LLM Model Comparison: Choosing the Right AI Partner in 2025

By now, we’ve all likely experimented with the modern tech wonder that is large language models (LLMs). You see their influence everywhere, from intelligent chatbots to AI responses in search engines to smart features on your favorite devices.

The explosion of these AI tools into the mainstream market means we now have many great options to choose from. And while they all seem to perform the same set of tasks (answer questions, write text, reason through problems) the similarities often end there.

Some LLMs shine at generating imaginative stories, while others boast superior logical reasoning (even if they need a moment to ponder). The differences aren’t always visible on the surface. You need to dig into their architecture, explore LLM benchmark results, and put them to the test.

Here’s how leading models like GPT, Claude, Gemini, Llama, and Mistral perform in real-world use cases. You’ll get clear insights on their strengths, tradeoffs, and what to keep in mind as these models evolve.

LLMs Explained: What They Do, and Why No Two Are the Same

Large language models (LLMs) are, at their core, sophisticated text prediction engines. Stripped of their shiny interfaces, they all perform one key function: take a text prompt and predict what text should come next. It sounds simple, but under the hood, it’s anything but.

LLMs are trained on enormous piles of text from books, websites, code repositories, news articles, and just about anything else that lives on the internet. By learning patterns in this training data, LLMs can consistently generate coherent, often helpful responses to prompts:

Popular uses for LLMs today include answering questions, summarizing documents, generating content, translating text, writing code, and even reasoning through problems. But not all LLMs perform the same way, and that’s by design.

A few key factors make one model behave differently from another:

• Training data: Models trained on broader, cleaner, and more diverse datasets tend to be better at handling nuance and less likely to hallucinate. Proprietary datasets can also give certain models an edge in specific domains. GPT-4, for instance, digested trillions of words, while smaller models might train on billions, and the difference shows in their understanding of nuance.
• Architecture: Most modern LLMs use transformer designs, but they implement them differently. Some models, like Claude, use “constitutional AI” approaches that build in certain behavioral guardrails, while others, like Gemini, optimize for multimodal processing.
• Parameter count: This roughly indicates model complexity. Models with more parameters (measured in billions) can capture more intricate patterns, though bigger isn’t always better. Cleverly designed smaller models can sometimes outperform larger ones on specific tasks.
• Fine-tuning and RLHF: After pretraining, many LLMs go through a second phase where human feedback shapes their behavior. This is what makes models produce more accurate, helpful, and safe responses than those without this additional training. Some models also get domain-specific tuning for legal work, medical Q&A, coding, financial analysis, and so on.
• Multimodality: Older LLM versions only worked with text. But today’s models have evolved to handle images, audio, code, and even video, earning them the name multimodal AI systems. This unlocks new use cases, like describing images, transcribing speech, answering questions about a chart, and so on.

Long story short, all LLMs predict text, but how well, fast, and flexibly they do it comes down to what they were trained on, how they were built, and what they’re optimized for. One model might write poetry well but stumble on math. Another might breeze through code but fumble a joke. It all boils down to architecture, data, and design.

Proprietary vs. Open-Source Large Language Models

The LLM industry today is split into two major camps with fundamentally different approaches to availability and control: proprietary models (aka closed-source AI systems) and open-source models. How much freedom you get to use, modify, and understand LLMs depends heavily on which category they fall into.

Proprietary models like GPT and Claude represent the “black box” approach. These models deliver impressive performance but come with significant restrictions. You can only access them through official APIs or applications, never seeing how they actually work inside.

Companies behind proprietary LLMs control how they’re used, how they evolve, and what you can (and can’t) do with them. When you use these services, your data travels to company servers where the real processing happens, which raises concerns about data privacy and vendor lock-in.

On the flip side, open-source models like DeepSeek and Llama adopt a contrasting philosophy. You can download the actual LLM weights, run them on your own hardware, and even peek under the hood to understand how they function. This creates possibilities that simply don’t exist with commercial options, including:

1. Complete privacy: Your data never leaves your systems
2. No usage limits: Process as much as your hardware allows
3. Customization: Fine-tune models on your specific data
4. Cost control: No per-token pricing surprises

It’s worth noting that there's a subdivision within open-source models. A few “open-source” models (e.g., Llama 3) are, in reality, only partially open-source. They come with usage restrictions that prevent certain applications and limit large-scale commercial deployment. True open-source models, however, have minimal restrictions beyond attribution requirements.

The performance gap between proprietary and open-source models has narrowed dramatically in the past year. While GPT and Claude still lead in complex reasoning tasks, models like Llama 3 405B and DeepSeek R1 now match or exceed earlier proprietary offerings at a fraction of the operational cost. On that note, let’s get to the comparisons.

3 Leading Proprietary Large Language Models (LLMs)

We’re well past the point where a single LLM dominates the AI landscape. What we have now is a group of heavyweight contenders, each built by a major company and tuned for different strengths. These are the systems with billions in funding, massive computational resources, and some of the brightest minds in AI working to improve them.

And while they all handle language at a high level, the details vary in ways that matter depending on what you’re building or optimizing for. Here are the three most widely used proprietary LLMs today and what sets them apart in practice:

OpenAI: GPT-4o

• Key strengths: General performance, reasoning, coding, multimodal capabilities
• Context window: 128,000 tokens (GPT-4o)
• Access: API, ChatGPT, Microsoft Copilot
• Parameter count: 175 billion (estimated)
• Multimodal: Yes (text, images, audio, code)

Since the revolutionary GPT 3 debut in 2022, OpenAI’s GPT models have remained the most widely used LLMs on the market, in part because they’re embedded in so many tools, from Microsoft’s Copilot to Duolingo’s language tutor.

GPT-4o is currently the most capable release in OpenAI’s line of GPT models. The “o” stands for “omni,” referring to its multimodal nature, which means GPT-4o can handle text, audio, and images in a single model. This lets it interpret visual prompts and respond with spoken answers, giving it a much broader range of use cases than earlier versions.

In many tests, GPT-4o consistently produced the most coherent and well-reasoned responses across general knowledge, creative writing, and structured outputs. It also leads the field in code generation and explanation, making it a favorite among developers.

GPT-4o is currently available through OpenAI’s API and ChatGPT interface, and is also integrated into Microsoft Copilot and Azure OpenAI services.

• Standout: Best overall generalist, with strong performance across almost all domains.
• Tradeoff: Occasionally low latency.

Anthropic: Claude 3 Model Family

• Key strengths: Long-context understanding, safety, summarization
• Context window: 200,000 tokens
• Access: API only
• Parameters count: Not disclosed
• Multimodal: Yes (text, images, code, audio)

Anthropic’s Claude 3 family consists of three models with distinct performance profiles:

• Claude 3 Opus: The flagship model optimized for complex reasoning
• Claude 3.7 Sonnet: Middle-tier balance of performance and speed
• Claude 3.7 Haiku: Fastest model for everyday tasks

Claude’s big promise is that it’s built to be “helpful, harmless, and honest.” That’s more than just marketing. It reflects Anthropic’s unique training process, called Constitutional AI, which shapes the model using ethical principles instead of human reinforcement alone.

This creates a noticeably different interaction style. Claude demonstrates superior instruction-following, often interpreting ambiguous requests the way a human would rather than taking prompts hyper-literally. Quite a few tests show Claude often matching or exceeding GPT-4o in domains like programming and creative writing.

Claude’s models are especially impressive when dealing with large documents. Its 200,000 context window means you can feed it entire research papers or transcripts and still get grounded, relevant answers.

Claude is slightly more conservative than GPT-4 in creative tasks and less confident when unsure, but that also means it hallucinates less.

• Standout: Best for summarization, safety-focused tasks, and enterprise integrations.
• Tradeoff: Less creative freedom, slower rollout of new capabilities

Google DeepMind: Gemini

• Key strengths: multimodal input, long context, integration across Google apps
• Context window: Up to 2 million tokens
• Parameters: Varies; most not public
• Access: API, Google AI Studio, and Vertex AI
• Multimodal: Yes (text, image, code, audio)

Gemini is Google’s umbrella family of LLMs, designed to handle everything from smartphone assistants (Nano) to high-end reasoning tasks (Ultra). Gemini 2.0 Flash and Pro are the most widely used today, with Flash designed for fast inference and Pro offering more thoughtful, context-rich replies.

For teams already embedded in the Google ecosystem, Gemini models offer easy integration with Docs, Gmail, and more (often a deciding factor for enterprise adoption). 

Gemini's standout feature is its enormous context window. In practice, this means you can paste massive datasets, logs, or full codebases into a single prompt. That makes it ideal for enterprise teams needing to parse long materials or generate highly contextual outputs.

Performance-wise, Gemini is competitive with GPT-4o and Claude Opus in reasoning, especially with knowledge-heavy prompts. However, Gemini’s responses can feel inconsistent depending on the version used, and its availability is still somewhat restricted. It's powerful, but not always easy to access or test in a unified way.

• Standout: Exceptional long-context handling and seamless Google integration.
• Tradeoff: Fragmented model versions and inconsistent access across platforms.

Leading Open-Source Large Language Models (LLMs)

Open-source LLMs represent a fundamental shift in how powerful technology is distributed. Unlike closed models, you can inspect them, modify them, and in many cases, use them commercially without restriction. That’s a big upside for developers and businesses who want more control, fewer vendor risks, and clearer insights into how their models behave.

This freedom comes with tradeoffs in performance and ease of use, but the gap is closing faster than many expected. Let’s see a few of the top players:

Meta: Llama 3

• Key strengths: Flexibility, local deployment, strong performance at scale
• Context window: up to 128,000 tokens
• Access: Open weights (via GitHub, Hugging Face)
• Parameters: Llama 3.1 (8 billion, 405 billion), Llama 3.2 (1 billion, 3 billion, 11 billion, 90 billion), Llama 3.3 (70 billion)
• Multimodal: Some variants (e.g., Llama 3.2)

Llama 3 is Meta’s open-weight model family and one of the most widely adopted open LLM bases in the world. You can download and run it locally or in the cloud, fine-tune it on your own data, and integrate it however you want. Because it’s free for commercial use, it’s behind many smaller tools and chatbots you may not realize are running on Llama.

The current lineup is split into three groups (Llama 3.1, 3.2, and 3.3) with each offering something different:

• Llama 3.1 8B and 405B are powerful text-only models.
• Llama 3.2 models (1B, 3B, 11B, 90B) are all multimodal.
• Llama 3.3 70B is a highly capable text model aimed at performance and efficiency.

The biggest benefit here is flexibility. You can download the models directly from GitHub, fine-tune them, control your data, avoid vendor lock-in, and deploy on-premises. However, setup and maintenance require more technical effort, and performance may not match the top commercial APIs out of the box.

• Standout: Fully open, customizable, and great for private deployments.
• Tradeoff: Currently lags behind the best proprietary models in reasoning and advanced coding

DeepSeek: R1 and V3

• Key strengths: High performance at lower compute cost, open model access
• Parameters: 671 billion
• Context Window: 128,000 tokens
• Variants: R1 (reasoning model), V3 (general LLM)
• Access: Open weights, chatbot, API

DeepSeek’s R1 and V3 models are relatively new players but have turned heads for their size and ambition. Built by a Chinese AI research team, they almost rival the performance of models like o1 and GPT-4, but were trained on far fewer resources and released openly, making them an attractive option for researchers and startups.

R1 is a reasoning-focused model, while V3 is a general-purpose LLM. In testing, both handled logical and mathematical problems well, though they lacked some polish in natural language tasks like writing and summarizing. They show particular strength in step-by-step problem solving.

That said, adoption is still early, and documentation and support are limited. There’s also uncertainty around the future of access due to geopolitical factors. Most organizations looking for commercial support and reliability will likely stick with the established players for now, but DeepSeek represents an important signal about where the market is heading.

• Standout: Large-scale open models with strong reasoning skills.
• Tradeoff: Less refinement, lower general language quality, and early-stage support.

Cohere: Command

• Key strengths: Enterprise-friendly, RAG-tuned, accurate on retrieval tasks.
• Parameters: R7B has 7B; others not disclosed
• Context window: Up to 128,000 tokens
• Access: API only

Cohere’s Command R models are different. They’re built for enterprise use, especially tasks involving private company data. The latest model, Command R+, is optimized for retrieval-augmented generation (RAG), which helps it answer questions based on your documents or internal databases.

Rather than trying to outperform GPT-4 in raw capabilities, Command models focus on being usable, stable, and reliable. Businesses like Oracle and Notion already use them.

• Standouts: Purpose-built for company-specific tasks and solid RAG support for internal data use
• Tradeoffs: No open weights available and not as versatile as more general LLMs

Amazon: Nova

• Key strengths: Long context window, AWS-native, improving benchmarks.
• Parameters: Unknown
• Context window: Up to 300,000 tokens
• Access: API via AWS

Nova is Amazon’s family of foundation models, and they’ve improved a lot recently. The three core models (Nova Micro, Nova Lite, and Nova Pro) are optimized for different workloads. They’re available through Bedrock, Amazon’s AI platform on AWS.

Nova isn’t flashy, and Amazon has been tight-lipped about the technical details. But benchmark tests show strong results across coding, math, and reasoning. Because of its integration with AWS, it’s already being used behind the scenes by enterprise applications.

1. Standout: Huge context window and seamless integration with AWS tools
2. Tradeoff: Closed weights and lack of transparency around model architecture

Mistral: Large 2

• Key strengths: Strong reasoning, multilingual performance, open weights.
• Parameters: 123 billion
• Context window: 128,000 tokens
• Access: Open weights (with license restrictions)

French startup, Mistral AI, has built a reputation for creating remarkably efficient LLMs. Their Mistral Large 2 (123B parameters) performs competitively with models twice its size, making it ideal for organizations with limited computational resources.

Mistral supports a broad range of tasks and is a strong performer on benchmarks like GSM8K and ARC. It’s also available with open weights, which means you can fine-tune it, though not completely open-source.

Where Mistral truly shines is in its deployment flexibility. The model can run on consumer hardware with thoughtful optimization, which opens up possibilities for edge computing and air-gapped environments where commercial APIs aren’t an option.

• Standout: Competitive with GPT-4-level models and licensed for commercial fine-tuning
• Tradeoff: Not as widely deployed as Llama or DeepSeek, and the license has limits on redistribution

Alibaba Cloud: Gwen2.5

• Key strengths: Wide range of model sizes, huge context window, domain-specific variants.
• Parameters: 0.5 billion to 72 billion
• Context window: Up to 1,000,000 tokens
• Access: Open, API, chatbot

Qwen2.5 is the most flexible model family on this list. With dozens of models targeting different use cases (math, coding, vision, long context, etc.) it offers a buffet of options depending on your needs.

The top model, Qwen2.5 Max, performs on par with closed models like Gemini and Claude. And with a context window of one million tokens, it’s currently unmatched among open-source models in how much data it can process at a time.

• Standout: Huge context window and specialized variants for every task
• Tradeoff: API access limits fine-tuning, and documentation is fragmented

xAI: Grok 3

• Key strengths: Stellar reasoning, cultural relevance (especially on X).
• Parameters: Unknown
• Context window: 128,000 tokens
• Access: Chatbot, limited open use

Grok is the brainchild of xAI, the AI company backed by Elon Musk. Its latest version, Grok 3, scores surprisingly well in both language and reasoning performance tests.

It’s worth noting that Grok 3 is trained heavily on data from X (formerly Twitter), which makes it unique, if slightly niche. While it’s not broadly used in enterprise, it’s a model to keep an eye on given xAI’s ambitions.

• Standout: Strong reasoning performance and tied into a growing media ecosystem
• Tradeoff: Limited availability and unclear development roadmap

TensorWave: AI Infrastructure That Powers The Next Generation of LLMs

Exploring LLM performance is one thing. Running and scaling those models in the real world is another. TensorWave’s cloud platform provides the perfect environment for hands-on LLM training and deployment without the massive upfront investment of physical solutions.

Unlike general-purpose cloud providers, TensorWave’s infrastructure is purpose-built for AI workloads. Our AMD Instinct™ MI-Series accelerators deliver industry-leading memory capacity (256GB HBM3E per GPU), letting you run multiple LLMs simultaneously.

With TensorWave, you get:

• Bare-metal performance optimized for large-scale LLMs
• High-bandwidth memory that keeps context windows flowing
• Scalable compute for training, fine-tuning, and inference
• Managed inference that’s reliable, fast, and easy to deploy

For companies serious about finding the right LLM fit, TensorWave offers specialized testing environments for real-world simulations. This means you can accurately measure how different models perform under specific conditions before committing to a particular solution.

TensorWave essentially makes sure your infrastructure never holds you back. Ready to run your stack smarter and scale with confidence? Get in touch today.

Key Takeaways

LLM comparison isn’t so much about finding the “best” model as it is about finding the right fit for your specific needs. While proprietary models like GPT, Claude, and Gemini currently lead in most use cases, open-source variants like Llama, Mistral, and Qwen are closing the gap fast, offering serious power with far more freedom and flexibility.

Of course, even the best model won’t perform well without the right infrastructure. That’s why we created TensorWave. With stellar high-bandwidth memory capacity and high-performance systems designed exclusively for AI workloads, TensorWave gives your chosen model the room and power it needs to thrive. Connect with a Sales Engineer today.