A Comparison of the Top AI Models: Features, Use Cases and Cost

Once the stuff of science fiction, artificial intelligence is now a mainstream technology. What started with the 2022 release of OpenAI’s GPT-3.5 language model and ChatGPT has evolved into a full-blown arms race to build smarter and smarter AI models. The release of DeepSeek-R1 has only intensified the momentum, driving companies to develop systems with more advanced reasoning capabilities at a lower cost.

But not all AI models are created equal, and the industry metrics used to compare them can be difficult for everyday users to understand. The list below highlights some of the top AI models available today, breaking down their defining features and strengths so you can determine the one that best fits your specific needs.

What Is an AI Model?

An AI model is a type of computer program trained on large datasets to recognize patterns, make predictions and generate outputs with minimal human intervention. The process begins with human researchers feeding the model relevant data that has been cleaned and prepared ahead of time. Then, they apply algorithms — sets of mathematical rules and instructions — that help the model learn how to identify specific patterns within the training data. Once an AI model has been tested for accuracy and properly trained, it should be able to generalize what it has learned and analyze new, unseen data on its own.

AI models are designed to perform specific tasks, with more advanced models handling more complex problems. Depending on how they’ve been trained, AI models can do anything from recognizing faces in video footage to translating text into other languages.

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Top AI Models: A Comparison

The following list includes AI models developed by tech giants and independent researchers alike, along with some key metrics to help you compare them at a glance.   

GPT-5.2

GPT-5.2 is OpenAI’s latest flagship model, building on the GPT-5 series with stronger reasoning, improved reliability and more advanced multimodal capabilities. Designed for both everyday productivity and enterprise-grade AI systems, GPT-5.2 balances speed with deeper analytical performance. It can process and generate text, images and audio, while supporting significantly expanded context windows for long documents and complex workflows.

• Capabilities: Advanced reasoning, multimodal understanding, coding assistance, and large context queries. 
• Use Cases: Summarizing and generating text, technical debugging, deep research, complex workflow automation and multimodal content understanding. 
• Benchmarks: High performance on reasoning and coding benchmarks compared to prior GPT generations; along with strong scores across math, science and complex instructions.
• Availability: GPT-5.2 is the default model for ChatGPT but can also be accessed through the developer API and cloud platforms like Microsoft Azure, Oracle Cloud Infrastructure and Alibaba Cloud. 
• Cost: Fine-tuning pricing starts at $1.25 per 1 million input tokens and $10  per 1 million output tokens. 

GPT OSS 120b

GPT OSS 120b is an open-weight, 120-billion-parameter model from OpenAI designed with developer features for flexible deployment and customization. Unlike closed proprietary systems, GPT OSS 120b allows developers to run and fine-tune the model in their own environments, supporting private and research use cases. It offers strong reasoning and coding performance while giving organizations more control over infrastructure and data.

• Capabilities: High-quality reasoning and chain-of-thought, 128K token context window and the ability to be deployed on-device or in their cloud network. 
• Use Cases: Local inference, research prototyping, open-source experimentation, private deployment and custom task fine-tuning
• Benchmarks: GPT OSS 120b is comparable to other OpenAI models on the AIME benchmark evaluation, which measures various STEM categories, and PhD-level questions.  
• Availability: Developers can download the open-weight model through Hugging Face. 
• Cost: GPT OSS 120b is free to download but can cost organizations assume hardware and cloud costs to deploy the model.

OpenAI o1

OpenAI o1 followed the release of GPT-4o, blowing away GPT-4o in competition math, competition code and PhD-level science questions. Trained through reinforcement learning, o1 can develop chains of thought to produce more thoughtful responses, solve complex problems step by step and learn from its mistakes.  

• Capabilities: Demonstrates advanced reasoning; improves its performance by learning from past mistakes; delivers more thoughtful responses.  
• Use cases: Writing and debugging code; solving complicated math problems in quantum computing; analyzing cell data in healthcare. 
• Benchmarks: Rivals human experts in reasoning-based topics, excelling in college mathematics, professional law and physics. 
• Availability: Users with a ChatGPT Team account can access OpenAI o1, while Pro and Enterprise users can access OpenAI o1 pro mode.  
• Cost: Pricing starts at $15 per 1 million input tokens and $60 per 1 million output tokens. 

Claude Opus 4.6

Claude Opus 4.6 is Anthropic’s flagship reasoning model, built to handle sustained, complex conversations and deep analytical tasks. It features enhanced long-context comprehension and refined instruction-following capabilities, while also demonstrating strong performance in coding and software engineering workflows. The model can generate, refactor and debug code across, making it well-suited for professional and enterprise environments.

• Capabilities: Deep reasoning, long-context comprehension and advanced safety and instruction adherence
• Use Cases: Research workflows, enterprise automation, legal and compliance Q&A, complex planning and analysis tasks
• Benchmarks: Performs exceptionally well in agentic coding along with other agentic capabilities such as search, financial analysis and tool use. 
• Availability:  Claude Opus 4.6 is available to users with a Claude Pro subscription or through the API and cloud platforms like Amazon Bedrock, Google Vertex AI and Microsoft Azure.
• Cost: Fine-tuning pricing starts at $5 per one million input tokens and $25 per one million output tokens. Claude’s Pro $20 monthly subscription also provides access. 

Gemini 3 Pro

Gemini 3 Pro is Google’s most advanced general-purpose AI model, designed for multimodal reasoning and large-scale enterprise applications. The company’s Gemini models can process text, images and other data types within extremely large context windows, making it capable of handling complex, multi-step workflows. Gemini 3 Pro is deeply integrated into Google’s cloud ecosystem and productivity tools.

• Capabilities: Multimodal reasoning, large context support and complex planning and synthesis
• Use Cases: Enterprise AI agents, multimodal assistants, autonomous workflow planners and advanced AI coding and research tasks.
• Benchmarks: Gemini 3 pro stands out in academic and abstract reasoning along with scientific knowledge and agentic terminal coding compared to other models. 
• Availability: Gemini 3 pro is available through the Gemini Chatbot and Gemini API for those looking to develop with the model.
• Cost: Pricing starts at $2 per 1 million input tokens and $12 per 1 million output tokens. Users looking for just chatbot access can use the model through the Google AI Pro subscription, starting at $19.99 a month. 

DeepSeek-R1

Developed by Chinese AI startup DeepSeek, DeepSeek-R1 is an open-source AI model that took the industry by storm, proving that a more compact and cost-efficient model can compete with those made by tech giants. Trained through reinforcement learning, DeepSeek-R1 showcases extensive context and chain-of-thought reasoning to tackle complex subjects and situations.  

• Capabilities: Explains complex math and scientific concepts; improves its performance on its own over time; evaluates text and data to provide relevant insights. 
• Use cases: Writing and debugging code; solving difficult math problems; producing creative written content; running customer service chatbots.
• Benchmarks: Excels in mathematics, coding and general reasoning. 
• Availability: DeepSeek-R1 is open-source and available under the MIT License. It can be found on deepseek-r1.com and other platforms like Microsoft Azure, Amazon Web Services and Hugging Face. It also powers DeepSeek’s eponymous chatbot.    
• Cost: Pricing is $0.14 per 1 million input tokens and $2.19 per 1 million output tokens. 

Grok 4

Developed by Elon Musk’s AI company xAI, Grok 4 shares the same name as the Grok chatbot it powers. The model builds upon the foundations of its predecessor, Grok 3, offering stronger reasoning skills, native tool use and support for text, image and voice inputs. Grok 4 was trained with reinforcement learning, so it can evaluate its process, fix its mistakes and adjust its performance over time. It also comes in a more powerful “heavy” version, which has a team of AI agents that work together as a sort of “study group,” according to Musk, collaborating to solve complex tasks.

• Capabilities: Autonomously browses the web, X and other news sources for up-to-date information; breaks down problems into manageable steps; handles text, voice and image inputs; comes with a ‘Voice Mode’ that can converse back and forth with users. 
• Use cases: Researching current events; building apps that require long-context reasoning; solving advanced math and coding problems; holding natural voice conversations; analyzing visual data. 
• Benchmarks: Excels in math, coding, science, abstract reasoning and pattern recognition; achieves 50.7% on Humanity’s Last Exam (the first-ever model to do so).
• Availability: Available to Premium+ and SuperGrok subscribers on X and Grok; API access also available to developers; Grok 4 Heavy is only available to SuperGrok Heavy subscribers.
• Cost: Premium+ plans start at $30/month; SuperGrok plans start at $30/month and SuperGrok Heavy is $300/month.

Llama 4 Maverick

Part of Meta’s Llama 4 family, Llama 4 Maverick marks the company’s transition from the Llama 3 family. The model is natively multimodal and runs on 17 billion active parameters. However, Llama 4 models only need to activate a small number of parameters to operate, lowering their cost and latency.   

• Capabilities: Needs only a fraction of its parameters for efficiency; runs on a single Nvidia H100 DGX host; performs well in image and text understanding. 
• Use cases: Building multilingual chatbots; analyzing documents; producing videos and images for marketing campaigns.   
• Benchmarks: Surpasses competitor models in reasoning, coding, multilingual capabilities and long-context scenarios.  
• Availability: Llama 4 Maverick can be downloaded from the Llama website and Hugging Face. Users can also use Meta AI with Llama 4 through the Meta website, Instagram Direct, Messenger and WhatsApp. 
• Cost: Pricing is $0.19 per 1 million input tokens and $0.49 per 1 million output tokens. 

Mistral 3

Mistral 3 is Mistral AI’s most advanced model to date and is intended for complex reasoning. It delivers strong multilingual understanding, coding performance and long-context reasoning while providing enterprise-grade performance with greater deployment flexibility thanks to its open-weights design.

• Capabilities: Multimodal, long-context reasoning, multilingual tasks and instruction following
• Use Cases: Enterprise assistants, large-scale retrieval applications, multilingual content generation, open research deployment.
• Benchmarks: Mistral Large 3 outperforms models such as Qwen3 and Gamma 3 in reasoning and instruction following. 
• Availability: Mistral Large 3 is available on the company’s chatbot, Le Chat. Developers can also access the model though cloud providers such as Amazon Bedrock, Microsoft Azure and IBM WatsonX.
• Cost: Fine-tuning starts at $0.50 per 1 million input tokens and $1.50 per 1 million output tokens. 

Mistral Voxtral Transcribe 2

Mistral Voxtral Transcribe 2 consists of two modals — Voxtral Mini Transcribe V2 and Voxtral Realtime — designed for high-accuracy batch and instant transcription across multiple languages.  Both modals deliver fast, reliable transcriptions while maintaining strong performance in noisy or accented speech scenarios. The family of modals is optimized for enterprise and developer workflows that require speech recognition capabilities.

• Capabilities: Converts speech to text across multiple languages and supports real-time and batch transcription
• Use Cases: Meeting transcriptions, call center recording, media subtitling and voice-based applications
• Benchmarks: Voxtral Transcribe 2 rivals models such as Scribe v2, GPT-4o mini Transcribe and Gemini 2.5 Flash when transcripting audio in multiple languages. 
• Availability: Available through Mistral’s API
• Cost: Batch transcription starts at $0.003 per minute and real-time processing begins at $0.006 per minute via API

Aya Expanse 8B

Aya Expanse 8B is part of Cohere Lab’s Aya project, a global initiative that involves more than 3,000 independent researchers working to expand AI’s multilingual capabilities. Open-source and text-only, Aya Expanse 8B can produce outputs in 23 languages, including English, French, Chinese, Arabic, Korean and Vietnamese. 

• Capabilities: Specializes in text-based applications; produces outputs in 23 different human languages. 
• Use cases: Translating text into another language; producing content in multiple languages; summarizing written text.
• Benchmarks: Excels in multilingual performance and keeps up with comparable open-weight models like Llama 3.1. 
• Availability: Aya Expanse 8B can be accessed on Hugging Face or WhatsApp. 
• Cost: Aya Expanse 8B is free to use on WhatsApp or Hugging Face.

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What Is the Best AI Model?

It’s impossible to designate an AI model as “the best” for various reasons. For one, the benchmarks used to compare these models are inherently broken and not very effective in general. Even when companies do manage to present helpful comparisons, the differences between models can be so slim that they’re inconsequential. 

And the AI race continues to provide improved AI technologies — the models of today are merely stepping stones for even more powerful systems on the horizon. So, when in doubt, just pick the AI model that most closely fits your particular needs, but keep an eye out for any upcoming models that could give you an even greater competitive advantage.

Notable AI Model Releases

Since 2023, AI companies  have released increasingly powerful and multimodal foundation models — often with major leaps in reasoning, performance and speed. The updates below capture recent milestone launches across leading model providers.

Claude Opus 4.6 (February 2026)

Claude Opus 4.6 model introduces several key updates to past models. It features a 1 million-token context window with adaptive thinking that can pick up contextual clues for better logical processing. The model was designed for tasks like agentic coding, financial analysis and multi-step agentic workflows. 

GPT-5.2 (December 2025)

GPT-5.2 released as an upgrade to GPT-5, which some users found to be subpar, and was intended to compete with other models like Gemini 3. Specifically, the model sought to address its agentic coding and reasoning capabilities. 

Google Gemini 3 (November 2025)

Google released its much-anticipated Gemini 3 models (Gemini 3 Pro and Deep Thinking) building on its previous models. The new releases included improvements to long-term reasoning and multimodal understanding. Gemini 3 was made available on the Google Gemini app, Google AI Studio and Vertex AI. 

GPT-5 (August 2025)

OpenAI released GPT-5, which the company calls its fastest and smartest model to date. The new model brought improvements to multiple areas, including coding, math, multimodal understanding and health knowledge. Despite the improvements, users reported feeling underwhelmed by the release. 

Grok 4 (July 2025)

Elon Musk’s xAI released Grok 4, the latest version of its conversational AI, designed to be faster and more capable than prior models. Grok 4 was trained on xAI’s custom compute cluster and emphasizes real-time awareness of current events through its integration with X. xAI claims it now rivals top models like GPT-4o and Claude 3.5 in benchmark performance, although third-party evaluations are limited.

Google Gemini 2.5 Pro (April 2025)

Gemini 2.5 Pro is the latest release in Google DeepMind’s Gemini model family. The model builds on the Gemini 1.5 architecture, and features a longer context window, enhanced code generation and tighter integration across Google Workspace. The release followed months of model consolidation by Google, and reinforced its strategy to unify Gemini across its consumer and cloud offerings.

Claude 3.7 Sonnet (February 2025)

Anthropic launched Claude 3.7 Sonnet, a speed-optimized version of its mid-tier model in the Claude 3 family. The release focused on improved responsiveness, advanced reasoning and lower latency — key priorities for enterprise adoption. It arrived as the company faced renewed legal scrutiny over AI training data.

DeepSeek-R1 (January 2025)

DeepSeek AI, a Chinese lab building open-weight large language models, released DeepSeek-R1, a bilingual Chinese-English model optimized for scientific reasoning and instruction following. The lab gained attention for its scaling roadmap and open research approach, positioning it as one of the most prominent AI efforts outside the U.S.

Meta LLaMA 3 (April 2024)

Meta introduced LLaMA 3, its third-generation family of open-weight models ranging from 8B to 70B parameters. LLaMA 3 models improved significantly over LLaMA 2 in reasoning, coding and multilingual tasks. The models power Meta’s AI assistant across WhatsApp, Instagram, Facebook and Messenger and form the foundation of its open-source AI infrastructure strategy.

GPT-4o (May 2024)

OpenAI introduced GPT-4o, a fully multimodal model that processes text, vision and audio inputs with native support for real-time interaction. The “o” in GPT-4o stands for “omni,” reflecting its ability to reason across modalities. It matches GPT-4 Turbo in language tasks and outperforms earlier models in speed and voice responsiveness.

Claude 3 Model Family (March 2024)

Anthropic debuted the Claude 3 model family, including Claude 3 Haiku, Sonnet and Opus — each tailored for different workloads. The top-tier Opus model outperformed GPT-4 on many standard benchmarks, particularly in math and logical reasoning.

Mistral Mixtral 8x7B (December 2023)

Mistral AI, a Paris-based startup, launched Mixtral — a mixture-of-experts (MoE) model that activates two of eight 7 billion parameter  models at a time. Mixtral demonstrated strong performance across language and reasoning tasks and helped validate MoE as a viable architecture for scalable inference.

GPT-4 (March 2023)

OpenAI released GPT-4, a major upgrade over GPT-3.5. The model introduced multimodal capabilities (text and image), stronger reasoning skills and improved safety systems. It was initially accessible via ChatGPT Plus and API, and became a widely used foundation for commercial and academic research.

Claude 1 (March 2023)

Anthropic launched Claude 1, its first commercial language model, designed to be helpful, honest and harmless. It was trained with a technique called Constitutional AI, which aimed to align outputs with human values without relying on reinforcement learning from human feedback (RLHF).

Bard (March 2023)

Google launched Bard, its experimental conversational AI built on LaMDA. While initial responses were criticized for factual errors, Bard marked Google’s public entry into the generative AI race and evolved significantly throughout the year, later transitioning to Gemini branding.

ChatGPT (Free Preview Launched November 2022; scaled February 2023)

Though technically released in late 2022, ChatGPT became a global phenomenon in early 2023. Built on GPT-3.5, it showcased the potential of chat-based interfaces and helped trigger the current wave of generative AI adoption. Its success led to rapid integration across Microsoft products via Azure OpenAI Services and fueled broader LLM development across the tech sector.

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