Cloud LLM Providers

Short list of LLM providers

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Using LLMs is not very expensive, might be no need to buy new awesome GPU. Here is a list if LLM providers in the cloud with LLMs they host.

Shop door in the cloud

LLM providers - Original

Anthropic LLM Models

Anthropic has developed a family of advanced large language models (LLMs) under the “Claude” brand. These models are designed for a wide range of applications, emphasizing safety, reliability, and interpretability.

Key Claude Model Variants

Model Strengths Use Cases
Haiku Speed, efficiency Real-time, lightweight tasks
Sonnet Balanced capability & performance General-purpose applications
Opus Advanced reasoning, multimodal Complex, high-stakes tasks

All models in the Claude 3 family can process both text and images, with Opus demonstrating particularly strong performance in multimodal tasks.

Technical Foundations

  • Architecture: Claude models are generative pre-trained transformers (GPTs), trained to predict the next word in large volumes of text and then fine-tuned for specific behaviors.
  • Training Methods: Anthropic uses a unique approach called Constitutional AI, which guides models to be helpful and harmless by having them self-critique and revise responses based on a set of principles (a “constitution”). This process is further refined using reinforcement learning from AI feedback (RLAIF), where AI-generated feedback is used to align the model’s outputs with the constitution.

Interpretability and Safety

Anthropic invests heavily in interpretability research to understand how its models represent concepts and make decisions. Techniques like “dictionary learning” help map internal neuron activations to human-interpretable features, allowing researchers to trace how the model processes information and makes decisions. This transparency is intended to ensure that models behave as intended and to identify potential risks or biases.

Enterprise and Practical Applications

Claude models are deployed in various enterprise scenarios, including:

  • Customer service automation
  • Operations (information extraction, summarization)
  • Legal document analysis
  • Insurance claims processing
  • Coding assistance (generation, debugging, code explanation)

These models are available through platforms such as Amazon Bedrock, making them accessible for integration into business workflows.

Research and Development

Anthropic continues to advance the science of AI alignment, safety, and transparency, aiming to build models that are not only powerful but also trustworthy and aligned with human values.

In summary, Anthropic’s Claude models represent a leading approach in LLM development, combining state-of-the-art capabilities with a strong focus on safety, interpretability, and practical enterprise use.

OpenAI LLM Models (2025)

OpenAI offers a comprehensive suite of large language models (LLMs), with the latest generations emphasizing multimodality, extended context, and specialized capabilities for coding and enterprise tasks. The primary models available as of May 2025 are outlined below.

Key OpenAI LLMs

Model Release Date Multimodal Context Window Specialization API/ChatGPT Availability Fine-Tuning Notable Benchmarks/Features
GPT-3 Jun 2020 No 2K tokens Text generation API only Yes MMLU ~43%
GPT-3.5 Nov 2022 No 4K–16K tokens Chat, text tasks ChatGPT Free/API Yes MMLU 70%, HumanEval ~48%
GPT-4 Mar 2023 Text+Image 8K–32K tokens Advanced reasoning ChatGPT Plus/API Yes MMLU 86.4%, HumanEval ~87%
GPT-4o (“Omni”) May 2024 Text+Image+Audio 128K tokens Multimodal, fast, scalable ChatGPT Plus/API Yes MMLU 88.7%, HumanEval ~87.8%
GPT-4o Mini Jul 2024 Text+Image+Audio 128K tokens Cost-efficient, fast API Yes MMLU 82%, HumanEval 75.6%
GPT-4.5 Feb 2025* Text+Image 128K tokens Interim, improved accuracy API (preview, deprecated) No MMLU ~90.8%
GPT-4.1 Apr 2025 Text+Image 1M tokens Coding, long-context API only Planned MMLU 90.2%, SWE-Bench 54.6%
GPT-4.1 Mini Apr 2025 Text+Image 1M tokens Balanced performance/cost API only Planned MMLU 87.5%
GPT-4.1 Nano Apr 2025 Text+Image 1M tokens Economy, ultra-fast API only Planned MMLU 80.1%

*GPT-4.5 was a short-lived preview, now deprecated in favor of GPT-4.1.

Model Highlights

  • GPT-4o (“Omni”): Integrates text, vision, and audio input/output, offering near real-time responses and a 128K-token context window. It is the current default for ChatGPT Plus and API, excelling in multilingual and multimodal tasks.
  • GPT-4.1: Focuses on coding, instruction-following, and extremely long context (up to 1 million tokens). It is API-only as of May 2025, with fine-tuning planned but not yet available.
  • Mini and Nano Variants: Provide cost-effective, latency-optimized options for real-time or large-scale applications, trading off some accuracy for speed and price.
  • Fine-Tuning: Available for most models except the very latest (e.g., GPT-4.1 as of May 2025), allowing businesses to customize models for specific domains or tasks.
  • Benchmarks: Newer models consistently outperform older ones on standard tests (MMLU, HumanEval, SWE-Bench), with GPT-4.1 setting new records in coding and long-context understanding.

Use Case Spectrum

  • Text Generation & Chat: GPT-3.5, GPT-4, GPT-4o
  • Multimodal Tasks: GPT-4V, GPT-4o, GPT-4.1
  • Coding & Developer Tools: GPT-4.1, GPT-4.1 Mini
  • Enterprise Automation: All, with fine-tuning support
  • Real-Time, Cost-Efficient Applications: Mini/Nano variants

OpenAI’s LLM ecosystem in 2025 is highly diversified, with models tailored for everything from simple chat to advanced multimodal reasoning and large-scale enterprise deployment. The latest models (GPT-4o, GPT-4.1) push the boundaries in context length, speed, and multimodal integration, while Mini and Nano variants address cost and latency for production use.

MistralAI LLM Models (2025)

MistralAI has rapidly expanded its portfolio of large language models (LLMs), offering both open-source and commercial solutions that emphasize multilingual, multimodal, and code-centric capabilities. Below is an overview of their major models and their distinguishing features.

Model Name Type Parameters Specialization Release Date
Mistral Large 2 LLM 123B Multilingual, reasoning July 2024
Mistral Medium 3 LLM Frontier-class Coding, STEM May 2025
Pixtral Large Multimodal LLM 124B Text + Vision Nov 2024
Codestral Code LLM Proprietary Code generation Jan 2025
Mistral Saba LLM Proprietary Middle East, South Asian Lang. Feb 2025
Ministral 3B/8B Edge LLM 3B/8B Edge/phones Oct 2024
Mistral Small 3.1 Small LLM Proprietary Multimodal, efficient Mar 2025
Devstral Small Code LLM Proprietary Code tool use, multi-file May 2025
Mistral 7B Open Source 7B General-purpose 2023–2024
Codestral Mamba Open Source Proprietary Code, mamba 2 arch. Jul 2024
Mathstral 7B Open Source 7B Mathematics Jul 2024

Premier & Commercial Models

  • Mistral Large 2: The flagship model as of 2025, featuring 123 billion parameters and a 128K-token context window. It supports dozens of languages and over 80 coding languages, excelling at advanced reasoning and multilingual tasks.
  • Mistral Medium 3: Released in May 2025, this model balances efficiency and performance, particularly strong in coding and STEM-related tasks.
  • Pixtral Large: A 124-billion-parameter multimodal model (text and vision), released in November 2024, designed for tasks requiring both language and image understanding.
  • Codestral: Specialized for code generation and software engineering, with the latest version released in January 2025. Codestral is optimized for low-latency, high-frequency coding tasks.
  • Mistral Saba: Focused on languages from the Middle East and South Asia, released in February 2025.
  • Mistral OCR: An optical character recognition service launched in March 2025, enabling extraction of text and images from PDFs for downstream AI processing.

Edge and Small Models

  • Les Ministraux (Ministral 3B, 8B): A family of models optimized for edge devices, balancing performance and efficiency for deployment on phones and resource-constrained hardware.
  • Mistral Small: A leading small multimodal model, with v3.1 released in March 2025, designed for efficiency and edge use cases.
  • Devstral Small: A state-of-the-art coding model focused on tool use, codebase exploration, and multi-file editing, released May 2025.

Open Source and Specialized Models

  • Mistral 7B: One of the most popular open-source models, widely adopted and fine-tuned by the community.
  • Codestral Mamba: The first open-source “mamba 2” model, released July 2024.
  • Mistral NeMo: A powerful open-source model, released July 2024.
  • Mathstral 7B: An open-source model specialized for mathematics, released July 2024.
  • Pixtral (12B): A smaller multimodal model for both text and image understanding, released September 2024.

Supporting Services

  • Mistral Embed: Provides state-of-the-art semantic text representations for downstream tasks.
  • Mistral Moderation: Detects harmful content in text, supporting safe deployment.

MistralAI’s models are accessible via API and open-source releases, with a strong focus on multilingual, multimodal, and code-centric applications. Their open-source approach and partnerships have fostered rapid innovation and broad adoption across the AI ecosystem.

Meta LLM Models (2025)

Meta’s large language model (LLM) family, known as Llama (Large Language Model Meta AI), is one of the most prominent open-source and research-driven AI ecosystems. The latest generation, Llama 4, marks a significant leap in capability, scale, and modality.

Model Parameters Modality Architecture Context Window Status
Llama 4 Scout 17B (16 experts) Multimodal MoE Unspecified Released
Llama 4 Maverick 17B (128 experts) Multimodal MoE Unspecified Released
Llama 4 Behemoth Unreleased Multimodal MoE Unspecified In training
Llama 3.1 405B Text Dense 128,000 Released
Llama 2 7B, 13B, 70B Text Dense Shorter Released

Latest Llama 4 Models

  • Llama 4 Scout:

    • 17 billion active parameters, 16 experts, mixture-of-experts (MoE) architecture
    • Natively multimodal (text and vision), open-weight
    • Fits on a single H100 GPU (with Int4 quantization)
    • Designed for efficiency and broad accessibility

  • Llama 4 Maverick:

    • 17 billion active parameters, 128 experts, MoE architecture
    • Natively multimodal, open-weight
    • Fits on a single H100 host
    • Greater expert diversity for enhanced reasoning

  • Llama 4 Behemoth (preview):

    • Not yet released, serves as a “teacher” model for the Llama 4 series
    • Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks (e.g., MATH-500, GPQA Diamond)
    • Represents Meta’s most powerful LLM to date

Key Features of Llama 4:

  • First open-weight, natively multimodal models (text and images)
  • Unprecedented context length support (details not specified, but designed for long-form tasks)
  • Built using advanced mixture-of-experts architectures for efficiency and scalability

Llama 3 Series

  • Llama 3.1:

    • 405 billion parameters
    • 128,000-token context window
    • Trained on over 15 trillion tokens
    • Supports multiple languages (eight added in the latest version)
    • Largest open-source model released to date

  • Llama 3.2 and 3.3:

    • Successive improvements and deployments, including specialized use cases (e.g., Llama 3.2 deployed on the International Space Station)

  • Llama 2:

    • Earlier generation, available in 7B, 13B, and 70B parameter versions
    • Still widely used for research and production

Open Source and Ecosystem

  • Meta maintains a strong commitment to open-source AI, providing models and libraries for developers and researchers.
  • Llama models power many AI features across Meta’s platforms and are widely adopted in the broader AI community.

In summary:
Meta’s Llama models have evolved into some of the world’s most advanced, open, and multimodal LLMs, with Llama 4 Scout and Maverick leading the way in efficiency and capability, and Llama 3.1 setting records for open-source scale and context length. The ecosystem is designed for broad accessibility, research, and integration across diverse use cases.

Qwen LLM Models (2025)

Qwen is Alibaba’s family of large language models (LLMs), notable for their open-source availability, strong multilingual and coding capabilities, and rapid iteration. The Qwen series now includes several major generations, each with distinct strengths and innovations.

Generation Model Types Parameters Key Features Open Source
Qwen3 Dense, MoE 0.6B–235B Hybrid reasoning, multilingual, agent Yes
Qwen2.5 Dense, MoE, VL 0.5B–72B Coding, math, 128K context, VL Yes
QwQ-32B Dense 32B Math/coding focus, 32K context Yes
Qwen-VL Vision-Language 2B–72B Text + image inputs Yes
Qwen-Max MoE Proprietary Complex, multi-step reasoning No

Latest Generations and Flagship Models

  • Qwen3 (April 2025)

    • Represents Alibaba’s most advanced LLMs to date, with major improvements in reasoning, instruction following, tool use, and multilingual performance.
    • Available in both dense and Mixture-of-Experts (MoE) architectures, with parameter sizes ranging from 0.6B to 235B.
    • Introduces “hybrid reasoning models” that can switch between “thinking mode” (for complex reasoning, math, and code) and “non-thinking mode” (for fast, general chat).
    • Superior performance in creative writing, multi-turn dialogue, and agent-based tasks, with support for over 100 languages and dialects.
    • Open weights are available for many variants, making Qwen3 highly accessible for developers and researchers.

  • Qwen2.5 (January 2025)

    • Released in a wide range of sizes (0.5B to 72B parameters), suitable for both mobile and enterprise applications.
    • Trained on an 18-trillion-token dataset, with a context window up to 128,000 tokens.
    • Major upgrades in coding, mathematical reasoning, multilingual fluency, and efficiency.
    • Specialized models like Qwen2.5-Math target advanced math tasks.
    • Qwen2.5-Max is a large-scale MoE model, pretrained on over 20 trillion tokens and fine-tuned with SFT and RLHF, excelling at complex, multi-step tasks.

  • QwQ-32B (March 2025)

    • Focuses on mathematical reasoning and coding, rivaling much larger models in performance while being computationally efficient.
    • 32B parameter size, 32K token context window, open-sourced under Apache 2.0.

Multimodal and Specialized Models

  • Qwen-VL Series

    • Vision-language models (VL) that integrate a vision transformer with the LLM, supporting text and image inputs.
    • Qwen2-VL and Qwen2.5-VL offer parameter sizes from 2B to 72B, with most variants open-sourced.

  • Qwen-Max

    • Delivers top inference performance for complex and multi-step reasoning, available via API and online platforms.

Model Availability and Ecosystem

  • Qwen models are open-sourced under the Apache 2.0 license (except for some of the largest variants) and are accessible via Alibaba Cloud, Hugging Face, GitHub, and ModelScope.
  • The Qwen family is widely adopted across industries, including consumer electronics, gaming, and enterprise AI, with over 90,000 enterprise users.

Key Features Across the Qwen Family

  • Multilingual mastery: Supports 100+ languages, excelling in translation and cross-lingual tasks.
  • Coding and math: Leading performance in code generation, debugging, and mathematical reasoning, with specialized models for these domains.
  • Extended context: Context windows up to 128,000 tokens for detailed, long-form tasks.
  • Hybrid reasoning: Ability to switch between modes for optimal performance in both complex and general-purpose tasks.
  • Open-source leadership: Many models are fully open-sourced, fostering rapid community adoption and research.

In summary:
Qwen models are at the forefront of open-source LLM development, with Qwen3 and Qwen2.5 offering state-of-the-art reasoning, multilingual, and coding abilities, broad model size coverage, and strong industry adoption. Their hybrid reasoning, large context windows, and open availability make them a leading choice for both research and enterprise applications.

LLM providers - Resellers

Amazon AWS Bedrock LLM Models (2025)

Amazon Bedrock is a fully managed, serverless platform that provides access to a wide selection of leading large language models (LLMs) and foundation models (FMs) from both Amazon and top AI companies. It is designed to simplify the integration, customization, and deployment of generative AI in enterprise applications.

Supported Model Providers and Families

Amazon Bedrock offers one of the broadest selections of LLMs available, including models from:

  • Amazon (Nova series)
  • Anthropic (Claude)
  • AI21 Labs (Jurassic)
  • Cohere
  • Meta (Llama)
  • Mistral AI
  • DeepSeek (DeepSeek-R1)
  • Stability AI
  • Writer
  • Luma
  • Poolside (coming soon)
  • TwelveLabs (coming soon)

This diversity allows organizations to mix and match models for their specific needs, with the flexibility to upgrade or switch models with minimal code changes.

Amazon’s Own Models: Nova

  • Amazon Nova is the latest generation of Amazon’s foundation models, designed for high performance, efficiency, and enterprise integration.
  • Nova models support text, image, and video inputs, and excel at Retrieval Augmented Generation (RAG) by grounding responses in proprietary company data.
  • They are optimized for agentic applications, enabling complex, multi-step tasks that interact with organizational APIs and systems.
  • Nova supports custom fine-tuning and distillation, letting customers create private, tailored models based on their own labeled datasets.

Third-Party and Specialized Models

  • DeepSeek-R1: A high-performance, fully managed LLM for advanced reasoning, coding, and multilingual tasks, now available on Bedrock.
  • Meta Llama, Anthropic Claude, AI21 Jurassic, Mistral, Cohere, and others: Each brings unique strengths in language, coding, reasoning, or multimodality, covering a wide range of enterprise and research use cases.
  • Marketplace: The Bedrock Marketplace offers over 100 popular, emerging, and specialized FMs accessible via managed endpoints.

Customization and Adaptation

  • Fine-Tuning: Bedrock enables private fine-tuning of models with your own data, creating a secure, customized copy for your organization. Your data is not used to retrain the base model.
  • Retrieval Augmented Generation (RAG): Bedrock’s Knowledge Bases allow you to enrich model responses with contextual, up-to-date company data, automating the RAG workflow for both structured and unstructured data.
  • Distillation: Transfer knowledge from large teacher models to smaller, efficient student models for cost-effective deployment.

Model Evaluation

  • LLM-as-a-Judge: Bedrock offers a model evaluation tool where you can benchmark and compare models (including those outside Bedrock) using LLMs as evaluators. This helps select the best model for specific quality and responsible AI criteria.

Deployment and Security

  • Serverless and Scalable: Bedrock handles infrastructure, scaling, and security, letting organizations focus on application logic.
  • Security and Compliance: Data is encrypted in transit and at rest, with compliance for ISO, SOC, HIPAA, CSA, and GDPR standards.

In summary:
Amazon Bedrock provides a unified, secure platform to access, customize, and deploy a wide array of leading LLMs—including Amazon’s own Nova models and best-in-class third-party FMs—supporting fine-tuning, RAG, and advanced evaluation tools for enterprise-grade generative AI applications.

Groq LLM Models (2025)

Groq is not an LLM developer itself, but a hardware and cloud inference provider specializing in ultra-fast, low-latency deployment of leading large language models (LLMs) using its proprietary Language Processing Unit (LPU) technology. GroqCloud™ enables developers to run a variety of state-of-the-art, openly available LLMs at unprecedented speed and efficiency.

Supported LLMs on GroqCloud

As of 2025, GroqCloud offers high-performance inference for a growing list of top LLMs, including:

  • Meta Llama 3 (8B, 70B)
  • Mistral Mixtral 8x7B SMoE
  • Google Gemma 7B
  • DeepSeek
  • Qwen
  • Whisper (speech-to-text)
  • Codestral, Mamba, NeMo, and others

GroqCloud is regularly updated to support new and popular open-source and research models, making it a versatile platform for developers and enterprises.

Key Features and Advantages

  • Ultra-Low Latency: Groq’s LPU-based inference engine delivers responses in real time, with benchmarks showing significant speed advantages over traditional GPU-based inference.
  • OpenAI API Compatibility: Developers can switch from OpenAI or other providers to Groq by changing just a few lines of code, thanks to API compatibility.
  • Scalability: Groq’s infrastructure is optimized for both small and large-scale deployments, supporting everything from individual developers to enterprise-grade applications.
  • Cost-Effectiveness: Groq offers competitive, transparent pricing for LLM inference, with options for free, pay-as-you-go, and enterprise tiers.
  • Regional Availability: GroqCloud operates globally, with major data centers such as the one in Dammam, Saudi Arabia, supporting worldwide demand.

Example Models and Pricing (as of 2025)

Model Context Window Pricing (per million tokens) Use Cases
Llama 3 70B 8K $0.59 (input) / $0.79 (output) General-purpose LLM
Llama 3 8B 8K $0.05 (input) / $0.10 (output) Lightweight tasks
Mixtral 8x7B SMoE 32K $0.27 (input/output) Multilingual, coding
Gemma 7B Instruct — $0.10 (input/output) Instruction following

Ecosystem and Integration

  • Groq powers platforms like Orq.ai, enabling teams to build, deploy, and scale LLM-based applications with real-time performance and reliability.
  • Easy migration from other providers due to API compatibility and extensive model support.

In summary:
Groq does not create its own LLMs but provides industry-leading, ultra-fast inference for a wide range of top open-source and research LLMs (e.g., Llama, Mixtral, Gemma, DeepSeek, Qwen) via GroqCloud. Its LPU hardware and cloud platform are valued for speed, scalability, cost efficiency, and developer-friendly integration.