D
Ai Models Language Models

LLM Market Share 2024

Published:
Jun 15, 2026
Updated:
May 17, 2026
Last reviewed:
May 18, 2026

Research question

How is enterprise demand distributed across frontier large language models?

Short answer

Three frontier providers (GPT-4, Claude, Gemini) collectively control 85% of enterprise LLM deployments.

Source: Industry Analysis

Key findings

  • GPT-4 maintains dominant position with 35% market share
  • Claude shows strong growth, capturing 28% of enterprise deployments
  • Open-source models gaining traction in cost-sensitive segments

Download AI-ready context

Free

Get a structured Markdown file with key findings, chart data, sources, caveats, and prompts for AI research. Built for LLM Market Share 2024.

Context file preview

The downloaded Markdown file includes:

  • Key findings
  • Data table
  • Methodology
  • Source links
  • Suggested AI prompts 
# LLM Market Share 2024

## Research Question
## Short Answer
## Key Findings
## Data Table
## How To Use This With AI
## Suggested Prompts
## Sources
## Caveats

Data table

LLM Market Share 2024 — data table
name value
GPT-4 35
Claude 28
Gemini 22
Llama 10
Others 5

Analysis

The large language model market has consolidated around three major players, with GPT-4, Claude, and Gemini collectively controlling 85% of enterprise deployments. This concentration reflects the massive capital requirements for frontier model training, while open-source alternatives like Llama carve out niches in cost-sensitive applications. The trajectory suggests continued consolidation at the frontier tier, with growing open-source activity at the mid-tier performance level.

How to use this with AI

Use this context file to start a research conversation with an AI tool. It includes the key findings, chart data, sources, and caveats so the model starts from structured context instead of a blank prompt.

Use cases

  • Compare frontier LLM providers when shortlisting an enterprise AI vendor.
  • Build a market-sizing memo for an AI infrastructure investment thesis.
  • Sketch a build-vs-buy analysis for an internal LLM workload.

Suggested prompts

  • Using this Deepstory context on LLM market share, recommend three vendor shortlists for a Fortune 500 buyer optimizing for (a) capability, (b) cost, and (c) data control. Explain trade-offs.
  • Turn this LLM market share data into a one-page strategic brief for a CIO weighing single-vendor vs multi-vendor LLM strategy.

Sources

Source quality:
Placeholder / demo data
Last reviewed:
May 18, 2026
  • Industry Analysis Placeholder / demo

    Aggregated from deployment reports and API usage data

Caveats

  • Market share figures are aggregated estimates, not audited; underlying source is placeholder/demo for now.
  • Enterprise deployment share does not equal token volume or revenue share.

How relevant was this information?

#market-share #language-models #enterprise