity. Can locate, validate, and patch critical software vulnerabilities autonomously and raises the output limit to 1M tokens. At launch, access is limited to selected cybersecurity organizations through Google's Fairwind Program, with paid API customers and Google AI Ultra subscribers to follow; no public availability date has been announced.
GPT-6.1 Sol
OpenAI's upgrade to GPT-6 Sol, delivering near-Astra performance for complex work at a lower cost. Suited for agentic coding, computer use, and document-heavy professional work, with multimodal input, function calling, reasoning effort controls, a 1.05M-token context window, up to 128K output tokens, and cheaper cached input than GPT-6 Sol.
Claude Sonnet 5.5
Anthropic's Sonnet-class model offering the best combination of speed and intelligence, succeeding Claude Sonnet 5 as a direct upgrade. Especially strong at building features, fixing bugs, and well-scoped everyday coding and knowledge work, with adaptive thinking, a 1M-token context window, and up to 128K output tokens.
Ember-1
A specialized reasoning model from Fireworks Research, built on Kimi K3 and designed to make every token go further by producing shorter reasoning traces. Supports image input, tool use, and a 1M-token context window.
Qwen 3.8 Max Prime
A higher-throughput variant of Qwen 3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. Accepts text, image, and video input with reasoning, tool use, and a 1M-token context window.
GLM-5.3-Prime
The high-speed variant of Z.ai's GLM-5.3, inheriting its full capabilities while delivering 1.5-2x the output throughput through inference acceleration. Text input and output with reasoning, tool use, and a 1M-token context window.
Solar Mini 4
Upstage's compact, cost-efficient language model, a 35B-parameter mixture-of-experts with 3B active parameters and a 524K-token context window. Built for agentic use cases where response speed and cost matter, with reasoning and tool use.
GPT-6 Sol
The cost-efficient high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. Suited for demanding professional work, agentic coding, and document-heavy tasks, with multimodal input, function calling, reasoning effort controls, a 1.05M-token context window, and up to 128K output tokens.
GPT-6 Luna
The fast, most efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol for focused, high-volume tasks. Suited for latency-sensitive workloads such as chat, classification, extraction, and lightweight agentic steps, with multimodal input, function calling, reasoning controls, a 1.05M-token context window, and up to 128K output tokens.
MiMo-V2.6-Flash
Xiaomi's open-source mixture-of-experts foundation model with 309B total parameters and 15B activated per token, using a hybrid attention mechanism for efficient long-context inference. Multimodal across text, image, video, and audio input, with reasoning, tool use, and a 1M-token context window.
MiMo-V2.6-Pro
Xiaomi's flagship foundation model, built at a scale of over 1T parameters for the most demanding workloads. Natively multimodal across text, image, video, and audio input, with reasoning, tool use, and a 1M-token context window; weights are published on Hugging Face.
Claude Opus 5.5
Anthropic's Opus-class model for long-running agentic coding and knowledge work, succeeding Claude Opus 5 at a lower price. Particularly strong at multi-step changes in large codebases and sustained autonomous tasks, with always-on adaptive thinking steered by an effort parameter, a 1M-token context window, and up to 128K output tokens. Anthropic's recommended starting point for most workloads.
Qwen 3.8 Omni Flash
Alibaba's omni-modal reasoning model and the first Qwen model built around agentic capabilities with native audio-video understanding. Suited for audio-video analysis and summarization and multimodal agents, with tool use and a 1M-token context window.
Grok 4.7
xAI's flagship model for coding, agentic tasks, and knowledge work, succeeding Grok 4.6. Particularly strong at long-running software engineering tasks and verifying its own work, with image and file input, reasoning, tool use, a 500K-token context window, and an OpenAI-compatible API.
Ternary Bonsai 2 27B
PrismML's open-weight 27B-parameter reasoning model derived from Qwen 3.8 27B and shrunk with ternary compression. Supports coding, mathematics, tool calling, and image understanding with a 262K-token context window.
Pareto
A multimodal composite model from Unbiased built for research, coding, and agentic workflows, aiming at frontier-level performance across a broad range of general-purpose tasks. Supports image input, tool use, and a 262K-token context window.
GLM-5.3-FlashX
The high-speed variant of Z.ai's GLM-5.3-Flash, a natively multimodal model delivering inference speeds of up to 200 tokens per second. Built on the same hybrid sparse and linear attention architecture, with image and video input, reasoning, tool use, and a 1M-token context window.
Sakana Fugu Max
The cost-performance model in Sakana AI's Fugu family, a learned multi-agent orchestration system in which a language model routes and coordinates work across a pool of models. Supports image and file input, reasoning, tool use, and a 1M-token context window.
Sakana Fugu Ultra v2
The higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system, a language model trained to route and coordinate work across a pool of models. Supports image and file input, reasoning, tool use, and a 1M-token context window.
Ling 3.0 Flash VL
InclusionAI's open-weight vision-language model built on Ling 3.0 Flash (124B total, 5.5B active MoE), strengthening language capabilities while adding native visual perception and visual reasoning over images and video. 262K-token context window.
DeepSeek V4.1 Flash
DeepSeek's open-weight sparse mixture-of-experts model and the first built on the company's Causal Encoder-Decoder (CED) architecture, activating 8B parameters on input and 16B on output. Supports image input, reasoning, and tool use with a 1M-token context window at very low per-token cost.
Nex-N2.5-Mini
The smaller member of Nex AGI's open-weight Nex-N2.5 agentic family, aimed at agentic coding within a visual feedback loop at lower cost. Supports image input, reasoning, and a 262K-token context window.
Nex-N2.5-Pro
Nex AGI's open-weight agentic model built to turn goals into working, verified outcomes. Its core strength is agentic coding within a visual feedback loop, exploring codebases and implementing multi-file changes, with image input, tool use, and a 262K-token context window.
Mercury 2.5
Inception's latest diffusion LLM (dLLM), a fast reasoning model that produces and refines multiple tokens in parallel instead of generating them sequentially. Supports reasoning, tool use, and a 260K-token context window.
GPT-6 Astra
OpenAI's most capable frontier model for the hardest end-to-end work across complex reasoning, coding, computer use, browsing, research, scientific analysis, cybersecurity defense, and professional document creation. Supports long-context multimodal input, structured outputs, function calling, reasoning effort controls, MCP, skills, computer use, hosted shell, code interpreter, web search, file search, and apply-patch workflows through the Responses API.
Muse Spark 1.3
Meta's multimodal reasoning model for long-running agentic, multi-agent, and coding workflows. Designed to keep track of information across extended tasks, it accepts text, images, video, and documents and offers a 1M-token context window with tool use.
Gemini 3.8 Flash
Google's most intelligent Flash model, with significant gains over Gemini 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning. Engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows, while natively multimodal across text, image, audio, and video with a 1M-token context window.
Claude Fable 5.1
Anthropic's latest generally available Fable model for demanding reasoning, long-horizon agentic work, ambitious coding projects, multistep research, and document-heavy analysis. It extends Claude Fable 5 with stronger agentic coding and knowledge work, adaptive thinking, more precise safeguards, content provenance, lower cache-read pricing, and 1M-token context support.
Hy4 Preview
Tencent Hunyuan's fourth-generation open-weight flagship preview model, a Mixture-of-Experts system with 770B total parameters and 49B active parameters per token. It targets real-world productivity tasks across coding, office work, scientific research, and multi-step tool workflows, supports function calling, reasoning modes, and a context window of about one million tokens.
Qwen3.8-Flash-Next
Alibaba's open-weight multimodal preview of the architecture being developed for Qwen4. The sparse mixture-of-experts model has a 125B-parameter backbone with 6B parameters active per token plus 51B parameters of N-gram embeddings. It combines Gated DeltaNet with Qwen Sparse Attention, supports 262,144 tokens natively and up to one million tokens with YaRN, and targets efficient coding, office, visual, and long-context workloads.
Qwen 3.8 Flash
Alibaba's hosted Qwen 3.8 Flash model, a low-cost multimodal reasoning model based on the Qwen3.8-Flash-Next architecture. It supports text, image, and video inputs, one million tokens of context, function calling, structured output, built-in tools, and thinking mode for high-throughput coding, agentic workflows, document analysis, and visual understanding.
GLM-5.3-Flash
Z.ai's first natively multimodal model in the GLM-5 family, designed for efficient coding, agentic work, visual reasoning, and long-context tasks. It uses a 320B-parameter sparse mixture-of-experts architecture with 18B active parameters, hybrid linear and sparse attention, and a context window of up to one million tokens. The model weights are released under the MIT license for local deployment with frameworks including SGLang and vLLM.
Granite 4.2 3B
IBM's compact dense decoder-only reasoning language model from the Granite 4.2 family. Supports native thinking modes, reasoning-augmented tool calling, code generation, multilingual dialog across 12 tested languages, and a 128K-token context window. Released under Apache 2.0.
Granite 4.2 8B
IBM's balanced dense decoder-only reasoning language model from the Granite 4.2 family. Designed for enterprise agentic workflows, coding, tool calling, instruction following, multilingual dialog across 12 tested languages, and native thinking modes with a 128K-token context window. Released under Apache 2.0.
Granite 4.2 30B
IBM's flagship dense decoder-only reasoning language model from the Granite 4.2 family. Built for complex reasoning, coding, agentic tool use, instruction following, and multilingual dialog across 12 tested languages, with native 128K context and long-context extension up to 512K tokens. Released under Apache 2.0.
DeepSeek V4 Flash Vision Exp
DeepSeek's experimental multimodal V4 Flash model that adds image input while matching DeepSeek V4 Flash text capabilities for agents, reasoning, world knowledge, tool use, and Responses API workflows. It supports mixed text and image requests, one million tokens of context, and the same token-based pricing as DeepSeek V4 Flash.
GLM-5.3
Z.ai's (formerly Zhipu AI) coding and agentic upgrade built on the same base model as GLM-5.2, with all gains coming from extended post-training (more executable environments, longer-horizon tasks, more RL compute). Claims the strongest open-weight coding performance to date, with large jumps on Terminal-Bench 3.0, DeepSWE v1.1, SWE-Marathon v1.1, and cybersecurity benchmarks like CyberGym and ExploitBench. Supports low, high, and max reasoning effort levels with a 1M-token context route.
Qwen 3.8 27B
Alibaba's open-weight 27B dense vision-language model in the Qwen 3.8 family. It builds on the Qwen 3.6 27B line with stronger coding and office productivity capabilities across text and visual modalities, supports one million tokens of context through hosted APIs, and is suited to multimodal assistants, document work, coding, and long-running agent tasks.
Dots3-Note Preview
Dots Studio's open-weight mixture-of-experts model with 16B active parameters out of 280B total, the lightest model in the Dots 3 family. Supports image input, reasoning, tool use, and a 512K-token context window.
Gemini 3.7 Flash
Google DeepMind's most intelligent workhorse Flash model yet, released three weeks after Gemini 3.6 Flash with substantial gains in coding, agentic tool use, and knowledge work. Delivers stronger first-pass code accuracy, more functional web app generation, and improved multi-step planning, while natively multimodal across text, image, audio, and video with a 1M-token context window and configurable thinking levels.
Qwen 3.8 2.4T-A95B
Alibaba's open-weight Max-class Qwen 3.8 model, a sparse Mixture-of-Experts system with 2.4T total parameters and about 95B active parameters per token. The text model targets coding, research, complex reasoning, and long-horizon agentic workflows with a native 262K context window that can be extended to about one million tokens and is served by hosted providers with 1M context.
Seed 2.0 Code
ByteDance Seed's model optimized for agentic coding. Suited for frontend development, multilingual programming tasks, and coding-agent workflows, with image and video input, reasoning, tool use, and a 262K-token context window.
Seed 2.1 Turbo
ByteDance Seed's multimodal model for coding and long-horizon agent workflows. Suited for end-to-end software delivery, multi-step task execution, and understanding visual and video content, with reasoning, tool use, and a 262K-token context window.
Nemotron 3.5 Lightning
NVIDIA's open mixture-of-experts model with 3B active parameters out of 30B total, suited for high-throughput agentic workloads and specialized tasks. Supports reasoning and tool use with a 262K-token context window.
Sakana Namazu
A Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts. Suited for Japanese instruction following and enterprise use, with image input, tool use, and a 262K-token context window.
Muse Glimmer 30B
A dense, open-weight 30B multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. Supports image input, reasoning, and tool use with a 131K-token context window.
Solar Pro 4
Upstage's cost-efficient large language model with a 524K-token context window, built for long-horizon tasks and agentic workflows. Strong at office productivity and document-intensive work, with reasoning and tool use.
Muse Spark 1.2
Meta's reasoning model for complex agentic tasks, updating Muse Spark 1.1. Accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window with tool use.
Grok 4.6
xAI's model building on Grok 4.5 with a focus on long-running agents and ambitious interactive and visual work. Sustains complex multi-step tasks across research, analysis, and whole codebases, and shows more self-testing and verification on long trajectories. Reaches frontier intelligence across agentic coding and knowledge work benchmarks, matching GPT-5.6 Sol on the Artificial Analysis Intelligence Index, and produces notably stronger first passes on visual and interactive projects. Trained with agentic RL across kernel optimization, web development, and computer-aided design, with real-time information access and an OpenAI-compatible API.
Qwen 3.8 Max
Alibaba's flagship next-generation Qwen model, a sparse Mixture-of-Experts system with roughly 2.4 trillion total parameters and a 1M-token context window. Natively multimodal across text, images, video, and documents, with a configurable thinking mode, function calling, and built-in tools. Positioned by Alibaba as second only to Claude Fable 5 among frontier models, with particular strength in long-context agentic coding, document work, and visual reasoning. Previewed at the World AI Conference in Shanghai on 19 July 2026 and made broadly available on 3 August 2026.
Inkling Small
An open-weight multimodal mixture-of-experts model from Thinking Machines Lab with 12B active parameters out of 276B total, positioned as the smaller, more efficient member of the Inkling family. Accepts text, image, and audio input with reasoning, tool use, and a 524K-token context window.
Qwen 3.7 Flash
Alibaba's low-cost vision-language reasoning model, suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition and spatial understanding. Accepts text, image, and video input with a 1M-token context window.
Ling 3.0 Flash
InclusionAI's open-weight 124B-parameter mixture-of-experts model with about 5.1B parameters activated per token, designed for token efficiency and production-scale agentic inference. Supports reasoning, tool use, and a 262K-token context window.
Gemini 3.5 Flash Cyber
A specialized, cyber-focused Gemini model built on top of Gemini 3.5 Flash and fine-tuned for finding and fixing cybersecurity vulnerabilities at a lower price per token than larger models. Deployed within Google's CodeMender code security agent, where multiple 3.5 Flash Cyber agents collaborate to reach competitive frontier performance on benchmarks like CyberGym. Given its dual-use nature, it is available exclusively to governments and trusted partners via CodeMender as a limited-access pilot.
Laguna S 2.1
Poolside's open-weight coding agent model with 118B total and 8B active parameters, scoring 70.2% on Terminal-Bench 2.1. Built for agentic software engineering with reasoning, tool use, and a 1M-token context window.
Claude Opus 5
Anthropic's new state-of-the-art model, a thoughtful and proactive successor to Opus 4.8 that approaches Claude Fable 5's frontier intelligence at half the price. Delivers major gains in coding, agentic workflows, computer use, knowledge work, and scientific research, with strong self-verification and iteration, configurable effort settings, and Anthropic's best alignment and safety results to date. Also available in a Fast mode that runs around 2.5x the default speed.
Gemini 3.6 Flash
Google DeepMind's workhorse Flash model that builds on Gemini 3.5 Flash with better coding, knowledge work, and multimodal performance while reducing output token usage by roughly 17% per the Artificial Analysis Index. Natively multimodal across text, image, audio, and video with a 1M-token context window, configurable thinking levels, and built-in computer use, tuned for scaling agentic workflows at a lower cost per output token.
Motif 3 Beta
A large-scale sparse Mixture-of-Experts language model built from the ground up by South Korea's Motif Technologies with a fully in-house, proprietary design (Grouped Differential Latent Attention, per-expert Grouped PolyNorm, modified mHC). Has roughly 314B total parameters with about 13B active per token (384 routed experts, top-8, plus one shared expert) and a native 256K context window (262,144 tokens). Multilingual and general-purpose, with reasoning and tool-calling support in vLLM serving. This is an intermediate preview/beta checkpoint, released with open weights (bfloat16) for non-commercial research use ahead of the final release.
Gemini 3.5 Flash-Lite
Google's fastest and most cost-effective Gemini 3.5-class model, delivering around 350 output tokens per second per the Artificial Analysis Index. Designed for low-latency and high-throughput agentic workflows such as agentic search and document processing, with configurable thinking levels, built-in computer use, and full multimodal support across a 1M-token context window.
LongCat 2.0
Meituan's open-weight sparse mixture-of-experts language model with 48B active parameters out of 1.6T total. Suited for coding, repository-level changes, long-horizon problem solving, and agentic work, with reasoning, tool use, and a 1M-token context window.
Inkling
Thinking Machines Lab's open-weights general-purpose multimodal Mixture-of-Experts model with 975B total parameters and 41B active parameters. Inkling accepts text, image, and audio inputs, produces text, and is designed for agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation.
Kimi K3
Moonshot AI's flagship Kimi model for frontier intelligence, agentic coding, knowledge work, and deep reasoning. Kimi K3 supports a 1-million-token context window for long-running software engineering and research workflows.
KAT-Coder-Pro V2.5
Kwaipilot's flagship agentic coding model, designed to take over an entire issue or business workflow and autonomously locate and make the required changes. Supports tool use and a 262K-token context window.
GPT-5.6 Sol
OpenAI's flagship model in the GPT-5.6 series, advancing coding, scientific reasoning, long-horizon planning, and agentic workflows while improving reliability and efficiency on demanding real-world tasks. Adds a max reasoning-effort setting and an ultra mode that spawns subagents for complex, multi-step work.
GPT-5.6 Luna
The fast, low-cost tier of OpenAI's GPT-5.6 series, optimized for high-volume, latency-sensitive tasks such as classification, extraction, routing, and lightweight agentic steps. Approaches the larger GPT-5.6 tiers on many benchmarks while running several times faster at a fraction of the price.
GPT-5.6 Terra
The balanced tier of OpenAI's GPT-5.6 series, trading a small amount of peak quality for markedly lower latency and cost. Retains strong reasoning, coding, and agentic tool use with configurable reasoning effort, making it a default choice for production workloads that need frontier capability at scale.
Muse Spark 1.1
Meta Superintelligence Labs' updated flagship, building on Muse Spark with stronger agentic reasoning, more reliable multi-agent orchestration, and improved multimodal understanding across voice, text, and image. Extends the context window and reduces latency and reasoning token usage while raising coding and tool-use accuracy. Powers Meta AI across its product ecosystem.
Gemini 3.5 Pro
Google DeepMind's flagship Gemini model, rebuilt on a new foundation with a 2M-token context window and a Deep Think reasoning mode for the hardest math, coding, and multimodal tasks. Natively multimodal across text, image, audio, and video with streaming function calling and strong long-context grounding.
Grok 4.5
xAI's strongest model to date, built to excel at coding, agentic tasks, and knowledge work and co-developed alongside coding tools for real-world software engineering. Features real-time information access, extended reasoning, and large-context tool use with an OpenAI-compatible API.
Hy3
Tencent's open-weight 295B-parameter mixture-of-experts model (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use, with configurable reasoning effort, tool use, and a 262K-token context window.
Laguna XS 2.1
Poolside's open-weight coding agent model in the 33B-A3B class and a step forward from Laguna XS.2. Built for efficient agentic software engineering with reasoning, tool use, and a 262K-token context window.
Claude Sonnet 5
Anthropic's most capable Sonnet-class model, bringing frontier coding, agentic, and professional-work performance to the midsize tier while closing the gap with Opus 4.8 at a lower price. Supports adaptive thinking with selectable reasoning effort levels, a 1M-token context window, and text, image, and file inputs. Codenamed Fennec.
Sakana Fugu
Sakana AI's multi-agent orchestration model from Tokyo, delivered as a single OpenAI-compatible API. Fugu is itself a language model trained to call a pool of specialist LLMs (and recursive instances of itself), handling model selection, delegation, verification, and synthesis behind one endpoint. Built on Sakana AI's TRINITY and Conductor research, its routing intelligence is learned in model weights rather than hand-configured.
Sakana Fugu Ultra
The higher-quality tier of Sakana AI's Fugu multi-agent orchestration system, tuned for the hardest coding, reasoning, science, and agentic tasks. Coordinates a swappable pool of frontier LLMs through one OpenAI-compatible endpoint, delegating sub-tasks, verifying intermediate work, and synthesizing a single answer. Sakana reports strong vendor benchmarks including 93.2 on LiveCodeBench, 73.7 on SWE-Bench Pro, and 82.1 on TerminalBench.
Command A+
Cohere's enterprise flagship model building on Command A with stronger reasoning, agentic tool use, and multilingual performance across 23 languages. Optimized for secure, high-throughput RAG, retrieval, and long-horizon agent workflows in regulated environments, with private and on-premise deployment options.
Sarvam-105B
Sarvam AI's sovereign 105B-parameter Mixture-of-Experts model activating ~9B parameters per token, with a 128K-token context window. Trained on 12 trillion tokens across 22 Indian languages using 128 sparse experts with Multi-head Latent Attention and a custom low-fertility Indic tokenizer. Wins the majority of pairwise comparisons on Indian-language and STEM benchmarks.
GLM-5.2
Z.ai's (formerly Zhipu AI) flagship open-weight coding model with a 1M-token context window. Mixture-of-Experts architecture with 753B total parameters and ~40B active per request, featuring two cost-balancing reasoning modes. Tops several coding benchmarks while remaining a fraction of the cost of comparable proprietary frontier models. MIT-licensed weights.
Sarvam-30B
Sarvam AI's 30B-parameter Mixture-of-Experts reasoning model trained from scratch with only 2.4B active parameters per token. Optimized for real-time deployment and Indian languages, delivering strong reasoning, coding, and conversational performance while remaining efficient to serve. Open-weights.
Sarvam-M
Sarvam AI's 24B-parameter instruction-tuned model derived from Mistral-Small-3.1-24B, post-trained on English plus eleven major Indic languages (bn, hi, kn, gu, mr, ml, or, pa, ta, te). Delivers large relative gains on Indian-language, math, and programming benchmarks over its base model, with a hybrid reasoning mode for complex tasks.
Kimi K2.7 Code
Moonshot AI's latest open-source, coding-focused model in the Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. A 1-trillion-parameter model that cuts reasoning token usage by roughly 30% versus K2.6 while improving coding and agent performance — +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite for multi-language support. Released under a Modified MIT License and available via Kimi APIs and Hugging Face.
DiffusionGemma
Google DeepMind's experimental diffusion-based member of the Gemma 4 open model family. Unlike autoregressive models that generate text one token at a time, DiffusionGemma denoises a canvas of placeholder tokens to produce up to 256 tokens in parallel, finalizing output in one block. A Mixture-of-Experts model with 26B total parameters and 3.8B active per inference, delivering roughly 4x the throughput of similarly sized autoregressive Gemma models on local hardware. Excels at non-linear tasks like in-line editing, molecular sequencing, mathematical graphing, and self-correcting puzzles.
Claude Mythos 5
Anthropic's frontier Mythos-class model — the same underlying model as Claude Fable 5 but with safeguards lifted in some areas. It has the strongest cybersecurity capabilities of any model in the world, alongside state-of-the-art performance in software engineering, knowledge work, vision, and scientific research. Access is restricted to a small group of trusted cyberdefenders and infrastructure providers through Project Glasswing.
Claude Fable 5
Anthropic's first publicly available Mythos-class model, exceeding the capabilities of any model the company has previously made generally available. State-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, vision, and scientific research. Its lead grows on longer and more complex tasks. Ships with built-in safeguards that route sensitive cybersecurity, biology, chemistry, and distillation queries to Claude Opus 4.8.
Gemma 4 12B
Google's medium-size open-weight model with 12 billion parameters from the Gemma 4 family. Encoder-free unified multimodal architecture that natively processes text, image, audio, and video inputs without dedicated encoders. Features a 256K context window and supports 140+ languages. First medium-sized model capable of natively ingesting audio. Suitable for local deployment on GPUs with 16GB VRAM.
Nemotron 3 Ultra
NVIDIA's flagship open 550B-parameter Mixture-of-Experts model with 55B active parameters, built for frontier reasoning and orchestration in long-running agentic systems. Features hybrid Mamba-Transformer architecture, LatentMoE routing, multi-token prediction, and NVFP4 precision for 5x higher throughput. Achieves 30% lower cost-to-task-completion on agentic benchmarks. Supports 1M+ token context window with 95% accuracy on Ruler@1M.
MiniMax M3
MiniMax's frontier open-weight model with 1M-token context window, native multimodality (text, image, video), and strong coding capabilities. Built on MiniMax Sparse Attention (MSA) architecture, achieving 59% on SWE-Bench Pro with significantly improved efficiency at long context.
Claude Opus 4.8
Anthropic's most advanced model, building on Opus 4.7 with improvements across benchmarks in coding, agentic skills, reasoning, and knowledge work. Features enhanced honesty, better tool use efficiency, dynamic workflows support, and improved alignment.
Yi-Lightning
01.AI's flagship large language model with enhanced Mixture-of-Experts architecture. Ranked 6th on Chatbot Arena with particularly strong results in Chinese, Math, Coding, and Hard Prompts categories. Features advanced expert segmentation and optimized KV-caching.
Jamba Large 1.7
AI21's latest hybrid SSM-Transformer model with Mixture-of-Experts architecture. Features a 256K context window, improved grounding and instruction-following. 94B total parameters with 398B active, optimized for enterprise long-context tasks.
Snowflake Arctic
Snowflake's enterprise-focused open LLM with 480B total parameters using a fine-grained MoE architecture with only 17B active parameters per input. Apache 2.0 licensed, excels at SQL generation, coding, and enterprise intelligence tasks with breakthrough training efficiency.
Palmyra X5
Writer's most advanced adaptive reasoning model with a 1 million token context window. Processes full million-token prompts in approximately 22 seconds with multi-turn function calls in 300ms. Optimized for enterprise agentic AI workflows at 3-4x lower cost than GPT-4.1.
Ring-2.6-1T
InclusionAI's (Ant Group) trillion-parameter open-weights reasoning model with 63B active parameters per token. Built for real-world agent workflows with adaptive reasoning-effort modes. Features hybrid linear and MLA attention architecture with MIT license.
DBRX
Databricks' open-source 132B parameter Mixture-of-Experts transformer model with 36B active parameters per input. Released under Databricks Open Model License, optimized for enterprise workloads including SQL generation and coding tasks.
StableLM 2 12B
Stability AI's 12.1 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets. Supports multiple languages and offers strong performance for its compact size with instruction-tuned chat variant available.
Falcon 3 10B
TII's open-source 10B parameter model from the Falcon 3 family. Achieved number one position on Hugging Face's LLM leaderboard in its size category, outperforming Meta's Llama variants and other models under 13B parameters.
Falcon-H1
TII's hybrid Mamba-Transformer model that outperforms comparable offerings from Meta's Llama and Alibaba's Qwen in the 30-70B parameter range. Designed for real-world AI on everyday devices and resource-limited settings with state-of-the-art efficiency.
Solar Pro 3
Upstage's powerful Mixture-of-Experts language model with 102B total parameters and 12B active parameters per forward pass. Optimized for Korean with strong English and Japanese support. Excels at complex reasoning, structured output generation, and agentic workflows.
K2 Think
A 32 billion parameter open-weights reasoning model by LLM360/MBZUAI, built on Qwen2.5-32B. Trained with reinforcement learning and verifiable rewards for long chain-of-thought reasoning, agentic planning, and complex problem solving in math, science, and code.
Qwen 3.7 Plus
Alibaba's multimodal variant in the Qwen 3.7 family, optimized for vision understanding and multimodal tasks. Ranked
Qwen 3.7 Max
Alibaba's flagship proprietary model engineered for advanced agentic coding, complex reasoning, and long-horizon task execution. Ranked
Gemini 3 Flash
Google's balanced model combining Gemini 3 Pro's reasoning capabilities with the Flash line's latency, efficiency, and cost. Features configurable thinking levels, multimodal function responses, and streaming function calling for complex agentic workflows.
Gemini 3.1 Flash-Lite
Google's most cost-efficient Gemini model optimized for high-volume, low-latency use cases. Delivers 2.5x faster time to first token versus Gemini 2.5 Flash with full multimodal support. Ideal for agentic tasks, data extraction, translation, and classification.
Gemini 3.5 Flash
Google DeepMind's balanced Gemini 3.5 model that pairs Pro-line reasoning quality with Flash-line latency and cost. Natively multimodal across text, image, audio, and video with a 1M-token context window, configurable thinking levels, and streaming function calling, tuned for high-throughput production workloads.
Granite 4.1 30B
IBM's largest dense decoder-only 30B parameter language model from the Granite 4.1 family. Trained on approximately 15T tokens with long-context extension up to 512K tokens. Supports tool calling, RAG, code generation, multilingual tasks across 12 languages. Released under Apache 2.0.
Granite 4.1 8B
IBM's dense decoder-only 8B parameter language model from the Granite 4.1 family. Supports 131K-token context, tool calling, RAG, code generation with fill-in-the-middle, text summarization, classification, and extraction across 12 languages. Released under Apache 2.0.
Laguna M.1
Poolside AI's flagship agentic coding model with 225B total parameters and 23B active (MoE). Trained from scratch in-house on 30T tokens across 6,144 NVIDIA Hopper GPUs. Optimized for complex multi-step software engineering tasks including codebase exploration, file editing, test running, and iterative debugging.
DeepSeek V4 Flash
DeepSeek's efficient V4 model with 284B total parameters (13B activated). Optimized for speed and cost-efficiency while maintaining strong performance. Supports 1M token context window.
GPT-5.5
OpenAI's most capable model designed for complex real-world work including coding, online research, information analysis, and document creation. Features advanced agentic capabilities with tool search and multi-step task execution.
DeepSeek V4 Pro
DeepSeek's flagship V4 model with 1.6T total parameters (49B activated). MoE architecture supporting 1M token context. Closes the gap with frontier proprietary models on reasoning and coding benchmarks.
Qwen 3.6 27B
Alibaba's dense 27B parameter model that outperforms its own 397B MoE predecessor on agentic coding benchmarks. Strong multilingual and reasoning capabilities released under Apache 2.0.
MiMo-V2.5-Pro
Xiaomi's flagship 1.02T-parameter Mixture-of-Experts model with 42B active parameters, built on a hybrid-attention architecture with 3-layer Multi-Token Prediction. Designed for complex agentic tasks, software engineering, and long-horizon instruction following with a 1M-token context window.
Hy3 Preview
Tencent's flagship open-weight Mixture-of-Experts model from the Hunyuan family with 295B total parameters and 21B active. Integrates fast and slow thinking modes with configurable reasoning effort. Designed for agentic workflows, cross-file code refactoring, long-document analysis, and multi-step tool use.
Qwen 3.6 35B-A3B
Alibaba's efficient Mixture-of-Experts model with 35B total parameters and 3B active per token. Frontier-level agentic coding performance with 73.4% on SWE-bench Verified and 92.7 on AIME 2026. Released under Apache 2.0.
GPT-5.4 Mini
OpenAI's compact reasoning model optimized for coding, computer use, and subagent tasks. Approaches GPT-5.4 performance on several benchmarks while running more than 2x faster.
Muse Spark
Meta Superintelligence Labs' first model, featuring advanced reasoning, multimodal understanding, and agentic capabilities. Processes voice, text, and image inputs with tool use and multi-agent orchestration. Powers Meta AI across its product ecosystem.
Qwen 3.6 Plus
Alibaba's proprietary flagship model in the Qwen 3.6 family, targeting enterprise AI workflows with stronger agentic coding capability, visual coding support, and end-to-end enterprise engineering features.
Gemma 4 31B
Google's flagship open-weight dense model with 31 billion parameters from the Gemma 4 family. All parameters active per forward pass with top-tier performance on reasoning benchmarks including AIME 2026 and MMLU Pro. Supports vision and extended 256K context window.
Gemma 4 26B
Google's high-performance open-weight dense model with 26 billion parameters from the Gemma 4 family. Supports multimodal inputs including text and images with a 256K extended context window. Strong reasoning and code generation capabilities with all parameters active per forward pass.
Gemma 4 31B
Google's flagship open-weight dense model with 31B parameters. All parameters active per forward pass. Ranks among top open models with strong performance on AIME 2026 (89.2%) and MMLU Pro (85.2%). Supports vision and extended context.
Gemma 4 E4B
Google's efficient 4 billion parameter variant from the Gemma 4 family. Designed for resource-constrained environments while maintaining strong text generation quality. Text-only model with a 32K context window, balancing performance and efficiency.
Grok 4.3
xAI's latest and most intelligent model with strong agentic tool calling, minimal hallucinations, and configurable reasoning. Supports 1M token context window with competitive pricing.
GPT-OSS 120B
OpenAI's first open-weight large model with 120 billion parameters. Released under Apache 2.0 license, offering strong performance on reasoning and coding tasks while being fully self-hostable.
GPT-OSS 20B
OpenAI's compact open-weight model with 20 billion parameters. Released under Apache 2.0 license, designed for efficient deployment on consumer hardware while maintaining strong coding and reasoning capabilities.
Claude Opus 4.7
Anthropic's latest and most advanced model with state-of-the-art reasoning, coding, and analysis capabilities. Features improved tool use, extended thinking, and enhanced safety alignment.
Nemotron 3 Super 120B
NVIDIA's open hybrid Mamba-Transformer MoE model with 120B total parameters (12B active). Features 1M token context window and excels at agentic reasoning, coding, planning, and tool calling.
GPT-5.4
OpenAI's frontier reasoning model combining advances in coding, reasoning, and agentic workflows. Features 1.1M token context window and strong performance on complex multi-step problems.
Kimi K2.6
Moonshot AI's latest model with ultra-long context window support, strong reasoning capabilities, and excellent performance on complex multi-step tasks. Known for reliable long-document understanding.
Qwen 3.6
Alibaba's latest Qwen model with enhanced reasoning, multilingual capabilities, and improved instruction following. Features strong performance on coding, math, and general knowledge benchmarks.
MiniMax M2.7
MiniMax's latest large language model with strong multilingual and multimodal capabilities. Competitive pricing with high-quality text generation and improved reasoning performance.
Mistral Small 4
Mistral AI's efficient hybrid model unifying instruct, reasoning, and coding in a single model. Open-weight under Apache 2.0 with strong performance for its size class.
Gemini 3.1 Pro
Google's latest flagship multimodal model with state-of-the-art performance on reasoning, coding, and multimodal understanding. Features native tool use, grounding, and million-token context window.
GLM-5.1
Zhipu AI's latest bilingual model with strong Chinese and English capabilities. Features improved reasoning, coding, and tool use with competitive performance on academic benchmarks.
GPT-5.5 Pro
OpenAI's premium tier model with extended reasoning capabilities, higher accuracy on complex tasks, and priority access. Optimized for professional and enterprise workloads requiring maximum quality.
Grok 4.20
xAI's multi-agent capable model with 2M token context window. Available in reasoning, non-reasoning, and multi-agent variants for diverse enterprise workloads.
Mistral Medium 3.5
Mistral AI's balanced model offering strong multilingual performance with excellent price-performance ratio. Optimized for production workloads requiring reliable quality across European and global languages.
Grok 4
xAI's latest model with real-time information access, strong reasoning capabilities, and competitive performance on coding and analysis tasks. Features improved tool use and multimodal understanding.
DeepSeek V4
DeepSeek's fourth-generation model with improved mixture-of-experts architecture, enhanced reasoning and coding capabilities, and stronger multilingual performance. Competitive with frontier proprietary models.
Claude Sonnet 4.6
Anthropic's balanced model offering strong performance at lower cost and latency than Opus. Excellent for everyday coding, analysis, and content generation tasks with good reasoning capabilities.
Claude Opus 4.6
Anthropic's most capable model in the Claude 4 family, excelling at complex analysis, extended reasoning, scientific research, and advanced code generation. Features significantly improved accuracy and reduced hallucinations.
Devstral 2
Mistral AI's frontier code agents model designed for solving software engineering tasks. Open-weight model optimized for agentic coding workflows and complex development tasks.
GigaChat 3.1 Ultra
Sber's flagship large-scale Mixture-of-Experts model with 702B total parameters and 36B active. Designed for multilingual assistant workloads, reasoning, code generation, tool use, and large-cluster deployment. Open-weight release.
GigaChat 3.1 Lightning
Sber's compact Mixture-of-Experts model with 10B total parameters and 1.8B active. Designed for fast multilingual assistant workloads, reasoning, code, function calling, and product-style deployment on edge devices.
Mistral Large 3
Mistral AI's largest open-weight model with 41B active parameters (675B total MoE). State-of-the-art general-purpose multimodal model with 256K context window and powerful agentic capabilities. Released under Apache 2.0.
Grok 4.1 Fast
xAI's fast and cost-effective model with 2M token context window. Offers both reasoning and non-reasoning modes at significantly lower pricing than flagship models.
GLM-4.7
Zhipu AI's multilingual agentic coding model with strong reasoning, tool use, and UI generation capabilities. Predecessor to GLM-5.1 with competitive performance on coding benchmarks.
Claude Sonnet 4.5
Anthropic's previous-generation balanced model with strong coding and analysis capabilities. Offers excellent price-performance ratio for production workloads requiring reliable quality.
Claude Haiku 4.5
Anthropic's fastest model with near-frontier intelligence. Optimized for high-throughput, low-latency applications requiring quick responses at minimal cost. Supports extended thinking.
AlemLLM
Kazakhstan's flagship Mixture-of-Experts language model developed by Astana Hub with technical support from 01.AI. Features 247B total parameters with 22B active per token, achieving state-of-the-art results on Kazakh, Russian, and English benchmarks. Outperforms GPT-4o on Kazakh language tasks.
Trendyol LLM 8B T1
Turkish-optimized 8B chat model developed by Trendyol, Turkey's largest e-commerce platform. Built on Qwen3-8B and fine-tuned on large-scale Turkish e-commerce datasets. Features advanced chain-of-thought reasoning in Turkish with dual operation modes (/think and /no_think), strong instruction following, summarization, coding, and attribute extraction for catalogue enrichment. English reasoning capabilities are preserved alongside Turkish.
Gemini 2.5 Flash
Google's cost-effective model optimized for high throughput tasks. Balances speed and intelligence with strong multimodal capabilities and 1M token context window.
Gemini 2.5 Pro
Google's high-capability reasoning model with adaptive thinking for complex agentic and multimodal challenges. Features 1M token context window and strong performance on coding and scientific tasks.
YandexGPT 5 Lite
Yandex's compact 8B parameter language model trained on 15T tokens of primarily Russian and English text. Features 32K context window with strong performance on web, code, and mathematics tasks. Open-weight release.
GPT-5
OpenAI's fifth-generation flagship model with significant improvements in reasoning, multimodal understanding, and code generation. Features enhanced instruction following and expanded context window.
Nemotron Nano 9B v2
NVIDIA's compact 9B parameter model trained from scratch for both reasoning and non-reasoning tasks. Generates reasoning traces before final responses. Efficient for edge and on-device deployment.
Kumru 7B
Turkish large language model developed by VNGRS, pre-trained from scratch on 500 GB of Turkish corpora (300B tokens). Decoder-only architecture with a custom tokenizer optimized for Turkish, supporting code, math, and chat. Outperforms significantly larger multilingual models on the Cetvel Turkish benchmark. Available for on-premise enterprise deployment.
Llama 4 Maverick
Meta's quality-focused MoE model with 17B active parameters (400B total, 128 experts). Targets quality-critical tasks with benchmark scores competitive with GPT-4o and Gemini 2.5 Pro.
Llama 4 Scout
Meta's efficient MoE model with 17B active parameters (109B total, 16 experts). Supports up to 10M token context — the longest of any production model. Strong performance on reasoning and multilingual tasks.
Qwen3 Coder
Alibaba's Qwen3 Coder model optimized for software development tasks including code generation, debugging, code review, and technical documentation with strong multilingual programming support.
Qwen3 32B
Alibaba's Qwen3 32B dense language model with strong reasoning and multilingual capabilities, supporting function calling and code generation across diverse tasks.
Qwen3 235B
Alibaba's Qwen3 235B mixture-of-experts model delivering frontier-level performance with advanced reasoning, function calling, and code generation capabilities at massive scale.
Gemma 3 27B
Google's largest open-weight model in the Gemma 3 family with 27 billion parameters. Supports multimodal inputs including text and images with a 128K context window. Delivers strong performance across reasoning, code generation, and vision tasks, competitive with larger proprietary models.
Gemma 3 12B
Google's mid-size open-weight model with 12 billion parameters from the Gemma 3 family. Supports multimodal inputs including text and images with a 128K context window. Strong performance on reasoning and code generation tasks at moderate compute cost.
Gemma 3 4B
Google's compact open-weight model with 4 billion parameters from the Gemma 3 family. Supports multimodal inputs including text and images with a 128K context window. Balances efficiency and capability for vision and language tasks.
Command A
Cohere's flagship 111B parameter model optimized for demanding enterprises requiring fast, secure, and high-quality AI. Excels at RAG, tool use, and multilingual tasks with strong reasoning capabilities.
Mistral Small 3.1
Mistral AI's Small 3.1 model with 24B parameters offering efficient multimodal capabilities including vision, function calling, and code generation with a large 128K context window.
Phi-4 Mini
Microsoft's Phi-4 Mini model with 3.8B parameters providing lightweight yet capable language understanding and code generation, optimized for resource-constrained deployments with a large 128K context window.
DeepSeek R1
DeepSeek's reasoning-focused model trained with reinforcement learning for complex multi-step reasoning. Excels at math, science, and coding problems requiring chain-of-thought reasoning.
Codestral
Mistral AI's cutting-edge code generation model specializing in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code completion, correction, and test generation. Features efficient architecture with 2x faster generation than its predecessor.
ISSAI KazLLM 1.0 70B
Large language model developed by ISSAI (Nazarbayev University) customized from Llama 3.1 70B to improve helpfulness of responses in the Kazakh language. Part of Kazakhstan's initiative to ensure the country benefits from generative AI advancements.
Llama 3.3 70B Instruct
Meta's flagship open-weight model with 70 billion parameters. Strong multilingual capabilities with competitive performance on reasoning and coding benchmarks. Available for self-hosting and through various inference providers.
Phi-4
Microsoft's Phi-4 model with 14B parameters excelling at reasoning and code generation tasks, delivering strong performance relative to its compact size with efficient inference characteristics.
Command R7B
Cohere's compact 7B parameter model optimized for RAG, tool use, and code tasks. Delivers top-tier speed and efficiency on commodity GPUs and edge devices with 128K context window.
DeepSeek V3
DeepSeek's third-generation large language model featuring mixture-of-experts architecture, strong multilingual capabilities, and competitive performance on reasoning and coding benchmarks.
Llama 3.2 3B Instruct
Meta's lightweight open-weight model with 3 billion parameters from the Llama 3.2 family. Designed for on-device and edge deployment with strong text generation capabilities relative to its size. Supports instruction following and general-purpose tasks.
Llama 3.2 11B Vision Instruct
Meta's multimodal open-weight model with 11 billion parameters from the Llama 3.2 family. Supports both text and image inputs, enabling visual understanding tasks alongside standard text generation. Suitable for applications requiring vision capabilities at moderate scale.
Llama 3.2 90B Vision Instruct
Meta's largest multimodal open-weight model with 90 billion parameters from the Llama 3.2 family. Delivers strong performance on both text and image understanding tasks with competitive results on visual reasoning benchmarks. Designed for high-quality inference requiring vision capabilities.
Llama 3.1 8B Instruct
Meta's efficient open-weight model with 8 billion parameters from the Llama 3.1 family. Optimized for instruction following with strong performance on general tasks, coding, and multilingual benchmarks. Ideal for cost-effective deployment and edge inference scenarios.