10+ Large Language Model Examples & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model.
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 alongside 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 models differ across providers, but per-token pricing is the most widely used approach.
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
You can evaluate the large language models by examining their benchmark performance and real-world latency (available by clicking each model’s name in the table), and by reviewing their pricing to understand their overall efficiency and cost-effectiveness.
For further insights, explore comparisons of current and popular models, including an overview of Large Multimodal Models (LMMs) and how they differ from LLMs, and a detailed analysis of the Top 30+ Conversational AI platforms.
Detailed analysis of popular models
1. OpenAI’s 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.
OpenAI GPT-5.2
OpenAI’s GPT-5.2 release emphasizes stronger performance on complex and multi-step tasks such as building spreadsheets and presentations, coding, image understanding, long-context reasoning, and reliable tool use.
OpenAI reports GPT-5.2 achieves state-of-the-art results across multiple benchmarks, including GDPval, where it beats or ties human professionals on a large share of real-world occupational tasks.
The model also delivers improved software engineering performance (e.g., SWE-Bench Pro and SWE-Bench Verified), lower hallucination rates, and major gains in long-document comprehension. With these developments, GPT-5.2 becomes better suited for analyzing contracts, reports, and multi-file projects.
GPT-5.2 also improves vision capabilities for interpreting charts and interfaces, and achieves high reliability in tool-calling benchmarks, supporting end-to-end automation in workflows like customer support and data analysis.2
2. Claude 4.6
Anthropic introduced Claude Sonnet 4.6, its most advanced Sonnet model as of February 2026. It delivers broad improvements in coding, long-context reasoning, agent planning, computer use, and knowledge work:
- Context window: The model includes a 1M-token context window (beta) and becomes the default option for Free and Pro users on Claude.ai, with pricing unchanged from Sonnet 4.5.
- Performance: Anthropic claims Sonnet 4.6 closes much of the gap with Opus-class models, offering near–frontier-level performance for economically valuable tasks while remaining more cost-effective.
- Computer use capabilities: It allows Claude to operate software via clicks and typing rather than through APIs, and it demonstrates greater resistance to prompt-injection attacks.
Additional platform updates include improved tool usage, context compaction, and expanded integrations, such as MCP connectors in Claude for Excel, enabling more automated workflows across enterprise systems.
3. Gemini
Gemini 3 Pro is Google DeepMind’s latest multimodal foundation model designed for complex reasoning and professional-grade tasks.
Capabilities include:
- Advanced reasoning and understanding: Gemini 3 Pro produces detailed responses across complex tasks, going beyond surface-level answers.
- Multimodal intelligence: It natively processes and synthesizes information from text, images, audio, video, and code.
- Enhanced coding and agentic capabilities: Gemini 3 Pro focuses on vibe coding and agentic coding. It can follow instructions, write code, and integrate with tools more effectively than prior generations, supporting multi-step tasks and autonomous workflows.
Across key evaluations, Gemini 3 Pro achieves top scores compared with other large models, demonstrating notable strengths in reasoning, multimodal understanding, mathematics, and coding tasks.
It also demonstrates strong performance on vision and multimodal benchmarks, such as ScreenSpot-Pro and Video-MMMUi, indicating better interpretation of images, video, and visual data than many competitors.3
4. 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.
5. 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.
Qwen3.5 and Qwen3.5-Plus
Qwen released Qwen3.5, starting with its first open-weight model, Qwen3.5-397B-A17B, a native multimodal (vision-language) model for reasoning, code generation, agent workflows, and multimodal understanding.
The model uses a hybrid architecture that combines linear attention (Gated Delta Networks) with a sparse Mixture-of-Experts. Qwen also significantly expanded multilingual coverage, increasing support from 119 to 201 languages and dialects.
Alibaba also introduced Qwen3.5-Plus, a hosted version available through Alibaba Cloud Model Studio, featuring a 1M-token context window and built-in tool support with adaptive tool use.
Benchmark results suggest Qwen3.5-397B-A17B performs competitively against frontier models across language reasoning, instruction following, coding, agent benchmarks, multilingual evaluations, and vision-language tasks such as document understanding, spatial reasoning, and video comprehension.
6. 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.
7. xAI Grok-4 and Grok-4.1
xAI’s Grok-4 and its upgraded successor Grok-4.1 represent the company’s most advanced frontier large language models as of February 2026.
Built as multimodal and tool-enabled reasoning systems, these models are designed for conversational AI, agentic task execution, long-context reasoning, and real-time information retrieval.
xAI has positioned Grok-4.1 as a refinement optimized for accuracy, alignment, and extended task coherence. Variants such as “Fast” and long-context configurations target enterprise deployments and agent-based workflows.4
8. Mistral Large 3
Mistral Large 3 is Mistral AI’s flagship mixture-of-experts (MoE) model. It is built with a large total parameter count and a smaller active parameter subset per token, delivering frontier-level reasoning and coding performance while maintaining inference efficiency.
The model supports extended context windows and native multimodal capabilities, enabling it to process text and visual inputs within a single reasoning framework. This makes it suitable for enterprise document workflows, code generation, data analysis, and multimodal agent pipelines.5
9. ByteDance Doubao 2.0 (Seed 2.0 family)
Doubao 2.0, built on ByteDance’s Seed 2.0 model family, represents a major upgrade to China’s widely used AI assistant. Designed explicitly for the agentic workflows, the system emphasizes multi-step reasoning, autonomous task execution, structured tool use, and improved coding performance.
The model family includes specialized variants such as Pro, Lite, Mini, and Code, allowing cost-performance optimization across use cases.
10. Amazon Nova 2
Amazon Nova 2 is Amazon’s second-generation foundation model family, built for enterprise AI workloads. Unlike consumer-oriented AI systems, Nova 2 is positioned primarily as infrastructure, integrated with AWS Bedrock and designed for scalable deployment across enterprise environments.
The Nova 2 lineup includes variants such as Lite, Pro, Sonic, and Omni, covering text, multimodal, and speech-to-speech capabilities.
The Nova 2 Pro and Lite models focus on text generation, reasoning, and workflow automation, while Sonic and Omni extend to real-time speech and multimodal interaction. This modality coverage allows enterprises to build voice agents, multimodal copilots, and fully automated backend systems using a single cloud provider.6
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.7
- 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.8
- 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 translation9
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.
- 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.12
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.13
- 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 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.15
- 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.16
6. Healthcare and medicine
- 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.17
- 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.18
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.19
- 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.20
- 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.21
- 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.22
- 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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