10+ Large Language Model Examples & Benchmark
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 developed a model scoring system based on three key metrics: user preference, coding, and reliability. You can also view the price graph in relation to the model’s final score.
Reasoning: We used our AI reasoning benchmark to test 100 mathematics questions in a zero-shot setting, which means no example questions were used for training. The benchmark evaluated reasoning models and compared them to non-reasoning models to highlight their differences.
Coding: The coding metric indicates the code generation abilities of the LLM, rated by users of OpenLM.ai.1
Reliability: 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.
We developed our evaluation metrics with the needs of enterprises in mind. In this process, we utilized coding scores from OpenLM’s Chatbot Arena and applied min-max normalization to our scoreboard, as all scores had different evaluation intervals. This approach means that the highest-scoring model receives a score of 100%, while the lowest-scoring model gets a score of 0% for each specific metric.
The results from all three metrics have been proportioned to fall between 0 and 33.3, creating a total score of 100.
API cost is given for 1000000 input and output tokens per API call for 1 API call. 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.
Leading large language model examples
Developer | Model | Open Source |
|---|---|---|
OpenAI | GPT-5 | ❌ |
OpenAI | GPT-4.5-preview | ❌ |
xAI | Grok-3-preview | ✅ |
Google | Gemini-2.0-Flash | ❌ |
OpenAI | GPT-4o | ❌ |
OpenAI | o3-mini | ❌ |
OpenAI | o1 | ❌ |
Alibaba | Qwen2.5-Max | ✅ |
Anthropic | Claude 3.7 Sonnet | ❌ |
DeepSeek | DeepSeek-V3 | ✅ |
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.
Figure 1: Google Trends graph comparing the popularity of “open source LLMs” and “closed source LLMs”.
Detailed analysis of popular models
1. OpenAI GPT-5
GPT-5, released in August 2025, is OpenAI’s unified reasoning model. It adjusts automatically between fast responses and deeper reasoning, depending on the task. It is available across ChatGPT tiers, with extended reasoning included in Pro access.
Core features:
- Combines fast response and extended reasoning through real-time routing.
- Handles up to 400K tokens, allowing analysis of large documents and multimodal inputs.
- Reduces hallucinations and factual errors compared to previous models.
Performance highlights:
- Achieves high scores in math, coding, multimodal tasks, and health domains.
- Uses fewer tokens for complex reasoning, improving efficiency.
- Provides stronger coding support for debugging, front-end generation, and design logic.
- Produces more coherent and structured text with improved tone control.
Variants for different needs:
- Pro (thinking): extended reasoning mode for complex professional tasks.
- Standard: balanced option for general-purpose use.
- Mini: cost-efficient model for routine tasks.
- Nano: lightweight version for high-volume or embedded applications.
2. OpenAI GPT-4.5 and GPT-4o
GPT-4.5 is 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, enabling tasks such as captioning, diagram interpretation, and creating alternative text for accessibility. This makes them versatile for different applications.
- Efficiency: GPT-4 handles large tasks with limited 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.
3. Claude 4 (Opus 4 and Sonnet 4)
Claude 4, launched in May 2025, introduces two new models: Claude Opus 4 and Claude Sonnet 4. These models improve coding, reasoning, and handling of long-duration tasks compared to the Claude 3 family.
Claude Opus 4
Opus 4 is designed for complex coding, research, and agent workflows that may run for hours. Key features include:
- High scores on benchmarks such as SWE-bench and Terminal-bench.
- Supports both quick responses and extended reasoning.
- Handles multi-step processes and long-running tasks effectively.
Claude Sonnet 4
Sonnet 4 is more suitable for everyday professional use. Key features include:
- Improved coding and reasoning performance.
- More substantial alignment with instructions.
- Available to both free and paid users.
New capabilities in Claude 4
Both Opus 4 and Sonnet 4 add advanced functions:
- Extended thinking with tools: Can combine reasoning with external tools like code execution and file access.
- Parallel tool execution: Runs multiple tools at once for efficiency.
- Memory and context: Supports large context windows and file-based memory for longer workflows.
4. 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 enables deeper, more sustained reasoning over extended periods or complex problems, thereby 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.
5. Gemini
Google’s latest model, Gemini 2.0 Flash, was released in February 2025. The free edition includes all essential features, such as text-based prompting, the ability to upload and create photos, and the capability 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.
6. 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.
7. Qwen (Alibaba Cloud)
Qwen models scale data and model size for advanced AI applications. The latest release, Qwen2.5-Max, utilizes a Mixture of Experts (MoE) and is pre-trained on over 20 trillion 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.
8. Llama 4
Released in April 2025, Llama 4 is Meta’s latest open-weight, natively multimodal model family built with a mixture-of-experts (MoE) architecture.
It introduces two main variants:
- Llama 4 Scout, a 17B active parameter model with a record-breaking 10M token context window that fits on a single H100 GPU
- Llama 4 Maverick, a 17B active parameter model with 128 experts (400B total parameters) that outperforms GPT-4o and Gemini 2.0 Flash in reasoning, coding, and multimodal tasks.
Both models are distilled from Llama 4 Behemoth, a 288B active parameter, 2T total parameter research model.
Technical innovations
- Llama 4 introduces a Mixture-of-Experts (MoE) architecture, where tokens activate only a fraction of the parameters, thereby improving training and inference efficiency through the alternating use of dense and MoE layers.
- It is natively multimodal, using early fusion to jointly process text, image, and video tokens, trained on over 30 trillion multimodal tokens for cross-modal reasoning.
- Context capacity is expanded, with Llama 4 Scout supporting up to 10 million tokens, enabling advanced use cases like multi-document summarization, codebase analysis, and long-term task reasoning.
- For training efficiency, it leverages FP8 precision, MetaP hyperparameter tuning, and a 200-language dataset (10 times larger than Llama 3). Post-training innovations include a new pipeline of lightweight SFT, online RL, and DPO, combined with adaptive reinforcement strategies that strengthen reasoning, coding, and multimodal abilities while preserving conversational quality.
9. 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 distinct AI applications, providing powerful reasoning and efficiency enhancements.
Use cases and real-life large language model examples
Here are some key use cases of LLM models, along with relevant 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.2
- 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.3
- 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.
- 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 translation4
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.5
- Virtual assistants: LLMs enable virtual assistants to respond to user queries, manage tasks, and control smart devices.
- Personalized responses: Generate customized 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.8
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.9
Video: LLM-based code suggestions
- 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.10
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 a private dataset, helping businesses gain insights without needing deep technical expertise.
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.11
- 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.12
6. Healthcare
- Medical question answering: LLMs can assist in patient triage by answering medical questions.
- Real-life example: Med-PaLM, an LLM developed by Google Research, is designed to help readers analyze findings from patient tests. Thus, the reader can select the most appropriate answer for the disease, test, or treatment.13
- 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.14
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.15
- 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.16
- 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 that opposing counsel missed
- 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:
8. Education and training
- Personalized tutoring: LLMs act as AI tutors, providing step-by-step explanations and customized feedback to students.
- Real-life example: Khan Academy’s Khanmigo utilizes GPT-4 to assist students in solving math problems, writing essays, and practicing critical thinking skills.17
- Corporate training and onboarding: LLMs generate training content, quizzes, and adaptive learning paths for employees.
9. Human Resources and recruitment
- Resume screening and candidate matching: LLMs analyze job descriptions and resumes to recommend the best candidates.
- Real-life example: HiredScore utilizes AI to enhance recruiting by screening resumes and identifying complex job matches.18
- Employee engagement surveys: LLMs summarize open-ended survey responses and provide insights into employee sentiment.
10. Retail and eCommerce
- Product recommendations: LLMs analyze customer behavior and generate personalized shopping suggestions.
- Customer sentiment analysis: AI models process customer reviews to identify trends and inform inventory and marketing strategies.
FAQs
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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