10+ Large Language Model Examples – Benchmark & Use Cases

We have used open-source benchmarks to compare top proprietary and open-source large language model (LLM) examples. You can choose your use case to find the right model for it.

Comparison of the most popular large language models

We have created a model scoring system using 3 metrics: User preference, coding, and reliability. You can also see the price graph with respect to the final score of the model. You can adjust the criterion weights by using the sliders on top of the graph according to your needs:

User preference: This metric is based on the Elo score. Elo score is a widely used technique in various areas that need ranking. It originates from chess, and when a player outranks the other, they gain more scores. We have obtained this data from Chatbot Arena, which includes many users. ¹

Coding: The coding metric indicates the code generation abilities of the LLM rated by users of OpenLM.ai.²

Reliability: The reliability metric refers to the hallucination scores of the study conducted by Vectera. They use the Hughes Hallucination Model Evaluation to find out how often a model introduces hallucinations when summarizing a document. ³

API cost is given for 1000000 input and output tokens per API call for 1 API call.

You can see our methodology and further information on evaluation.

Examples of leading large language models

The first 11 models were sorted according to the default comparison scores, and the rest were sorted alphabetically by the developer names.

The table features vendors selected from the most popular general-purpose LLMs as they can handle various topics and tasks. Specialized AI tools were excluded since they are designed for specific functions and fall outside the scope of this broad evaluation. Additionally, we provide information on models available only by subscription.

For further insights, you can explore comparisons of current and popular models, including an overview of Large Multimodal Models (LMMs) and how they differ from LLMs, as well as a detailed analysis of the Top 30+ Conversational AI platforms.

Detailed analysis of popular models

1. OpenAI GPT-4.5 and GPT-4o

GPT-4.5 is the latest, largest, and most capable GPT model yet, designed for complex and high-performance tasks. Whereas GPT-4o focuses on efficiency, maintaining strong capabilities while reducing computational costs.

• Multimodal capabilities: Both models handle text and images, allowing for tasks like captioning, diagram interpretation, and creating alt-text for accessibility. This makes them versatile for different applications.
• Efficiency: GPT-4o handles large tasks with few resources, providing efficiency at scale. It is economical for business applications because it strikes a balance between performance and processing optimization.
• Processing capacity: GPT-4.5 and GPT-4o both have a text handling capacity of 25,000 words, but they require different computational resources.

GPT-4.5 excels at complex text processing requiring significant power, while GPT-4o prioritizes resource efficiency and real-time performance with reduced latency.

2. Claude 3.7 Sonnet, 3.0 Opus & 3.5 Haiku

Claude 3 is Anthropic’s AI transformer model. It offers three model tiers: Claude Sonnet, Claude Opus, and Claude Haiku. 

• Claude 3.7 Sonnet: Anthropic’s highest level of intelligence and capability, with toggleable extended thinking, marks the first model from Anthropic claiming extended thinking abilities. This allows for deeper, more sustained reasoning over extended periods or complex problems, enhancing decision-making and problem-solving.
• Claude 3 Opus: The expensive and powerful Opus is recommended for work automation, research support, and data processing. Opus specializes in AI vision, making it an ideal alternative for enterprises that require in-depth AI capabilities.
• Claude 3.5 Haiku: Haiku is claimed to have blazing-fast intelligence, making it the fastest model from Anthropic. It is highly recommended for translation, editorial management, and unstructured data processing tasks.

3. Gemini

Google’s latest model, Gemini 2.0 Flash, was released in February 2025. The free edition includes all of the essential features, such as text-based prompting, the ability to upload and create photos, and the ability to search Google apps and services.

The commercial version, Gemini Advanced, provides more comprehensive features:

• Advanced version of the AI model, suitable for higher-level tasks (i.e., data analysis)
• Ability to maintain longer chats
• Ability to utilize Gemini within Google apps such as Gmail and Docs
• 2 TB of storage

Gemini 2.0 Pro: Google claims that the Pro series is its best model yet for coding performance and complex prompts.

Gemini Ultra 1.0: This is Google’s largest model, specifically optimized for high-quality output for complex tasks. Ultra also claims to be a reasoning model.

DeepSeek-R1

DeepSeek-R1 is DeepSeek-AI’s latest reasoning-focused large language model (LLM) built on a transformer architecture. It incorporates multi-stage training, reinforcement learning (RL), and cold-start data for enhanced reasoning.

Versions:

• DeepSeek-R1-Zero: RL-trained without supervised fine-tuning, excelling in reasoning but with readability challenges.
• DeepSeek-R1: Improved with multi-stage training, rivaling GPT-4-level models.

Additionally, six distilled models (1.5B–70B parameters) based on Qwen and Llama cater to different computational needs.

Qwen (Alibaba Cloud)

Qwen models scale data and model size for advanced AI applications. The latest release, Qwen2.5-Max, uses a Mixture of Experts (MoE) and is pre-trained on 20T+ tokens with RLHF and SFT.

Versions:

• Qwen2.5-Max – Optimized for reasoning, coding, and general AI.
• Qwen2.5-72B – A strong-performing dense model.
• Qwen2.5-405B – One of the largest open-weight dense models.

Llama 3 (Meta AI)

Llama 3.3 features transformer-based pretraining and instruction fine-tuning.

Versions:

• 10B, 100B, 200B – Standard models.
• 10B-Instruct, 100B-Instruct, 200B-Instruct – Fine-tuned for better chatbot performance.

Each LLM series is optimized for different AI applications, offering powerful reasoning and efficiency improvements.

Evaluation method

We created our evaluation metrics based on enterprises’ needs. We have used user preference and coding scores from OpenLM’s Chatbot Arena and the reliability score from Vectera’s study to achieve consistent results. Although we have used external sources for the metrics now, we plan to use our benchmarks for different categories in the future.

We used min-max normalization for our scoreboard in this evaluation since all of these scores had different evaluation intervals. This means that the highest scoring model got 100%, and the lowest scoring model got 0% for the specific metric.

Benchmarks

Our researchers have conducted benchmarks for different metrics:

• For top coding models, we used 100 math problems suitable for an advanced high school student. These problems assess both logical reasoning and coding skills.
• For the most reliable models, we assessed an LLM’s reliability in retrieving precise numerical value answers from news stories on various topics; the answers were fact-checked against ground truth to ensure accuracy in exact figures rather than generalizations.
• Our AI reasoning benchmark tested 100 mathematics questions in a zero-shot setting, meaning no example questions were used for training. It evaluated reasoning models and compared them with non-reasoning models to highlight their differences.

LLM pricing

We have an article to help you understand the pricing methods of LLMs. Pricing structures vary by provider, but per-token pricing is the most common. To assist with cost estimation, our LLM API Price Calculator allows you to input your token volume needs and sort results by input cost, output cost, and total cost. This tool provides a clear breakdown of pricing based on usage, enabling informed decision-making.

Real-life use cases for large language models (LLMs)

Here are key use cases of LLM models with examples. To learn more about generative AI, see Generative AI applications.

1. Content creation and generation

• Writing assistance: LLMs can help draft, edit, and enhance written content, from blog posts to research papers, by suggesting improvements or generating text based on prompts. 
  • Real-life example: Grammarly uses LLMs to suggest grammar, punctuation, and style improvements for users, enhancing the quality of their writing.⁴
    
• Creative writing: Generate poetry, stories, or scripts based on creative prompts, aiding writers in brainstorming or completing their projects.
  • Real-life example: AI Dungeon, powered by OpenAI’s GPT-4, has a story mode that allows users to create and explore interactive stories, offering creative narratives.⁵
    
• Marketing content creation: Create compelling marketing content, including product descriptions, social media posts, and advertisements, tailored to specific audiences.
  • Real-life example: Copy.ai, an AI content generator, uses LLMs to generate marketing content, including social media posts, product descriptions, and email campaigns.⁶
    
• Language translation: Translate text between different languages while preserving context and meaning.
  • Real-life example: DeepL Translator uses LLM models trained on linguistic data for language translation⁷

2. Customer support and chatbots

• Automated customer service: LLMs power chatbots that can handle customer inquiries, troubleshoot issues, and provide product recommendations in real time.
  • Real-life example: Bank of America uses the AI chatbot Erica, powered by LLMs, to assist customers with tasks like checking balances, making payments, and providing financial advice.⁸
    
• Virtual assistants: LLMs enable virtual assistants to respond to user queries, manage tasks, and control smart devices.
  • Real-life examples: Amazon’s Alexa and Google Assistant both use LLMs to engage in two-way conversations; they are primarily available on home automation and mobile devices.^{9 10}
    
• Personalized responses: Generate personalized responses based on customer history and preferences, improving the overall customer experience.
  • Real-life example: Zendesk, a customer service platform, uses LLMs to provide tailored responses in customer support.¹¹

3. Software development

Language models can assist current developers and people who are learning to code on:

• Code writing: Assist developers by generating code snippets, providing suggestions, and writing entire functions or classes based on descriptive prompts.
  • Real-life example: Code Llama is a code-specialized LLM built by training on code-specific datasets. It can generate code and natural language prompts. It can create code by processing it using natural language. If a user asks “Write me a function that outputs the Fibonacci sequence.”, the LLM will create an output code based on the given prompt.¹²

Video: LLM-based code suggestions

Source: Meta¹³

• Bug detection and fixing: Analyze code to detect potential bugs and suggest fixes, streamlining the debugging process.
• Code documentation: Generate technical documentation, including API references, code comments, and user manuals, based on the source code.
  • Real-life example: TabNine, an AI code documentation tool, uses LLMs to update and revise documentation as code changes occur.¹⁴

4. Business intelligence 

• Data interpretation: Interpret complex datasets, providing narrative summaries and insights that are easier to interpret for non-technical stakeholders. The key practices include:
  • Insight generation
  • Data analysis
  • Story creation
    
• Report generation: Automatically generate business reports, financial summaries, and executive briefings from raw data and analytics.
  • Real-life example: Microsoft Research’s approach, GraphRAG, uses the LLM to create a knowledge graph based on the private dataset, helping businesses gain insights without needing deep technical expertise.¹⁵

5. Finance

• Financial risk assessment analysis: Assist in assessing financial risk by analyzing historical data, identifying patterns, and predicting potential market downturns.
  • Real-life example: Bloomberg GPT is an LLM specifically trained in financial data, helping analysts generate risk insights and forecasts from financial reports.¹⁶
    
• Fraud detection: Assist in identifying fraudulent activities by analyzing transaction patterns and generating alerts for suspicious behavior.
  • Real-life example: Feedzai employs LLMs to analyze transaction patterns and detect fraudulent activities.¹⁷

6. Healthcare

• Medical question–answering: LLMs can assist in patient triage by answering medical questions,
  • Real-life example: Med-PaLM, an LLM from Google Research, is designed to help readers examine findings from patient tests. Thus, the reader can pick the right answer for what disease, test, or treatment is most appropriate.¹⁸
    
• Drug research: Analyze and summarize scientific literature in pharmaceuticals and medicine.
  • Real-life example: BenevolentAI, an AI-enabled drug discovery and development company, employs LLMs to analyze scientific literature and identify potential drug candidates.¹⁹

7. Legal and compliance

• Contract analysis: Review and analyze legal documents, identifying key clauses, potential risks, and areas requiring attention.
  • Real-life example: Kira Systems uses LLMs to analyze and extract important information from legal contracts.²⁰
    
• Regulatory compliance: Automate monitoring compliance with regulations by analyzing and summarizing relevant legal texts.
  • Real-life example: Compliance.ai leverages LLMs to monitor the regulatory environment for relevant changes and maps them to your internal policies, procedures, and controls.²¹
    
• Legal research: Summarize case law, statutes, and legal opinions to assist lawyers and legal professionals in conducting research.
  • Real-life example: Casetext’s CARA uses LLMs to provide relevant case law and legal precedents based on the documents lawyers upload. Some practices include:
    • Find on-point cases on your facts and legal issues
    • Checking your documents for missing cases
    • Finding law cases opposing counsel missed²²

FAQ

Further reading

• Generative AI applications
• Large language model training

External Links

• 1. Chatbot Arena | OpenLM.ai.
• 2. Chatbot Arena | OpenLM.ai.
• 3. vectara/hallucination_evaluation_model · Hugging Face.
• 4. What Are Large Language Models? AI’s Linguistic Giants | Grammarly.
• 5. The Story Mode.
• 6. ” Automate tasks. Eliminate bloat.” Copy.ai. 2024. Retrieved on September 1, 2024.
• 7. DeepL's next-gen LLM outperforms ChatGPT-4, Google, and Microsoft for translation quality.
• 8. Erica® - Virtual Financial Assistant from Bank of America.
• 9. Amazon Alexa.
• 10. Google Assistant, your own personal Google. Google Assistant
• 11. Generative KI: Definition, Beispiele & Einsatzmöglichkeiten.
• 12. Introducing Code Llama, a state-of-the-art large language model for coding.
• 13. Introducing Code Llama, a state-of-the-art large language model for coding.
• 14. AI code documentation: Why documentation hurts and how AI helps - Tabnine. Tabnine
• 15. AI code documentation: Why documentation hurts and how AI helps - Tabnine. Tabnine
• 16. Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance | Press | Bloomberg LP.
• 17. About Us | Feedzai. Feedzai
• 18. Med-PaLM: A Medical Large Language Model - Google Research.
• 19. BenevolentAI | AI Drug Discovery | AI Pharma.
• 20. AI Contract Review Software | Kira | Litera.
• 21. Financial and Regulatory Compliance Software. Compliance.ai
• 22. What is CARA A.I. | Casetext.