LLM Benchmarks

Leading Large Language Models Compared

Monthly performance analyses of leading language models – from OpenAI and Google to local open-source solutions.

The AI Strategy & Research Hub of TIMETOACT GROUP Austria is among the leading experts in applied research on generative AI for enterprises. Our research findings feed directly into product development – enabling us to set the highest standards in implementing AI-powered solutions for businesses.

LLM Benchmarks | March 2025

Highlights:

  • New OpenAI models - o3-mini, o4-mini and GPT-4.1
  • Qwen3 - pushing state-of-the-art for local models
  • Google Is Breathing Down the Neck: Gemini Flash 2.5 Preview and Pro 2.5 v2
  • Practical insights from AI Coding
Explore the April LLM Benchmarks

The benchmark categories in detail

Here's exactly what we're looking at with the different categories of LLM Leaderboards

Docs

How well can the model work with large documents and knowledge bases?

CRM

How well does the model support work with product catalogs and marketplaces?

Integrate

Can the model easily interact with external APIs, services and plugins?

Marketing

How well can the model support marketing activities, e.g. brainstorming, idea generation and text generation?

Reason

How well can the model reason and draw conclusions in a given context?

Code

Can the model generate code and help with programming?

Cost

The estimated cost of running the workload. For cloud-based models, we calculate the cost according to the pricing. For on-premises models, we estimate the cost based on GPU requirements for each model, GPU rental cost, model speed, and operational overhead.

Speed

The "Speed" column indicates the estimated speed of the model in requests per second (without batching). The higher the speed, the better.

Archive

Curious about how the scores have evolved? Here you can find all links to previously published leaderboards

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