Most powerful LLMs (Large Language Models) in 2025
Read Time 28 mins | Written by: Cole

[Last updated: Sep. 2025]
The LLMs (Large Language Models) underneath the hood of ChatGPT, Claude, Copilot, Cursor, and other generative AI tools are the main tech your company needs to understand.
LLMs make chatbots possible (internal and customer-facing), can assist in increasing coding efficiency, and are the driving force behind why Nvidia exploded into the most valuable company in the world.
Model size, context window size, performance, cost, and availability of these LLMs determine what you can build and how expensive it is to run.
Here are the important stats (context window, availability, pricing, etc.) for the most powerful LLMs available.
LLMs (Large Language Models) for enterprise systems
OpenAI LLMs
ChatGPT and OpenAI are household names when it comes to large language models (LLMs). They started the generative AI firestorm with $10 billion in Microsoft funding and their GPT models have been at the top of the best LLMs available ever since.
With the August 2025 release of GPT-5, OpenAI has introduced unified reasoning capabilities and significantly improved performance across all domains.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost (per M tokens In/Out) |
---|---|---|---|---|---|---|
GPT-5 | Undisclosed (System) | 400,000 tokens (272K input + 128K output) | 128,000 tokens | September 2024 | Flagship unified model with built-in thinking capabilities. State-of-the-art across coding (74.9% SWE-bench), math (94.6% AIME 2025), health (46.2% HealthBench), and multimodal tasks. Automatic routing between fast responses and deep reasoning. 45% less likely to hallucinate than GPT-4o. | $1.25 / $10.00 |
GPT-5-mini | Undisclosed | 400,000 tokens (272K input + 128K output) | 128,000 tokens | May 2024 | Smaller, faster version of GPT-5 with reasoning capabilities. Optimized for cost-efficiency while maintaining strong performance. Supports verbosity control and minimal reasoning mode. | $0.25 / $2.00 |
GPT-5-nano | Undisclosed | 400,000 tokens (272K input + 128K output) | 128,000 tokens | May 2024 | Most cost-effective GPT-5 variant for high-volume applications. Maintains core reasoning abilities with optimized inference speed. | $0.05 / $0.40 |
GPT-5-Codex | Undisclosed | 400,000 tokens (272K input + 128K output) | 128,000 tokens | September 2024 | Specialized coding model optimized for agentic coding tasks with reasoning token support. SOTA performance on coding benchmarks. Designed for Codex IDE integration, GitHub workflows, and independent long-running coding tasks (up to 7+ hours). 93.7% fewer tokens than GPT-5 for simple tasks. | $1.25 / $10.00 |
o4-mini | Undisclosed | 128,000 tokens | 65,536 tokens | January 2025 | Latest reasoning model optimized for fast, cost-efficient reasoning. Best-performing model on AIME 2024/2025. Outperforms o3-mini on both STEM and non-STEM tasks. Supports significantly higher usage limits. | $3.00 / $12.00 |
GPT-4.1 | Undisclosed | 1,047,576 tokens | 32,768 tokens | June 2024 | Advanced model with enhanced reasoning accuracy and improved performance. Larger context window for complex tasks and document processing. Strong performance across coding, math, and multimodal understanding tasks. | $2.00 / $8.00 |
GPT-4o | Undisclosed | 128,000 tokens | 16,384 tokens | October 2023 | Multimodal model with vision, audio, and text capabilities. Still available for users who prefer its warmer, more conversational tone. Being phased out in favor of GPT-5. | $5.00 / $15.00 |
Context window notes:
- API access: All GPT-5 models support up to 400,000 tokens (w/ 128K output)
- ChatGPT interface: Context varies by subscription tier:
- Free tier: 8,000 tokens per conversation
- Plus tier: 32,000 tokens per conversation
- Pro/enterprise tiers: 128,000 tokens per conversation
- Reasoning token support: GPT-5-Codex includes reasoning tokens in its output allocation
Availability:
- GPT-5: Available to all ChatGPT users (Free, Plus, Pro, Team, Enterprise)
- GPT-5 Pro: Extended reasoning version available to Pro and Team users
- API access: All variants available via OpenAI API platform
- Enterprise integration: Available through Microsoft Copilot and enterprise partnerships
Anthropic LLMs
Anthropic was founded by ex-OpenAI VPs who wanted to prioritize safety and reliability in AI models. They moved slower than OpenAI but their Claude 3 family of LLMs were the first to take the crown from OpenAI GPT-4 on the leaderboards in early 2024.
Anthropic followed up with their groundbreaking Claude 4 family, including Claude 4 Opus and Claude 4 Sonnet for advanced coding tasks and reliable enterprise tools.
In August 2025, they released Claude Opus 4.1 and upgraded Claude Sonnet 4 with a 1 million token context window.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost (per M tokens Input/Output) |
---|---|---|---|---|---|---|
Claude Opus 4.1 | Not disclosed | 200,000 tokens | 8,192 tokens | November 2024 | Most capable and intelligent model with superior reasoning capabilities. Sets new standards in complex reasoning and advanced coding. Highest level of intelligence for the most challenging tasks. Multimodal (text and image input). | $15.00 / $75.00 |
Claude Sonnet 4 | Not disclosed | 200,000 tokens (1M with beta header) |
8,192 tokens | November 2024 | High-performance model with exceptional reasoning and efficiency. Balanced performance for most use cases. 1M token context available with beta header. Multimodal (text and image input). | $3.00 / $15.00 |
Claude Haiku 3.5 | Not disclosed | 200,000 tokens | 8,192 tokens | July 2024 | Fastest model with intelligence at blazing speeds. Quick and accurate performance for near-instant responsiveness. Ideal for high-volume, low-latency applications. Multimodal (text and image input). | $0.25 / $1.25 |
Google LLMs
Google was notoriously far behind on commercial LLMs – even though a Google team developed the revolutionary transformer technology that makes LLMs possible.
They've since not only caught up but established leadership in multimodal AI with their Gemini family. The current Gemini models represent the state-of-the-art in thinking models, with Gemini 2.5 Pro leading common benchmarks by significant margins and debuting at #1 on LMArena.
Google's strategic advantage lies in their comprehensive ecosystem: all current Gemini models feature 1-million token context windows, native multimodal processing (text, image, audio, video), and built-in tool integration with Google Search and code execution.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost (per M tokens In/Out) |
---|---|---|---|---|---|---|
Gemini 2.5 Pro | Not disclosed | 1,000,000 tokens (2M tokens coming soon) |
65,536 tokens | October 2024 | State-of-the-art thinking model with adaptive reasoning capabilities. Excels in complex math, coding, and STEM problems. Enhanced problem-solving with Deep Think mode. Multimodal (text, image, audio, video input). | $1.25–$2.50 / $10.00–$15.00 |
Gemini 2.5 Flash | Not disclosed | 1,000,000 tokens | 65,536 tokens | October 2024 | Best price-performance balance. Hybrid reasoning with controllable thinking budgets. Large-scale processing, agentic use cases. Native tool use, grounding with Google Search. Multimodal input support. | $0.30 / $2.50 |
Gemini 2.5 Flash-Lite | Not disclosed | 1,000,000 tokens | 65,536 tokens | October 2024 | Most cost-efficient 2.5 model. Optimized for high-volume, low-latency tasks. Controllable thinking budgets (off by default). Native tools support, classification, and translation tasks. | $0.10 / $0.40 |
Mistral LLMs
Mistral AI is a leading French AI company specializing in developing cutting-edge large language models (LLMs) designed for efficiency, performance, and accessibility. With a strong commitment to open-source innovation and affordable premium offerings, Mistral AI has positioned itself as a leading provider in the AI ecosystem, catering to both enterprise and community-driven use cases.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost (per M tokens In/Out) |
---|---|---|---|---|---|---|
Mistral Large 2 | Not disclosed | 128,000 tokens | 32,768 tokens | July 2024 | Flagship reasoning model with top-tier capabilities ranking as world's 2nd best API-accessible model. Native multilingual fluency, function calling, JSON mode, and Azure partnership integration. | $3.00 / $9.00 |
Mistral Medium 3.1 | Not disclosed | 131,000 tokens | 65,536 tokens | August 2025 | Cost-efficient frontier model delivering 90%+ of Claude Sonnet 3.7 performance at significantly lower cost. Excels in coding, STEM, and enterprise workflows. Multimodal with text and image understanding. | $0.40 / $2.00 |
Codestral | 22 billion | 32,000 tokens | 32,768 tokens | May 2024 | Developer-focused AI model trained on 80+ programming languages. Excels at code generation, fill-in-the-middle completion, test writing. Native IDE integrations with VSCode and JetBrains. | $1.00 / $3.00 |
Magistral Medium | Not disclosed | 128,000 tokens | 65,536 tokens | June 2025 | Advanced reasoning model with transparent chain-of-thought processing. 73.6% on AIME2024. Multilingual reasoning across 8+ languages. Designed for compliance-heavy industries requiring auditability. | Enterprise Pricing |
Best LLMs for coding & software development
These LLMs solve complex problems and deliver code that can be used to build production applications faster – not just vibe code a prototype.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost (per M tokens In/Out) |
---|---|---|---|---|---|---|
Claude Opus 4 | Not public | 200,000 tokens | 32,000 tokens | Mar 2025 | Excels at coding & complex problem-solving; sustained performance on long tasks; extended tool & agent workflows | $15.00 / $75.00 |
Claude Sonnet 4 | Not public | 200,000 tokens | 64,000 tokens | Mar 2025 | Superior coding & reasoning; precise instruction following; 72.7% on SWE-bench Verified | $3.00 / $15.00 |
Claude 3.7 Sonnet | ~175 B | 200,000 tokens |
Normal: 8,192 tokens Extended Thinking: 64,000 tokens (128,000 tokens with beta API) |
Oct 2024 | Exceptional reasoning; full development lifecycle support; state-of-the-art coding accuracy | $3.00 / $15.00 |
GPT-4.1 | Not public | 1,000,000 tokens | 32,768 tokens | June 2024 | Improved coding efficiency; code optimization & security analysis; 54.6% on SWE-bench Verified | $2.00 / $8.00 |
GPT-4o | ~1.8 T | 128,000 tokens | 16,384 tokens | October 2023 | Multimodal code understanding; rapid prototyping with vision & text | $2.50 / $10.00 |
o3 | Not public | 200,000 tokens | 100,000 tokens | June 2024 | Reflective reasoning; strong STEM & algorithmic performance | $10.00 / $40.00 |
o3-mini | Not public | 200,000 tokens | 100,000 tokens | September 2023 | Affordable educational model; 49.3% on SWE-bench | $1.10 / $4.40 |
o4-mini | Not public | 200,000 tokens | 100,000 tokens | June 2024 | Fast, cost-efficient; excels in math & visual analysis | $1.10 / $4.40 |
Gemini 2.5 Pro | Not public | 1,048,576 tokens | 65,535 tokens | May 2024 | Deep Think reasoning; top full-stack development performance | $1.25–$2.50 / $10.00–$15.00 |
DeepSeek R1 | 671 B MoE (37 B active) | 128,000 tokens | Not specified | Jan 2025 | Mixture-of-Experts; exceptional math & algorithmic reasoning | Open-source (MIT license) |
DeepSeek V3 | 671 B (37 B active) | 64,000 tokens | Not specified | Mar 2025 | Distilled reasoning; improved performance control & efficiency | Open-source (MIT license) |
Open source LLMs for enterprise
DeepSeek Open Source LLMs
DeepSeek shocked the AI community in 2025 by releasing the open-source model DeepSeek-R1, which demonstrated competitive performance against leading proprietary frontier models, challenging the traditional dominance of closed-source solutions.
Because of development in China and assertions from OpenAI, DeepSeek has some large security risks to account for before using for enterprise.
Model | Parameters | Context Window | Knowledge Cutoff | Strengths & Features | License Type |
---|---|---|---|---|---|
DeepSeek-R1 | 671 billion (MoE) | 64K | 8k |
Excels in reasoning-intensive tasks, including code generation and complex mathematical computations.
|
MIT License |
DeepSeek-V3 | Not publicly disclosed | 64K | 8k |
Outperforms other open-source models; achieves performance comparable to leading closed-source models.
|
MIT License |
DeepSeek-Coder-V2 | 236 billion | 16k | Not specified |
Enhanced coding and mathematical reasoning abilities; pre-trained on 6 trillion tokens.
|
MIT License |
DeepSeek-VL | Not publicly disclosed | Not specified | Not specified | Designed to enhance multimodal understanding capabilities. | MIT License |
Nvidia Open Source LLMs
Nvidia is known for their GPUs but they have a whole enterprise AI ecosystem – from dev tools to their NIM microservices platform. They had early entries into LLM space with ChatRTX and Starcoder 2 but their most powerful LLM offering is the Nemotron-4 340B model family.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Availability | License Type |
---|---|---|---|---|---|---|---|
Nemotron-4 340B Base | 340 billion | 4,096 tokens | 4,000 tokens | June 2023 |
Base model for synthetic data generation; trained on 9 trillion tokens across English texts, 50+ natural languages, and 40+ coding languages.
|
NVIDIA NGC, Hugging Face | NVIDIA Open Model License |
Nemotron-4 340B Instruct | 340 billion | 4,096 tokens | 4,000 tokens | June 2023 |
Fine-tuned model optimized for English conversational AI (single- and multi-turn interactions).
|
NVIDIA NGC, Hugging Face | NVIDIA Open Model License |
Nemotron-4 340B Reward | 340 billion | 4,096 tokens | 4,000 tokens | June 2023 | Multidimensional Reward Model designed for evaluating outputs and generating synthetic training data. | NVIDIA NGC, Hugging Face | NVIDIA Open Model Licens |
Meta Llama 3 Open Source LLMs
While Meta is commonly known for being a champion of open source in AI, their models are open weights and not true open source according to many. Either way, open weights still means you can run these models locally – which you can't do with OpenAI LLMs.
Model |
Parameters |
Context Window |
Max Output Tokens |
Knowledge Cutoff |
Strengths & Features |
License Type |
Cost |
70 billion |
128,000 tokens |
Not specified |
December 2023 |
General-purpose multilingual model with optimized transformer architecture, pretrained on 15T tokens.
|
Llama 3.3 Community License |
Free (open-source) |
|
70 billion |
128,000 tokens |
Not specified |
December 2023 |
Instruction-tuned multilingual model optimized for conversational tasks with RLHF fine-tuning.
|
Llama 3.3 Community License |
Free (open-source) |
|
1.23 billion |
128,000 tokens |
Not specified |
December 2023 |
Lightweight multilingual model, optimized for mobile AI applications, retrieval, summarization, and chat use cases.
|
Llama 3.2 Community License |
Free (open-source) |
|
3.21 billion |
128,000 tokens |
Not specified |
December 2023 |
Mid-sized multilingual model for agentic retrieval, summarization, conversational tasks, and efficient inference.
|
Llama 3.2 Community License |
Free (open-source) |
|
1.23 billion |
8,000 tokens |
Not specified |
December 2023 |
Quantized for highly constrained environments, optimized for mobile and edge use cases with minimal compute needs.
|
Llama 3.2 Community License |
Free (open-source) |
|
3.21 billion |
8,000 tokens |
Not specified |
December 2023 |
Efficiently quantized, optimized for resource-constrained deployments, suitable for mobile and embedded AI. |
Llama 3.2 Community License |
Free (open-sourc |
Qwen Open Source LLMs
Qwen refers to the LLM family built by Alibaba Cloud. Qwen2 has generally surpassed most open source models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | License Type |
---|---|---|---|---|---|---|
Qwen-2.5-7B | 7 billion | Not specified | Not specified | Not specified |
Enhanced general-purpose capabilities with improved performance.
|
Apache 2.0 |
Qwen-2.5-14B | 14 billion | Not specified | Not specified | Not specified |
Higher performance for more complex tasks and reasoning scenarios.
|
Apache 2.0 |
Qwen-2.5-32B | 32 billion | Not specified | Not specified | Not specified |
Advanced model suitable for highly complex tasks, reasoning, and language generation.
|
Apache 2.0 |
Qwen-2.5-72B | 72 billion | Not specified | Not specified | Not specified |
Large-scale model offering extensive capabilities in deep understanding and generation tasks.
|
Apache 2.0 |
Qwen-2.5-7B-Instruct-1M | 7 billion | Up to 1 million tokens | Not specified | Not specified |
Instruction-tuned, supports extended contexts, optimized for tasks requiring long context understanding.
|
Apache 2.0 |
Qwen-2.5-14B-Instruct-1M | 14 billion | Up to 1 million tokens | Not specified | Not specified |
Larger instruction-tuned model designed for complex tasks requiring extensive context.
|
Apache 2.0 |
Qwen-2.5-Coder-32B-Instruct | 32 billion | Not specified | Not specified | Not specified |
Optimized specifically for coding tasks, demonstrating state-of-the-art programming capabilities.
|
Apache 2.0 |
Qwen-2-VL-Instruct-7B | 7 billion | Not specified | Not specified | Not specified | Multimodal model with vision-language capabilities, optimized for instruction-following tasks. | Apache 2.0 |
Mistral AI Open Source LLMs
Mistral AI has positioned itself as a leading provider in the AI ecosystem, catering to both enterprise and community-driven use cases.
Model | Parameters | Context Window | Max Output Tokens | Knowledge Cutoff | Strengths & Features | Cost |
---|---|---|---|---|---|---|
Mistral Small (v3.1) | 24 billion | 131,000 tokens | Not specified | Mar 2025 |
Leader in small-model category; strong in text and image understanding.
|
Free (open-source) |
Pixtral (12B) | 12 billion | 131,000 tokens | Not specified | Sep 2024 |
Mid-sized multimodal model optimized for efficient text and image processing.
|
Free (open-source) |
Mistral Nemo | Not publicly disclosed | 131,000 tokens | Not specified | Jul 2024 |
Robust multilingual capabilities supporting extensive international languages.
|
Free (open-source) |
Codestral Mamba | Not publicly disclosed | 256,000 tokens | Not specified | Jul 2024 |
Specialized Mamba architecture for rapid inference and efficient code generation.
|
Free (open-source) |
Mathstral | Not publicly disclosed | 32,000 tokens | Not specified | Jul 2024 | Specialized model optimized for mathematical reasoning and computational problem-solving. | Free (open-source) |
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Cole
Cole is Codingscape's Content Marketing Strategist & Copywriter.