Top 10 Large Language Models in 2025

How We Judge LLMs in 2025

Gone are the days when the “best” large language model was simply the one with the most parameters—a number that once ballooned into the trillions as tech companies raced to build bigger digital brains. In 2025, the game has shifted: what matters now is how well a model works in real life. Efficiency, practicality, and real-world usefulness have taken center stage, overshadowing raw size.

Today’s LLM landscape splits clearly into two camps, each with its own strengths:

• Open-source models: These are like free blueprints—anyone can download them, tweak their code, and run them on their own computers or servers. They’re perfect for small businesses, researchers, or tinkerers who want to customize AI to their needs without paying ongoing fees.
• Closed-source models: These live behind corporate walls, available only through paid cloud services. They’re fully managed by companies like OpenAI or Google, which handle updates and maintenance, but they come with a price tag—often based on how much you use them.

To pick the top 10 models, we looked at a mix of hard data and real-world use:

• How well they score on standard tests (like solving math problems or understanding complex texts)
• How many people and businesses actually use them day-to-day
• How easy developers find them to work with (a strong community or good tools make a big difference)
• How fluent they are in different languages, especially English
• How fast they generate answers and how much they cost to run

The 10 Heavyweights

1. GPT-4.5 (OpenAI)

Tagline: “The Swiss-army champion” (pricey but all-purpose, closed-source)
If you need a model that can do it all, GPT-4.5 is hard to beat. Built by OpenAI with deep backing from Microsoft, it excels at everything from writing code to crafting stories, solving math problems, or even analyzing images and audio.

Standout strengths:

• Its reasoning skills are top-tier—think of it as having the logic of a graduate student
• It can handle super long inputs (up to 128,000 tokens, which is roughly 100,000 words—about the length of a short novel)
• It’s multimodal, meaning it works with text, images, and audio seamlessly
• A unique “self-correction” feature lets it catch its own mistakes and fix them

Downsides:

• You can’t run it on your own computer—it only lives in the cloud
• Costs add up quickly if you use it a lot (API fees can sting for heavy users)
• While it’s good with many languages, it sometimes stumbles with nuanced Chinese compared to models built specifically for that language

2. Claude 3.5 Sonnet (Anthropic)

Tagline: “Long-form pro & safety first”
Anthropic’s Claude 3.5 Sonnet is the go-to for anyone dealing with massive amounts of text—like lawyers sifting through contracts or researchers analyzing thousands of pages of data.

Standout strengths:

• It handles the longest context window of any top model (200,000 tokens—enough to digest an entire book in one go)
• Built-in “constitutional AI” keeps it on the straight and narrow: it avoids harmful content and sticks to ethical guidelines, making it a favorite in regulated fields like finance or law
• It’s a pro at understanding dense, technical writing—perfect for legal briefs, financial reports, or scientific papers

Downsides:

• It’s not great with images or audio (unlike GPT-4.5 or Gemini, it’s mostly a text specialist)
• Its Chinese skills lag behind models built for that language, since it hasn’t been trained on as much Chinese data

3. Qwen3 (Alibaba Cloud)

Tagline: “King of Chinese, with open-source options”
When it comes to understanding and speaking Chinese, Qwen3 (from Alibaba’s cloud division) is in a league of its own. It’s the top choice for businesses or apps focused on Chinese-speaking users.

Standout strengths:

• Native-level fluency in Chinese—it gets slang, regional dialects, and cultural nuances that foreign models miss
• It handles long texts (128,000 tokens) and responds quickly
• Unlike many closed-source models, it offers open-source versions (like Qwen3-8B or 72B) for customization
• Its “Agent Mode” lets it act like a digital assistant, automatically using tools (like calculators or search engines) to get things done

Downsides:

• While it’s good with English, it’s not quite as sharp as GPT-4.5 for complex English tasks
• It’s still building recognition outside of China, so international users are slower to adopt it

4. Gemini 1.5 Pro (Google)

Tagline: “Search-savvy with a million-token memory”
Google’s Gemini 1.5 Pro is like having a supercharged search engine combined with a top-tier LLM. It’s deeply integrated with Google’s tools, making it a workhorse for professionals.

Standout strengths:

• Its experimental version can handle a mind-blowing 1 million tokens—enough to process an entire textbook or a 10-hour podcast transcript
• It uses “real-time search” to pull in up-to-the-minute info, so its answers stay current (great for news, stock updates, or trending topics)
• It’s a whiz with images, videos, and audio—for example, it can describe a graph, transcribe a meeting, or even explain a meme

Downsides:

• It sometimes plays it too safe, giving vague answers to avoid mistakes
• Its API (the tool developers use to build with it) is less flexible than some rivals, making it harder to customize

5. Llama 3.1 (Meta)

Tagline: “The open-source flagship”
Meta’s Llama 3.1 is the hero of the open-source world. Released for free, it’s become a favorite for anyone who wants to run AI on their own terms.

Standout strengths:

• Fully open-source—no hidden code, so developers can tweak it however they want
• It supports multiple languages and works with text, images, and audio
• The Pro version handles 128,000 tokens, and it’s easy to run on your own servers (no need to rely on the cloud)
• A huge community of developers contributes updates and tools, making it better every month

Downsides:

• Its Chinese skills are just average—you’ll need to fine-tune it with extra Chinese data to get top results
• It’s a resource hog, needing powerful GPUs to run smoothly

6. DeepSeek-V3 (DeepSeek)

Tagline: “Math and code whiz, easy on the wallet”
This underdog from Chinese startup DeepSeek has made waves with its standout skills in two key areas: writing code and solving math problems.

Standout strengths:

• Its coding and math abilities rival GPT-4.5, but it costs far less to use
• It handles long inputs (128,000 tokens) and responds quickly—great for engineers or researchers
• It offers a free API for small projects, making it easy to test out

Downsides:

• It’s still working on adding image or audio support (for now, it’s text-only)
• Being from a smaller company, it lacks the brand recognition of giants like Google or OpenAI

7. Ernie Bot 4.5 (Baidu)

Tagline: “Chinese knowledge guru”
Baidu’s Ernie Bot 4.5 is like having a walking encyclopedia of Chinese language and culture, thanks to its tight integration with Baidu’s massive knowledge graph.

Standout strengths:

• It’s unbeatable for Chinese knowledge questions—whether you’re asking about ancient history, local customs, or technical terms
• It works with voice and images, making it great for apps: think voice-controlled assistants or tools that analyze photos
• It pulls in info from Baidu’s search engine, so its answers are fact-checked against a huge database

Downsides:

• It’s less creative than models like GPT-4.5—great for facts, but not as good for stories or marketing copy
• Its open-source community is smaller, so there are fewer tools to tweak it

8. Mixtral 8×22B (Mistral AI)

Tagline: “Europe’s efficient polyglot”
This French-built model is a hit in Europe, thanks to its smarts with European languages and its ability to run cheaply.

Standout strengths:

• Uses a “sparse expert” design that makes it super efficient—it costs less to run than most models of its power
• It’s fantastic with French, German, Spanish, and other European languages, understanding local slang and idioms
• Partially open-source, so businesses can tweak it for regional needs

Downsides:

• It struggles with Chinese—it’s not built for Asian languages
• Its large file size makes it hard to deploy without powerful servers

9. Command R+ (Cohere)

Tagline: “Enterprise-grade organizer”
Cohere’s Command R+ is built for big businesses, with a focus on managing and making sense of huge piles of company documents.

Standout strengths:

• It’s optimized for “retrieval-augmented generation” (RAG)—a fancy term for pulling answers from your company’s own files (like employee handbooks or client records)
• It handles multilingual documents effortlessly, making it great for global companies
• Built-in security features keep sensitive data safe, which is a must for banks, hospitals, or legal firms

Downsides:

• It’s not as good for casual chat or creative writing as more general models
• It’s focused almost entirely on business users, so regular folks rarely use it

10. Yi-1.5 (01.ai)

Tagline: “Bilingual long-form wizard”
Founded by AI expert Kai-Fu Lee, 01.ai’s Yi-1.5 shines when it comes to handling long texts and switching between Chinese and English.

Standout strengths:

• It handles 200,000 tokens—perfect for translating long documents or summarizing multi-chapter reports
• Its Chinese and English skills are evenly matched, making it great for cross-border teams
• Open-source versions let developers tweak it, and a growing community is building tools for it

Downsides:

• It’s still working on adding image or audio support (for now, it’s text-only)
• It has fewer built-in tools than top models, so developers have to do more work to integrate it into apps

Quick-Look Comparison Table

Model	Max Context	Open Source	English Level	Multimodal	Best For
GPT-4.5	128 K	❌	⭐⭐⭐⭐⭐	✅	General, creative, coding
Claude 3.5	200 K	❌	⭐⭐⭐⭐⭐	✅	Long docs, compliance tasks
Qwen3	128 K	Partial	⭐⭐⭐⭐	✅	Chinese apps, agents
Gemini 1.5	1 M (exp.)	❌	⭐⭐⭐⭐	✅	Search + office suite
Llama 3.1	128 K	✅	⭐⭐⭐⭐	✅	On-prem & customization
DeepSeek-V3	128 K	API only	⭐⭐⭐⭐	❌	Math, code, research
Ernie 4.5	32 K	❌	⭐⭐⭐	✅	Chinese QA & knowledge
Mixtral	64 K	Partial	⭐⭐⭐⭐	✅	European langs, cost-efficiency
Command R+	128 K	❌	⭐⭐⭐⭐	✅	Enterprise RAG & docs
Yi-1.5	200 K	✅	⭐⭐⭐⭐	❌	Long docs, bilingual writing

10-Second Selection Guide

• English-heavy or global work → GPT-4.5, Claude, Llama, Gemini
• Chinese-first → Qwen3, Ernie, Yi-1.5
• Math / code → DeepSeek-V3, GPT-4.5
• Ultra-long papers & contracts → Claude 3.5, Yi-1.5, Gemini 1.5
• Budget-friendly or on-prem → Llama 3.1, open Qwen3
• Enterprise security & compliance → Claude, Command R+

Conclusion: What’s Next for LLMs?​

In 2025, these big language models aren’t just fancy text generators anymore. They’re starting to act more like “AI helpers” — ones that can make their own decisions, use tools, and even understand what’s going on around them.​

Looking ahead, we’ll probably see more “personalized” models. Imagine an AI that knows exactly how you like to work, what you already know, and what you need help with — and adjusts itself just for you. For businesses looking to harness these advanced models, having the right infrastructure is key. Tools like WhaleFlux, an intelligent GPU resource management tool designed for AI enterprises, will play a crucial role. By optimizing the efficiency of multi-GPU clusters, WhaleFlux helps reduce cloud computing costs while accelerating the deployment speed and stability of large language models — ensuring that even the most cutting-edge LLMs can be put to work smoothly and cost-effectively.​

Whether you’re a coder, a business boss, or just someone who uses AI for fun, knowing the differences between these top models will help you pick the right one. Technology keeps getting better and better, but the real skill is figuring out which tool works best for your job. And that’s something that won’t change anytime soon.