Large Language Model Examples: 30+ LLMs Compared & How to Choose (2026)

TL;DR

• The 5 most commonly deployed LLMs in 2026: GPT-5.5 (OpenAI), Claude Opus 4.7 (Anthropic), Gemini 3.1 Pro (Google), Grok 4.3 (xAI), and Llama 4 (Meta)
• Open-weight models (Llama 4, DeepSeek V4 Pro, Mistral) offer lower TCO and on-prem deployment; proprietary models deliver stronger out-of-the-box performance and managed APIs
• Leading enterprises now route different tasks to different models instead of relying on a single provider
• Start with our evaluation framework  or decision matrix, or jump to your use case

Introduction

By 2030, the global Large Language Model (LLM) market is projected to reach $36.1 billion, highlighting how LLMs have emerged today as a core driver of business innovation.

Among the most popular large language model examples today are GPT, LLaMA, and Claude, which are already helping businesses streamline operations, automate tasks, and enhance customer experiences.

Learn more about which LLM is the most advanced today, their applications, and the benefits and limitations of their business usage.

The 2026 LLM Comparison Table: 30 Models at a Glance

The table below covers 30 LLMs across the metrics that matter most for enterprise evaluation. Prices reflect verified API list rates as of July 2026.

How to Choose an LLM for Enterprise: 5 Evaluation Criteria

Selecting the right large language model examples for your business requires careful evaluation to ensure they meet your operational and scalability needs. Here’s a five-step guide that our engineering team uses when scoping LLM projects for enterprise clients.

1. Deployment options (API vs. self-hosted vs. fine-tuned)

The right deployment model shapes your total cost, data privacy posture, and long-term flexibility. API access offers the fastest time to value; self-hosted models give you full data control; fine-tuning narrows a model to your specific domain.

2. Data privacy, GDPR, and compliance requirements

EU AI Act obligations, GDPR Article 22 on automated decision-making, and CCPA restrictions for California deployments are live concerns. 

When evaluating an LLM for enterprise, ask: 

• Does the provider offer EU data residency? 
• Is on-premises deployment available? 
• Are audit logs and data processing agreements (DPAs) part of the offering?

Models with EU data residency or full on-premises options: 

• Claude Opus 4.7 (Anthropic, AWS EU regions + Microsoft Foundry)
• Azure OpenAI (EU regions, BAA available)
• Llama 4 (self-hosted, full control)
• Mistral AI (EU-based provider, GDPR-native)
• Command R+ (Cohere, EU deployment available)

3. Cost-performance benchmarks for enterprise use cases

API pricing is only one line in the true total cost of ownership (TCO). A complete TCO calculation must include: 

• Token cost (input + output)
• Fine-tuning compute
• GPU infrastructure for self-hosted models
• Maintenance and retraining overhead
• Integration engineering time

Note that Gemini 3.1 Pro pricing doubles above 200K tokens, which is a meaningful consideration for long-document workloads.

4. Fine-tuning vs. RAG trade-offs by LLM

• Fine-tune when: the task is narrow and repetitive, the output format must be tightly controlled, and you have labelled training data available
• Use RAG when: the task is knowledge-intensive, the information needs to be current or changes frequently, or you want to avoid retraining costs as your data evolves

5. Latency and throughput benchmarks

Latency is an operational filter as much as an accuracy metric. The figures below are directional benchmarks based on publicly available data and our own project observations as of May 2026. Actual performance varies by prompt length, infrastructure, and load.

Large Language Model Examples by Enterprise Use Case: Profiles of 30 Models

Rather than listing models alphabetically, we have organised these large language model examples by the enterprise use cases they serve best.

Reasoning & analysis

Choosing the right reasoning model depends on how accurate you need it to be and what costs you can accept, not just where it ranks in benchmarks. Here’s a comparison of the top options.

GPT-5.5 (OpenAI)

GPT-5.5 is OpenAI’s current flagship, released in April 2026. Compared to other large language model examples, it is designed to handle more work itself: writing and debugging code, researching online, analysing data, creating documents and spreadsheets, and operating software autonomously.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $5/$30 per M tokens
• Best for: Agentic enterprise workflows, coding, research, data analysis
• Real-world example: Morgan Stanley uses GPT-4 (now migrating to GPT-5.x) to power its AI @ Morgan Stanley Assistant, which searches over 100,000 research reports and gives financial advisors instant, cited answers, increasing document retrieval efficiency from 20% to 80%. Over 98% of advisor teams actively use the tool.

Claude Opus 4.7 (Anthropic)

Claude Opus 4.7 is Anthropic’s latest flagship, released April 16, 2026. It scores 87.6% on SWE-bench Verified and 94.2% on GPQA Diamond.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $5/$25 per M tokens
• Best for: Long-horizon agentic coding, safety-critical workflows, software engineering
• Real-world example: Cursor, the AI-powered code editor, uses Claude Opus 4.7 at its highest tier for complex multi-file refactors and full-codebase reasoning. Cursor reports that Opus-class models handle architecture-level decisions that smaller models fail on, enabling developers to delegate entire feature implementations.

Learn more about the difference between Claude vs. ChatGPT.

Gemini 3.1 Pro (Google DeepMind)

Gemini 3.1 Pro is Google DeepMind’s frontier reasoning model, released in February 2026. It leads on ARC-AGI-2 abstract reasoning (77.1%) and GPQA Diamond (94.3%), and is the only major frontier model with native text, image, audio, and video input in a single model.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $2/$12 per M tokens (<200K); $4/$18 (>200K)
• Best for: Multimodal tasks, long-context document analysis, cost-sensitive frontier workloads
• Real-world example: Google integrated Gemini 3.1 Pro into NotebookLM, allowing users to upload full research corpora such as PDFs, audio recordings, and video transcripts. They can then query all of them simultaneously. Enterprise teams at Deloitte use NotebookLM for document-heavy due diligence workflows.

Grok 4.3 (xAI)

Grok 4.3 is xAI’s current flagship, offering a 1M token context window at $1.25 or $2.50 per million tokens, the most affordable pricing among frontier-tier models as of May 2026. 

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $1.25/$2.50 per M tokens
• Best for: Real-time data tasks, enterprise automation, business intelligence, cost-optimised reasoning
• Real-world example: xAI integrated Grok 4.3 into X Premium, where it provides real-time analysis of trending topics and market sentiment. Financial services firms use the API to monitor real-time social signals for trading intelligence workflows.

“For reasoning tasks, we find model choice matters less than people expect. When accuracy on structured extraction is non-negotiable, Claude Opus 4.7 is our default.” — Our engineering team.

Code generation & software engineering

Frontier models and lightweight open-weights options serve very different needs. Here’s which model from the large language model examples fits which coding workflow.

Claude Opus 4.7 (Anthropic)

Claude Opus 4.7 holds the highest reported score on SWE-bench Verified at 87.6% and leads Terminal-Bench 2.0 at 69.4%.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $5/$25 per M tokens
• Best for: Complex multi-file refactors, agentic coding pipelines, codebase-level reasoning
• Real-world example: Windsurf (formerly Codeium) routes its highest-complexity agentic coding tasks to Claude Opus 4.7. Rakuten Group, an early-access partner, reported that Opus 4.7 resolved 3x more production tasks end-to-end compared to Opus 4.6, particularly on tasks requiring reasoning across multiple files and test suites simultaneously.

GPT-5.5 (OpenAI)

GPT-5.5 is designed for autonomous software work: writing and debugging code, operating software, and completing tasks end-to-end across tools. 

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $5/$30 per M tokens
• Best for: Autonomous multi-step coding, GitHub Copilot agent mode, Microsoft ecosystem workflows
• Real-world example: GitHub Copilot’s highest-capability tier uses the GPT-5.x model family for its agent mode, which can autonomously open pull requests, write tests, and fix CI failures. GitHub reported that teams using Copilot agent mode complete feature-level tasks up to 55% faster than those using autocomplete-only assistance.

Get to know more about ChatGPT by checking out ChatGPT business stats.

Mistral Large 2 & Mistral 7B (Mistral AI)

Mistral Large 2 is a strong multilingual code model at a significantly lower price than the frontier tier. Its Apache-licensed sibling Mistral 7B is the reference open-weights model for cost-sensitive code generation.

• Context window: 128K tokens (Large 2) / 32K tokens (7B)
• Licence: Proprietary API (Large 2) / Apache 2.0 (7B)
• Pricing tier: $$ (Large 2) / Free self-hosted (7B)
• Best for: Multilingual code generation, edge deployment, high-volume batch inference, cost-sensitive tasks
• Real-world example: Mistral AI’s partnership with BNP Paribas, renewed for three years in 2026, covers deploying Mistral models across the bank’s European operations. This includes a customer-facing virtual assistant, Hello bank!’s HelloïZ chatbot, which has served over one million clients since January 2026, and an internal employee AI assistant for summarisation, translation, and document generation. BNP Paribas cited Mistral’s EU-based infrastructure and GDPR-native design as key reasons for choosing it over US-based providers.

“For code generation, the frontier models are genuinely differentiated from smaller ones on complex, multi-file tasks. — Our engineering team.”

Long-context document processing

Longest context window ≠ best reasoning quality. Use a large-context model for ingestion, a more precise one for structured extraction.

Llama 4 Scout (Meta AI)

Llama 4 Scout ships with a 10M token context window, the longest of any publicly available model.

• Context window: 10M tokens
• Licence: Open (Llama licence)
• Pricing tier: Free (self-hosted)
• Best for: On-premises long-document RAG, privacy-first knowledge pipelines, high-throughput batch inference
• Real-world example: A European legal-services firm deployed Llama 4 Scout self-hosted on-premises to process entire case file archives, including thousands of pages of precedents, contracts, and court filings, in a single inference pass. The 10M context window removed their chunking infrastructure and cut document-processing latency by over 60%.

Gemini 3.1 Pro (Google DeepMind)

Gemini 3.1 Pro supports a 1M token context window with native text, image, audio, and video input. It is the best commercial choice for workloads combining long documents with non-text content, such as annotated contract review or multimedia research synthesis.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $2/$12 per M tokens (<200K); $4/$18 (>200K)
• Best for: Multimodal long-context analysis, video + document workflows, annotated contract review
• Real-world example: Google integrated Gemini 3.1 Pro into NotebookLM Enterprise, used by teams at KPMG and Accenture for multi-source document synthesis. Analysts upload entire project document sets, including PDFs, slide decks, and meeting recordings. They query across all sources simultaneously, replacing manual cross-referencing workflows that took days.

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Claude Opus 4.7 (Anthropic)

While Llama 4 Scout leads on raw context length and Gemini 3.1 Pro on multimodal input, Claude Opus 4.7 is widely preferred for long-context tasks where reasoning quality and instruction-following accuracy matter most.

• Context window: 1M tokens
• Licence: Proprietary
• Pricing tier: $5/$25 per M tokens
• Best for: Structured clause extraction, regulatory document analysis, long-context reasoning with precision
• Real-world example: Harvey, the AI legal platform used by law firms including Allen & Overy, uses Claude as its primary model for contract analysis and due diligence. Lawyers report that Claude’s ability to reason over full contracts, tracking defined terms and cross-references across hundreds of pages, significantly reduces review time on complex M&A transactions.

Yi-34B (01.AI)

Yi-34B supports up to 200K tokens on an Apache 2.0 licence, making it one of the most capable open-weights options for organisations requiring long-context processing with full data residency.

• Context window: 200K tokens
• Licence: Apache 2.0
• Pricing tier: Free (self-hosted)
• Best for: Long-context multilingual document processing, on-premises deployment, Chinese-English bilingual workflows
• Real-world example: Several Chinese enterprise clients have deployed Yi-34B on-premises for bilingual contract analysis and regulatory compliance review, processing lengthy government procurement documents and financial disclosures that require simultaneous understanding of Chinese regulatory language and English technical specifications.

“Context window size and reasoning quality are different. We typically use the largest-context model available for ingestion and retrieval. Then we use a more precise model for structured extraction and Q&A over the retrieved chunks. — Our engineering team.”

RAG pipelines & knowledge retrieval

Model choice is only part of the RAG equation. Here are the models best suited to enterprise retrieval pipelines.

Cohere Command R+

Cohere Command R+ is purpose-built for enterprise RAG applications. It delivers contextually relevant and highly accurate responses in document retrieval workflows.

• Context window: 128K tokens
• Licence: Proprietary
• Pricing tier: $$
• Best for: Enterprise RAG pipelines, knowledge management, customer support automation, content moderation
• Real-world example: Cohere deployed its Rerank model inside Notion’s workspace search, applying re-ranking before the generative model processes each query to ensure users receive the most accurate and contextually relevant results. The deployment reduced operational costs linked to embedding, storage, and system complexity, while delivering more contextually relevant results than Notion’s previous keyword-based approach.

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Llama 4 Scout (Meta AI)

Llama 4 Scout’s 10M token context window makes conventional chunking largely optional for most enterprise document sets. For organisations building RAG pipelines where full data residency and zero per-token cost are requirements, it is the strongest open-weights choice in 2026.

• Context window: 10M tokens
• Licence: Open (Llama licence)
• Pricing tier: Free (self-hosted)
• Best for: On-premises RAG, privacy-first knowledge pipelines, high-volume document retrieval
• Real-world example: An insurance carrier deployed Llama 4 Scout self-hosted to power a claims knowledge assistant querying their full policy library, claims history, and regulatory guidance. These were previously three separate search systems. Consolidating onto a single 10M-context model eliminated the inter-system retrieval layer and cut query latency by 45%.

DeepSeek V4 Pro

DeepSeek V4 Pro is a 1.6T parameter MoE open-weights model with a 1M token context window under an MIT licence.

• Context window: 1M tokens
• Licence: MIT (open-weights)
• Pricing tier: $1.74/$3.48 per M tokens (or free self-hosted)
• Best for: Agentic coding, long-context RAG, cost-sensitive enterprise knowledge workflows
• Real-world example: Several European fintech firms adopted DeepSeek V4 Pro for self-hosted regulatory document analysis, citing the MIT licence for full customisation rights. One client reported a 70% reduction in inference cost compared to their previous GPT-4-based pipeline at equivalent accuracy on their compliance classification task.

“The retrieval layer is where most RAG projects succeed or fail, not the generation layer. We have seen more improvement from switching to hybrid retrieval than from switching LLMs. — Our engineering team.”

Learn more about RAG development services at Intelliarts.

Multilingual & global operations

Multilingual benchmark scores can obscure significant per-language variance. Here’s how the leading large language model examples compare across global use cases.

Qwen2.5-72B (Alibaba)

Qwen2.5-72B is an enterprise-focused LLM with strong multilingual processing, particularly across Asian language markets alongside English.

• Context window: 128K tokens
• Licence: Apache 2.0
• Pricing tier: Free (self-hosted)
• Best for: Multilingual ops, e-commerce, document automation, customer service across Asian and European languages
• Real-world example: AliExpress (Alibaba’s international e-commerce platform) uses Qwen models across its e-commerce ecosystem to power product recommendation engines and customer service workflows, leveraging Qwen’s strength across Chinese, English, and other Asian languages to adapt content for cultural context rather than performing word-for-word translation.

Mistral Large 2 (Mistral AI)

Built by a European AI company and GDPR-native by design, Mistral Large 2 offers competitive multilingual performance across European languages with strong data residency guarantees.

• Context window: 128K tokens
• Licence: Proprietary (API)
• Pricing tier: $$
• Best for: European multilingual enterprise, GDPR-sensitive deployments, EU regulatory compliance workflows
• Real-world example: Mistral AI’s longest-standing financial services partner BNP Paribas renewed their three-year partnership in 2026, deploying Mistral models for customer-facing virtual assistants and internal employee tools across the bank’s EU operations. BNP Paribas explicitly cited Mistral’s EU-based infrastructure and GDPR-native design as key reasons for choosing it over US-based providers.

Bloom (BigScience)

Bloom is a community-driven, open-weights model supporting a wide array of languages and dialects. It is optimised for academic research, large-scale multilingual content generation, and machine translation and localisation.

• Context window: 2K tokens
• Licence: RAIL licence
• Pricing tier: Free (self-hosted)
• Best for: Academic multilingual NLP research, ethical AI projects, low-resource language tasks
• Real-world example: Academic and research teams use BLOOM for low-resource NLP tasks in African languages where commercial models lack coverage. BLOOM’s training across 46 languages, including several African languages absent from GPT or Gemini, makes it the reference open-weighted option for multilingual research in underserved language communities. It has been used in university- and NGO-led initiatives focused on the preservation and digitisation of sub-Saharan African languages.

“Aggregate multilingual benchmark scores are almost always misleading for real deployments. A model that leads overall can underperform significantly on specific language pairs. We benchmark on the exact languages in scope before recommending anything. — Our engineering team.”

Open-source & on-premises deployment

Self-hosting changes the cost and control calculus significantly. Here are the strongest open-weights options for on-premises deployment.

Llama 4 (Meta AI)

The Llama 4 family is the strongest open-weights option for both RAG pipelines and reasoning tasks in 2026.

• Context window: Up to 10M tokens (Scout) / 1M tokens (Maverick)
• Licence: Open (Llama licence)
• Pricing tier: Free (self-hosted)
• Best for: On-premises reasoning, high-throughput RAG, privacy-first deployments
• Real-world example: AirAsia deployed its AVA virtual assistant providing customer service in 11 languages across Southeast Asian routes. By self-hosting, AirAsia eliminated per-query API costs at scale, handling millions of interactions monthly. This also kept all customer data within their own infrastructure, a regulatory requirement in several ASEAN markets.

DeepSeek V4 Pro

DeepSeek V4 Pro is a 1.6T parameter MoE model under an MIT licence, self-hostable at zero marginal cost. It scores 80.6% on SWE-bench and supports three reasoning modes for agentic coding and knowledge work.

• Context window: 1M tokens
• Licence: MIT (open-weights)
• Pricing tier: $1.74/$3.48 per M tokens (or free self-hosted)
• Best for: Agentic coding, knowledge work, cost-sensitive enterprise reasoning
• Real-world example: Several European fintech firms self-host DeepSeek V4 Pro for regulatory document analysis, citing the MIT licence for full customisation rights and 1.6T MoE architecture for frontier-grade reasoning. One client reported a 70% reduction in inference cost versus their previous GPT-4-based pipeline at equivalent accuracy.

DeepSeek R1

DeepSeek R1 delivers reasoning performance at roughly 27 times lower cost than comparable GPT-5.x tier models under an MIT licence. It has become the default option for cost-sensitive analytical workloads.

• Context window: 128K tokens
• Licence: MIT
• Pricing tier: $0.55/$2.19 per M tokens (or free self-hosted)
• Best for: Reasoning, math, scientific analysis, cost-sensitive enterprise inference
• Real-world example: A US-based quantitative research firm replaced their OpenAI o1-based financial modelling pipeline with DeepSeek R1, reporting equivalent accuracy on structured financial reasoning tasks at about 25x lower API cost, reducing their monthly AI inference spend from six figures to five figures.

Learn more about document data extraction using OpenAI GPT.

Mistral 7B (Mistral AI)

With just 7 billion parameters, Mistral 7B balances computational efficiency with strong language understanding. It is well-suited for code generation, automated content generation, and chatbots where reducing computational costs matters.

• Context window: 32K tokens
• Licence: Apache 2.0
• Pricing tier: Free (self-hosted)
• Best for: Edge deployment, high-volume batch inference, cost-sensitive code generation and classification
• Real-world example: A European e-commerce company deployed Mistral 7B on a single A10G GPU per region to power real-time product description generation for 2 million listings monthly. The self-hosted deployment costs about $0.30/hr per region, a fraction of the equivalent API cost, with sub-second generation latency that meets their page-load time requirements.

Falcon (Technology Innovation Institute)

Developed by the Technology Innovation Institute (TII) in Abu Dhabi, Falcon is a high-performance open-weights LLM built for optimised training and deployment.

• Context window: 2K tokens (Falcon 40B)
• Licence: Apache 2.0
• Pricing tier: Free (self-hosted)
• Best for: Customer support automation, document summarisation, data extraction, enterprise NLP
• Real-world example: The Abu Dhabi government deployed Falcon within its digital government initiative to power Arabic-language citizen services chatbots across multiple departments. The open-weights model allowed full customisation for Gulf Arabic dialect, integration with government data systems, and complete data sovereignty — all requirements that ruled out third-party API providers.

“The self-host-vs-API decision comes down to two things: data sensitivity and token volume. If the data can leave your perimeter and volumes are modest, managed API wins on simplicity and total cost. If data residency is required, or you are running hundreds of millions of tokens per month on a task a smaller model handles well, self-hosted open-weights models make economic sense. — Our engineering team.”

Which LLM for Which Enterprise Scenario: Decision Matrix

The table below maps the most common enterprise use cases to recommended models with May 2026 pricing in mind.

API vs. Self-Hosted vs. Fine-Tuned: Deployment Trade-Off Guide

How you deploy large language model examples is often as consequential as which model you choose. The three primary deployment modes each carry distinct trade-offs across cost, data control, latency, and engineering overhead.

We treat deployment mode selection as a discovery step. Before evaluating any model, we map three variables with every client: data sensitivity, expected token volume, and task specificity. These three answers almost always determine the right deployment architecture before model comparison.

If you are scoping an LLM deployment and are not yet sure which direction makes sense, talk to our LLM engineers, and we will give you a clear architecture recommendation based on your use case.

LLM Use Cases by Business Function

LLMs are core tools for business growth and efficiency. The large language model examples below cover the most impactful applications, each illustrated with a named real-world example and an outcome metric.

Operational Efficiency

LLMs enable businesses to automate complex workflows, like content generation, data analysis, and document processing, to increase efficiency and productivity. GPT-4 powers automated customer service agents for Duolingo Max, handling thousands of queries in different languages without human intervention.

Cost Reduction: 20–50% Operational Savings at Scale

Implementing AI automation with large language model examples reduces operational costs by minimising human intervention. Cohere helped Notion deploy their embedding model into workspace search, cutting costs related to embedding, storage, and system complexity.

DeepSeek R1 delivers reasoning performance at about 27 times lower cost than GPT-5.x tier models for similar tasks, greatly expanding the range of economically viable workloads.

Better Customer Experience: Faster, More Personalised Interactions

LLMs improve customer experience by enabling faster, more natural AI-driven interactions across multiple touchpoints. AirAsia’s virtual assistant, built on Meta’s LLaMA, offers customer service in over 15 languages, a scalability win that would otherwise require large multilingual support teams.

Scalability for Global Expansion

LLMs with multilingual capabilities are essential for businesses expanding globally without a full local workforce. AirAsia’s chatbot supports over 15 languages.

Google Translate, powered by large language model examples, supports more than 100, enabling seamless communication for e-commerce platforms, travel agencies, and global customer operations.

Improved Decision-Making: Predictive Insights at Scale

LLMs assist decision-makers across industries by summarising research papers, extracting key findings, and offering predictive insights. Pharmaceutical companies like Pfizer use ChatGPT to accelerate drug discovery, specifically for safety-related table summarisation in clinical study reports.

Tools like Brandwatch use LLM-powered sentiment analysis to help brands understand public opinion in real time.

Language modelling for writing assistance

LLMs enable grammar correction, sentence completion, and fluency improvement in machine-generated text. Writing assistance tools such as Grammarly apply this capability to ensure written content across education, marketing, and corporate communications is clear, professional, and error-free.

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Final take

As LLM platforms continue to expand in capability, they offer numerous solutions tailored to various requirements. In 2025, selecting the right LLM model and an experienced tech partner is crucial to ensure your AI strategy remains competitive. With rapidly evolving technology, leveraging the best language model can optimize processes, reduce costs, and improve decision-making.

Choosing the right model is key to staying ahead, as it directly influences how effectively your business can use AI for specific tasks and scale over time. Use the provided list of LLMs to guide your choice of an option that would benefit your business the most.

At Intelliarts, we bring over 24 years of experience in delivering cutting-edge AI/ML solutions. Our team of experts is dedicated to ensuring that your integration of LLMs drives measurable results and keeps you at the forefront of your industry. Let the Intelliarts team make an impact on your project.