Discover the best large language models for digital products

The monthly TIMETOACT GROUP Language Model (LLM) Benchmarks help you choose the best AI models for digital product development.

Based on real benchmark data from our own software products, we re-evaluate each month the performance of different LLM models in addressing specific challenges. We examine specific categories such as document processing, CRM integration, external integration, marketing support, and code generation.

LLM Benchmarks | January 2025

    In the first issue of TIMETOACT GROUP LLM Benchmarks V2 we present our new benchmarks and talk about DeepSeek r1.

    Highlights:

    • Second Generation Benchmark - Early Preview
    • DeepSeek r1
    • Cost and price development of DeepSeek r1
    LLM Benchmarks | January 2025

    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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