Summary guide

Best AI models for summarization

This page favors text-capable models with enough context and reasonable pricing to summarize meetings, support threads, articles, and internal notes at production scale.

Live signalLarge enough context for long threads and documents
Live signalText-focused capability for note cleanup and summaries
Live signalPricing that works for repeat summarization jobs
Live signalUseful for meetings, docs, support history, and content repackaging
Fast answer

Start with the live shortlist, then validate the route

Summarization models need enough context to hold the full input and pricing low enough to process long material repeatedly. Reliable text quality matters more than frontier reasoning alone.

Data source and freshness

Catalog-backed, not a static price sheet

TVP refreshes this page from the live OpenRouter model catalog. This render used 480 public model records and was synchronized Jul 31, 2026, 9:45 AM UTC. Pricing, availability, and context values can change.

Verify the exact route before sending production traffic, then use the linked model and provider pages as the source for current values. Read the TVP data methodology.

Shortlist

Top live candidates right now

x-ai

xAI: Grok 4.20

Grok 4.20 is a reasoning model from xAI with industry-leading speed and agentic tool calling capabilities. It combines the lowest hallucination rate on the market with strict prompt adherance, delivering...

Context2,000,000
Input$1.25
Output$2.50

2,000,000 token context makes it suitable for long threads, notes, and document summarization.

  • text
  • image
  • file
  • tools
  • structured
x-ai

xAI: Grok 4.20 Multi-Agent

Grok 4.20 Multi-Agent is a variant of xAI’s Grok 4.20 designed for collaborative, agent-based workflows. Multiple agents operate in parallel to conduct deep research, coordinate tool use, and synthesize information...

Context2,000,000
Input$1.25
Output$2.50

2,000,000 token context makes it suitable for long threads, notes, and document summarization.

  • text
  • image
  • file
  • structured
meta-llama

Meta: Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

Context1,310,720
Input$0.1
Output$0.3

1,310,720 token context makes it suitable for long threads, notes, and document summarization.

  • text
  • image
  • tools
  • structured
xiaomi

Xiaomi: MiMo-V2.5

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...

Context1,050,000
Input$0.14
Output$0.28

1,050,000 token context makes it suitable for long threads, notes, and document summarization.

  • text
  • audio
  • image
  • video
  • tools
  • structured
openai

OpenAI: GPT-5.6 Luna

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...

Context1,050,000
Input$0.1
Output$0.6

1,050,000 token context makes it suitable for long threads, notes, and document summarization.

  • file
  • image
  • text
  • tools
  • structured
FAQ

What buyers usually ask

What makes a good summarization model?

Summarization models need enough context to hold the full input and pricing low enough to process long material repeatedly. Reliable text quality matters more than frontier reasoning alone.

Should summarization use the largest context model possible?

Only when the source is genuinely large. For many jobs a mid-priced model with sufficient context is a better fit than the largest available context window.

Next step

Use the guide, then validate the route in live TVP data.

TVP keeps the shortlist connected to the current catalog, provider coverage, and token pricing so buyers can move from research to routing without starting over.