Çdo model kryesor AI. Një platformë.

Claude Sonnet 5.5

Anthropic

Claude Sonnet 5.5 offers the best combination of speed and intelligence, with adaptive thinking for coding, tool use, reasoning, and long-horizon work.

Claude Sonnet 5

Anthropic

Claude Sonnet 5 is Anthropic's most agentic Sonnet model yet, built for coding, tool use, reasoning, and long-horizon professional work at lower cost than Opus-class models.

Claude Sonnet 4.6

Anthropic

Claude Sonnet 4.6 is Anthropic's latest balanced model offering strong coding, reasoning, and agentic capabilities with improved instruction following.

Claude Sonnet 4.5

Anthropic

Claude Sonnet 4.5 is the best coding model to date, with significant improvements across the entire development lifecycle.

Claude Opus 5.5

Anthropic

Claude Opus 5.5 is Anthropic's model for long-running agentic coding and knowledge work. It combines a one million token context window with adaptive thinking that is always on, a 128K token maximum output, and a medium default effort level.

Claude Opus 5

Anthropic

Claude Opus 5 is Anthropic's model for complex agentic coding and enterprise work, delivering intelligence close to Claude Fable 5 at half the price. It uses adaptive thinking to calibrate reasoning per task and supports a one million token context window at standard pricing. Unlike Fable 5, Opus 5 has no data retention requirements for general access.

Claude Opus 4.8

Anthropic

Claude Opus 4.8 is Anthropic's most capable generally available model, with a step-change improvement in agentic coding over Claude Opus 4.7. It uses adaptive thinking to calibrate reasoning per task and supports a one million token context window at standard pricing.

Claude Opus 4.7

Anthropic

Claude Opus 4.7 is Anthropic's most capable generally available model, with a step-change improvement in agentic coding over Claude Opus 4.6. It uses adaptive thinking to calibrate reasoning per task and supports a one million token context window at standard pricing.

Claude Opus 4.6

Anthropic

Claude Opus 4.6 is Anthropic's flagship language model built for complex, multi-step work in coding, financial analysis, and legal reasoning. It uses extended thinking to work through complex problems carefully and features a one million token context window.

Claude Opus 4.5

Anthropic

Claude Opus 4.5 brings further reasoning, coding, and agentic improvements over Opus 4.1, with stronger tool use and tighter instruction following.

Claude Haiku 5.5

Anthropic

Claude Haiku 5.5 is a fast, cost-efficient model for high-volume, latency-sensitive tasks such as classification, extraction, routing, and subagent work.

Claude Haiku 4.5

Anthropic

Claude Haiku 4.5 delivers similar levels of coding performance at one-third the cost and more than twice the speed of larger models.

Claude Fable 5.2

Anthropic

Claude Fable 5.2 is built for demanding reasoning and long-horizon agentic work, with adaptive thinking, a 1M token context window, and up to 128K output tokens.

Claude Fable 5.1

Anthropic

Claude Fable 5.1 is Anthropic's next model in the Fable family, with improvements in agentic coding, long-running agentic workflows, knowledge work, front-end and visual code generation, and finance and analysis tasks. It supports adaptive thinking and a 1M token context window.

O4 Mini

OpenAI

OpenAI's fast, lightweight reasoning model optimized for multi-step problem solving at lower cost.

O3 Mini

OpenAI

o3-mini is the lightweight, low-cost reasoning variant of o3, well suited to quick analytical tasks at scale.

O3

OpenAI

o3 is OpenAI’s general-purpose reasoning model, balancing strong analytical performance with reasonable latency and cost.

GPT OSS 120b

OpenAI

OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases – gpt-oss-120b is for production, general purpose, high reasoning use-cases.

GPT OSS 20b

OpenAI

OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases – gpt-oss-20b is for lower latency, and local or specialized use-cases.

GPT-6.1 Terra

OpenAI

GPT-6.1 Terra is OpenAI's fast, efficient model for visual reasoning, precise computer use, and clear, natural writing. Use it to match product photos to catalog entries, interpret technical drawings, act on application screens, or draft and revise text. Tool calling requires the Responses API.

GPT-6.1 Sol

OpenAI

GPT-6.1 Sol is OpenAI's newest Sol model, delivering near-Astra performance at a lower cost for complex coding, computer use, and professional work.

GPT-6 Sol

OpenAI

GPT-6 Sol is OpenAI's mid-tier GPT-6 model, built to power complex coding and agentic workflows.

GPT-6 Luna

OpenAI

GPT-6 Luna is OpenAI's most efficient GPT-6 model, built for focused, high-volume tasks.

GPT-6 Astra

OpenAI

GPT-6 Astra is OpenAI's most capable model, built for complex reasoning, coding, computer use, research, and document creation.

GPT-5.6 Terra

OpenAI

GPT-5.6 Terra is an OpenAI GPT-5.6 model that balances intelligence and cost, using the Responses API for reasoning and stateful context management.

GPT-5.6 Sol

OpenAI

GPT-5.6 Sol is OpenAI's frontier GPT-5.6 model for complex professional work, using the Responses API for reasoning and stateful context management.

GPT-5.6 Luna

OpenAI

GPT-5.6 Luna is an OpenAI GPT-5.6 model optimized for cost-sensitive workloads, using the Responses API for efficient text generation.

GPT-5.5 Pro

OpenAI

GPT-5.5 pro uses OpenAI's Responses API with built-in tools, improved reasoning, and stateful context management.

GPT-5.5

OpenAI

GPT-5.5 is OpenAI's flagship model with strong coding, reasoning, and multimodal capabilities.

GPT-5.4 Pro

OpenAI

GPT-5.4 pro uses OpenAI's Responses API with built-in tools, improved reasoning, and stateful context management.

GPT-5.4 Nano

OpenAI

GPT-5.4 nano is OpenAI's smallest and fastest model, optimized for edge and low-latency use cases.

GPT-5.4 Mini

OpenAI

GPT-5.4 mini is a smaller, faster, and more cost-efficient version of GPT-5.4 for lightweight tasks.

GPT-5.4

OpenAI

GPT-5.4 is OpenAI's flagship model with strong coding, reasoning, and multimodal capabilities.

GPT-5.1

OpenAI

GPT-5.1 is OpenAI’s incremental improvement over GPT-5, with stronger coding, reasoning, and writing.

GPT-5 Nano

OpenAI

GPT-5 Nano is OpenAI’s smallest GPT-5 variant, optimized for low latency and cheap, high-throughput tasks.

GPT-5 Mini

OpenAI

GPT-5 Mini is the lightweight, low-cost variant of GPT-5, well suited to high-volume coding and reasoning tasks.

GPT-5

OpenAI

OpenAI's model excelling at coding, writing, and reasoning.

GPT-4o Mini

OpenAI

GPT-4o Mini is the lightweight, low-cost variant of GPT-4o, well suited to high-volume tasks with multimodal inputs.

GPT-4o

OpenAI

GPT-4o is OpenAI’s multimodal flagship, accepting text and images and responding quickly across a wide range of tasks.

GPT-4.1 Nano

OpenAI

GPT-4.1 Nano is OpenAI’s smallest and cheapest GPT-4.1 variant, optimized for high-throughput, low-latency tasks.

GPT-4.1 Mini

OpenAI

Fast, affordable version of GPT-4.1 with a million-token context window.

GPT-4.1

OpenAI

OpenAI's flagship GPT model for complex tasks with a million-token context window.

Gemma 4 26b A4b IT

Google

Gemma 4 is Google's most intelligent family of open models, built from Gemini 3 research to maximize intelligence-per-parameter.

Gemini 4 Carbon

Google

Google's most capable Gemini 4 reasoning model, built for complex software engineering, math and science problems, autonomous multi-step agents, and long outputs up to 1M tokens.

Gemini 4 Argon

Google

Google's frontier model for deep work on complex, real-world tasks across coding, finance, legal, cybersecurity and science. The first model in the Gemini 4 family.

Gemini 3.8 Flash

Google

Our most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows.

Gemini 3.7 Flash

Google

Gemini 3.7 Flash is a highly capable, natively multimodal reasoning model optimized for agentic workflows and real-world tasks.

Gemini 3.6 Flash

Google

Gemini 3.6 Flash provides sustained frontier-level intelligence optimized for real-world tasks at a higher speed and lower cost, excelling at code generation, agentic execution, and spatial reasoning.

Gemini 3.5 Flash Lite

Google

Gemini 3.5 Flash-Lite is a low-latency, cost-effective multimodal model optimized for high-throughput, low-cost execution for subagent tasks and document parsing.

Gemini 3.5 Flash

Google

Gemini 3.5 Flash is Google's fast multimodal model with frontier intelligence, superior search, and grounding capabilities.

Gemini 3.1 Pro

Google

Google's most intelligent Gemini model with improved reasoning, a medium thinking level, and a 1M token context window.

Gemini 3.1 Flash Lite

Google

Google's lightest and most cost-efficient Gemini model for high-throughput tasks.

Gemini 3 Flash

Google

Gemini 3 Flash is Google's fast multimodal model with frontier intelligence, superior search, and grounding capabilities.

Gemini 2.5 Pro

Google

Google's most capable Gemini 2.5 model with strong reasoning, thinking support, and a 1M token context window.

Gemini 2.5 Flash Lite

Google

Google's lightest and most cost-efficient Gemini 2.5 model for high-throughput tasks.

Gemini 2.5 Flash

Google

Google's fast multimodal Gemini 2.5 model with strong reasoning and a 1M token context window.

Grok 4.20 Multi Agent 0309

xAI

xAI's Grok 4.20 multi-agent model with a 2M-token context window. Multiple agents collaborate in parallel to perform deep research tasks, with function calling, structured outputs, and reasoning capabilities.

Grok 4.20 0309 Reasoning

xAI

xAI's Grok 4.20 reasoning model. Uses extended thinking to work through complex problems, returning a reasoning trace alongside the final answer.

Grok 4.20 0309 Non Reasoning

xAI

xAI's Grok 4.20 non-reasoning model. Skips the thinking trace for fast, single-pass responses while keeping the same training as the reasoning variant.

Grok 4.7

xAI

xAI's Grok 4.7, a frontier reasoning model for coding, agentic tasks, and knowledge work. Accepts text and image inputs, and supports function calling, structured outputs, and configurable reasoning effort (low, medium, high, xhigh).

Grok 4.6

xAI

xAI's Grok 4.6, a flagship reasoning model for coding, agentic tasks, and visual work. Accepts text and image inputs, and supports function calling and structured outputs.

Grok 4.5

xAI

xAI's Grok 4.5, a frontier model built for coding, agentic tasks, and knowledge work. Accepts text and image inputs, and supports function calling, structured outputs, and configurable reasoning effort (low, medium, high).

Grok 4.3

xAI

xAI's Grok 4.3 model with a 1M-token context window and strong agentic tool calling with minimal hallucinations. Accepts text and image inputs, and supports function calling, structured outputs, and configurable reasoning effort (none, low, medium, high).

Muse Wonder 1.0

Meta

Muse Wonder 1.0 is Meta's agentic and coding model for complex software engineering, multi-step tool loops, and long-context reasoning.

Muse Spark Open

Meta

Muse Spark Open is Meta's open-weights reasoning model for coding and agentic workflows. It supports native image understanding and function calling, and is served through Fireworks serverless inference.

Muse Spark 1.3

Meta

Muse Spark 1.3 is Meta's latest agentic and coding model, tuned for multi-step tool loops, browser tasks, software engineering, and long-horizon reasoning.

Muse Spark 1.2

Meta

Muse Spark 1.2 is Meta's agentic and coding model for multi-step tool loops, software engineering assistants, and long-context reasoning.

Muse Spark 1.1

Meta

Muse Spark 1.1 is Meta's agentic and coding model for multi-step tool loops, software engineering assistants, and long-context reasoning.

Muse Ember 1.0

Meta

Muse Ember 1.0 is Meta's fast, efficient model with top-tier speed for its class, built for subagent workloads.

Llama Guard 3 8b

Meta

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama 4 Scout 17b 16e Instruct

Meta

Meta's Llama 4 Scout is a 17 billion parameter model with 16 experts that is natively multimodal. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.

Llama 3.3 70b Instruct FP8 Fast

Meta

Llama 3.3 70B quantized to fp8 precision, optimized to be faster.

Llama 3.2 11b Vision Instruct

Meta

The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image.

Llama 3.2 3b Instruct

Meta

The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.

Llama 3.2 1b Instruct

Meta

The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.

Llama 3.1 8b Instruct FP8

Meta

Llama 3.1 8B quantized to FP8 precision

Deepseek v4 Pro

DeepSeek

ddeepseekText Generation DeepSeek V4 Pro is a high-capability reasoning model from DeepSeek, served via Fireworks infrastructure for production-grade inference.

Kimi K3

Moonshot AI (Kimi)

Kimi K3 is Moonshot's flagship 2.8 trillion-parameter model, built on Kimi Delta Attention (a hybrid linear attention mechanism) with Attention Residuals. It offers native visual understanding, always-on reasoning, and a 1M-token context window for long-horizon coding, knowledge work, and deep reasoning tasks.

Kimi K2.6

Moonshot AI (Kimi)

Kimi K2.6 is a frontier-scale open-source 1T parameter model with a 262.1k context window, multi-turn tool calling, vision inputs, and structured outputs for agentic workloads.

Mistral Small 3.1 24b Instruct

Mistral AI

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.

Qwq 32b

Qwen

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

Qwen3.8 27b

Qwen

Qwen 3.8 27B is a 27-billion-parameter instruction-tuned language model from Alibaba's Qwen family, designed for vision, efficient general-purpose text generation and agentic workloads.

Qwen3 30b A3b FP8

Qwen

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support.

Qwen2.5 Coder 32b Instruct

Qwen

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:

GLM 5.3 Flash

Z.ai (GLM)

The first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM 5.3

Z.ai (GLM)

GLM-5.3 is Z.ai's flagship agentic coding model, pairing a 1M-token context window with reasoning, function calling, and structured outputs to power multi-step, tool-driven development workflows.

GLM 5.2

Z.ai (GLM)

Z.ai's flagship agentic coding model

GLM 4.7 Flash

Z.ai (GLM)

GLM-4.7-Flash is a fast and efficient multilingual text generation model with a 131,072 token context window. Optimized for dialogue, instruction-following, and multi-turn tool calling across 100+ languages.

M3.1 Flash

MiniMax

MiniMax M3.1 Flash is a fast, cost-efficient multimodal model for agentic tasks, coding, and computer use. It accepts text, image, and video input, always reasons before answering with adjustable effort, and has a 1M-token context window.

M3

MiniMax

MiniMax's M3 language model with frontier coding and agentic capabilities, a 1M token context window, and multilingual support.

M2.7

MiniMax

MiniMax's M2.7 language model with multilingual capabilities.

Qwen3.8 Max

Alibaba

Alibaba's Qwen 3.8 Max is a 2.4-trillion-parameter MoE flagship built for professional-grade coding and long-horizon autonomous work, capable of delivering complete, production-grade projects spanning 10+ days across legal, financial, design, and other specialized domains. Native visual understanding of images and extended video runs through the full plan-execute-verify cycle, served via DashScope's OpenAI-compatible endpoint.

Qwen3.7 Plus

Alibaba

Alibaba's Qwen 3.7 Plus is the cost-effective member of the Qwen3.7 series, pairing strong text capabilities with image and video understanding and full-stack agent-level intelligence for coding, tool use, and GUI-based automation, served via DashScope's OpenAI-compatible endpoint.

Qwen3.7 Max

Alibaba

Alibaba's Qwen 3.7 Max is the largest and most capable model in the Qwen3.7 series, a next-generation flagship built for the agent-centric era with deep strengths in programming, office and productivity tasks, and long-term autonomous execution, served via DashScope's OpenAI-compatible endpoint.

Qwen3.5 397b A17b

Alibaba

Alibaba's Qwen 3.5 is a 397B-parameter mixture-of-experts model with 17B active parameters, offering strong reasoning capabilities with efficient inference.

Qwen3 Max

Alibaba

Alibaba's Qwen 3 Max is a large language model with strong coding, reasoning, and multilingual capabilities, served via DashScope's OpenAI-compatible endpoint.

PhoneLLM is an open model tuned for voice agents. The model is based on Nemotron Nano 30B base capabilities and excels at typical voice agent tasks with thinking disabled.

pparticleText Generation Particle-1 is Particle AI's reasoning model for chat, agentic coding, and tool use. It accepts text and images, with a 1,048,576-token context window, streaming responses, and configurable reasoning effort.

Pareto

Unbiased

uunbiasedText Generation Pareto is Unbiased's blended AI model. It engages multiple language models in parallel for each request, synthesizes one answer, and supports text and vision inputs through a single API response.

Nemotron 3 120b A12b

NVIDIA

NVIDIA Nemotron 3 Super is a hybrid MoE model with leading accuracy for multi-agent applications and specialized agentic AI systems.

Inkling 256k

Thinkingmachines

The 256K-context variant of Inkling, Thinking Machines' open-weights hybrid reasoning MoE model. Same hybrid reasoning, tool-use, and streaming support as the base model, with an extended context window for longer conversations and documents. Currently intended for low-traffic testing and internal use rather than high-throughput production deployments.

Inkling 2 Small

Thinkingmachines

Inkling 2 Small is a multimodal Mixture-of-Experts model from Thinking Machines Lab with 594B total and 18B active parameters and up to 1M tokens of context. It accepts text and image inputs and generates text, with built-in reasoning and tool calling.

Inkling

Thinkingmachines

Inkling is Thinking Machines' open-weights hybrid reasoning model, built on a mixture-of-experts architecture. It reasons by default, exposing its chain-of-thought as leading thinking content blocks, with reasoning effort tunable via a Tinker-specific output\_config.effort parameter. Available through Tinker's beta Anthropic Messages-compatible endpoint alongside tool use, streaming, and multi-turn conversations. Currently intended for low-traffic testing and internal use rather than high-throughput production deployments; prompt caching, citations, and audio input are not supported through this endpoint.

Granite 4.0 H Micro

IBM Granite

Granite 4.0 instruct models deliver strong performance across benchmarks, achieving industry-leading results in key agentic tasks like instruction following and function calling. These efficiencies make the models well-suited for a wide range of use cases like retrieval-augmented generation (RAG), multi-agent workflows, and edge deployments.

SEA-LION stands for Southeast Asian Languages In One Network, which is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.

Ember 1

Fireworks

Fireworks' Ember-1 is a specialized long-context multimodal reasoning model built on Kimi K3. It produces shorter reasoning traces using approximately 40% fewer tokens while maintaining comparable quality across Fireworks' evaluations.

Deepseek v4.1 Flash 1017

DeepSeek

We introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. The model natively processes images and text, and generates text autoregressively.

Deepseek v4 Pro 0813

DeepSeek

DeepSeek V4 Pro is a high-capability reasoning model from DeepSeek with a one million token context window, built for long-horizon agentic workflows and complex, multi-step problem-solving

Deepseek v4 Flash 0731

DeepSeek

DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities.

Deepseek R1 Distill Qwen 32b

DeepSeek

DeepSeek-R1-Distill-Qwen-32B is a model distilled from DeepSeek-R1 based on Qwen2.5. It outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

Clef-omni is a multimodal decision model built on a 30B-parameter mixture-of-experts (3B active) backbone. It turns a state and a schema of typed questions into decisions, reads the state as text, JSON, images, audio, or video, and returns a probability for every allowed option of every question.

Clef-flash is a fast 9B multimodal decision model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, or video, and returns a probability for every allowed option of every question.

Clef is a 27B multimodal decision model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, or video, and returns a probability for every allowed option of every question.

Clairic is a System One-style model that can extract, compute, estimate, and forecast values from inputs. It is strongly typed and outputs confidence scores and probabilities against its outputs. This model is in early research preview from the Cloudflare Workers AI team.