Best LLMs For Summarization: Overview, Tables and Costs

Best LLMs For Summarization: Overview, Tables and Costs

November 22, 2024 Hugo Huijer
Looking to dive into the world of AI-powered summarization? I've spent countless hours researching various Large Language Models (LLMs) to help you find the perfect fit for your summarization needs. While I haven't personally tested all these models (hey, transparency is important!), I've gathered data from reliable sources to give you a solid overview of what's available.

What are the best LLMs for summarization?

When it comes to summarizing text efficiently, not all LLMs are created equal. I've narrowed down the options to five standout models that offer different advantages depending on your specific needs. Here's a quick comparison of the top contenders:
ModelContext WindowPrice (Input/Output)Best For
Claude 3 Haiku200K tokens$0.25/$1.25 per 1MHigh-volume, cost-effective summarization
Gemini 1.5 Pro2M tokens$1.25/$5 per 1MVery long documents and books
GPT-4-mini128K tokens$0.15/$0.6 per 1MBalance of quality and cost
Open-mistral-nemo128K tokens$0.3/$0.3 per 1MConsistent pricing, batch processing
Command-r128K tokens$0.15/$0.6 per 1MSpecialized summarization tasks
Let's dive deeper into each option:
Anthropic Logo

Anthropic - Claude 3 Haiku

Looking for an efficient summarizer that won't break the bank? Claude 3 Haiku might be your answer. It's Anthropic's most affordable option, but don't let that fool you – it still packs a punch when it comes to quality. The 200K token context window means you can throw pretty lengthy documents at it without breaking a sweat.
Context Window200K tokens
Input Cost$0.25 per 1M tokens
Output Cost$1.25 per 1M tokens
ProviderAnthropic
Google Gemini Logo

Google - Gemini 1.5 Pro

If you're dealing with massive documents, Gemini 1.5 Pro is the heavyweight champion you're looking for. With its impressive 2M token context window, you could theoretically feed it an entire book! While it's pricier than some alternatives, that extra context space can be a game-changer for certain projects.
Context Window2M tokens
Input Cost$1.25 per 1M tokens
Output Cost$5 per 1M tokens
ProviderGoogle
OpenAI Logo

OpenAI - GPT-4-mini

The GPT-4-mini strikes a sweet spot between cost and capability. It's like getting premium features at a mid-range price point. While its context window isn't the largest, 128K tokens is plenty for most summarization tasks you'll encounter in the real world.
Context Window128K tokens
Input Cost$0.15 per 1M tokens
Output Cost$0.6 per 1M tokens
ProviderOpenAI
Mistral Logo

Mistral - Open-mistral-nemo

Here's something refreshing: Open-mistral-nemo offers the same price for both input and output tokens. This makes it super easy to calculate costs for your projects. With its 128K context window and consistent pricing, it's particularly good for batch processing when you need to summarize multiple documents.
Context Window128K tokens
Input Cost$0.3 per 1M tokens
Output Cost$0.3 per 1M tokens
ProviderMistral
Cohere Logo

Cohere - Command-r

Cohere's Command-r model has built quite a reputation for summarization tasks. It offers competitive pricing similar to GPT-4-mini, and while its context window isn't the largest, it's more than capable of handling most standard documents you'll throw at it.
Context Window128K tokens
Input Cost$0.15 per 1M tokens
Output Cost$0.6 per 1M tokens
ProviderCohere

Remember, the "best" LLM for summarization really depends on your specific needs. If you're processing entire books or research papers, Gemini 1.5 Pro's massive context window might be worth the extra cost. For high-volume, routine summarization, Claude 3 Haiku offers great value. And if you want something in the middle, GPT-4-mini, Open-mistral-nemo, and Command-r all offer solid performance at reasonable prices.

The field of AI is evolving rapidly, so while these recommendations are current as of my research, it's always worth checking the latest offerings and pricing from these providers. Happy summarizing!

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