From $10 usage-based🇺🇸
Anthropic's model for demanding reasoning and long-horizon agentic work, with thinking always on.
Best large language models (LLMs) for business (October 2026)
This category lists commercial large language models that companies use through an API or a cloud platform to build their own applications.
In this guide:17 toolsBefore you buyHow to choose large language models (LLMs) for business (October 2026)Questions
17tools in this category
4countries with local strength
5can host data in the EU
How results are ranked: How we review
Results
From $4 usage-based🇺🇸
Anthropic's frontier model for long-running agentic coding and knowledge work, with a one-million-token context.
From $10 usage-based🇺🇸
OpenAI's most capable model for hard end-to-end work, with five reasoning levels and a context of about one million tokens.
From $0.10 usage-based🇺🇸
OpenAI's most affordable GPT-6 model for high volumes of focused tasks, with reasoning levels and image input.
From $1.50 usage-based🇫🇷
Multimodal model from Paris-based Mistral AI for agents and coding, via API and with open weights.
Usage-based · Free trial🇨🇦
Mixture-of-experts model from Toronto-based Cohere with reasoning, image input and 48 languages, also with open weights.
From $3,250 per month · Free trial🇨🇦
Reranking API from Toronto-based Cohere that reorders search results by semantic relevance, multilingual and also for JSON.
From $0.66 usage-based🇨🇳
Reasoning model from Hangzhou-based DeepSeek with one million tokens of context and thinking mode on by default, at a low price per token.
From $2 usage-based🇺🇸
Frontier Grok model for coding, agentic tasks and knowledge work, with four reasoning levels and a 500,000-token context.
From $0.11 usage-based · Free trial🇨🇳
Fast, low-cost Qwen model from Alibaba Cloud with image and video understanding and a Frankfurt deployment.
From $1.65 usage-based · Free trial🇨🇳
Alibaba Cloud's flagship Qwen model with thinking and non-thinking modes, deployable in Frankfurt as well.
From $0.75 usage-based · Free plan🇺🇸
Google's multimodal Flash model for agents and enterprise workflows, taking text, images, video, audio and PDF as input.
Free🇨🇳
DeepSeek's open multimodal mixture-of-experts model with one million tokens of context and a much smaller KV cache.
Free🇺🇸
Google DeepMind's open multimodal model family in five sizes, from E2B for mobile devices to 31B, with thinking modes.
Free🇺🇸
OpenAI's open-weight reasoning models in two sizes, 120b for one 80 GB GPU and 20b for 16 GB of memory, under Apache 2.0.
Free🇫🇷
Mistral AI's open hybrid model that unifies instruct, reasoning and coding, with image input and a 256,000-token context.
Free🇨🇳
Alibaba's current open-weight generation with image and video understanding, adjustable thinking and up to 1M tokens of context.
How to choose large language models (LLMs) for business (October 2026)
This category lists commercial large language models that companies use through an API or a cloud platform to build their own applications. It is split into frontier models with the highest capability, fast and low-cost models for high volumes, reasoning models that think through a problem before answering, and multimodal models that also read images, audio, video or PDF files. Each entry describes one model or model line and the conditions under which the vendor offers it.
In business software these models draft and summarize text, extract fields from documents, classify requests, answer questions from internal knowledge in RAG setups and act as the planning part of AI agents that call tools. Most vendors now let you choose how much the model reasons per request, which changes quality, latency and cost at the same time.
Before you pick a model, test it on your own tasks and languages instead of relying on benchmarks. Compare prices per million input and output tokens, discounts for batch jobs and caching, and surcharges for regional processing. Ask where requests are processed and stored, whether data is used for training and how long the vendor keeps logs. Check how long a model version stays available, since retirements force you to retest your application.
Questions about large language models (LLMs) for business (October 2026)
How are LLMs priced?
Most vendors charge per million input tokens and per million output tokens, often with discounts for batch processing and cached prompts. Reasoning models also bill the tokens they use for thinking, so costs depend on the effort level you choose.
When do I need a reasoning model?
For multi-step analysis, planning in agents and tasks where a wrong answer is costly. For classification, extraction or short replies a fast model is usually enough and much cheaper.
Can I keep data in the EU when using an LLM API?
Several vendors offer EU processing or storage, sometimes for a surcharge or only in certain tiers. Others process only in the US or in China. Check the vendor's data residency documentation for the specific model.
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