Marktechpost Releases ‘AI2025Dev’: A Standardized Intelligence Framework for AI Models, Benchmarks, and Ecosystem Indicators

Marktechpost released AI2025Devits 2025 statistics platform (available to AI Devs and Researchers without registration or login) designed to turn a year’s worth of AI work into a queryable dataset that includes model output, openness, training scale, benchmark performance, and ecosystem participants. Marktechpost is a California-based AI news platform covering machine learning, deep learning, and data science research.

What’s new in this release

2025 release of AI2025Dev extend coverage across two layers:

1. Get rid of the mathfocusing on model implementation and framework, licensing structure, vendor activity, and feature level classification.
2. Ecosystem indicatorsincluding curated “Top 100” collections that connect the models to the papers and the people and money behind them. This release includes the following sections:

• Top 100 research papers
• Top 100 AI Researchers
• Top AI startups
• Top AI innovators
• Top AI investors
• Sponsorship views which connects investors with companies

These indexes are designed to be navigable and sortable, rather than static lists, so teams can track relationships across artifacts such as company, model type, benchmark scores, and release date.

AI Releases 2025: year-level metrics from market map dataset

AI2025Dev‘AI Releases to 2025’ overview supported by systematic market mapping dataset 100 consecutive releases again 39 active companies. The dataset adapts each entry to a consistent schema: name, company, type, license, flagshipagain release_date.

Key integrated indicators in this release include:

• Total issued: 100
• Share open: 69%calculated as a combined share of Open Source again Open Weights releases (44 and 25 entries respectively), with 31 Pertaining to release
• Flagship models: 63which allows separation of the implementation of the boundary layer from other derivatives or sub-scope implementations
• Operating companies: 39which shows a cluster of large releases among a limited set of vendors

The classification of the model in the market map is clearly written, allowing for different questions and comparative analysis. Distribution includes LLM (58), Agentic Model (11), Conceptual Model (8), Tool (7), Multimodal (6), Frame (4), Code Model (2), Sound model (2)plus Embedding model (1) again Agent (1).

Key Findings 2025: Phase-level shifts captured as measurable signals

The release packs a ‘Key Results 2025’ layer that shows the annual rate of change as measurable pieces of the dataset instead of comments. The platform highlights three emerging technology themes:

• Open to receive weightsit captures the growing share of weighted releases that are available under open source or open weight principles, and an expression that means more teams can scale, fine-tune, and use without vendor-locked understanding.
• Agentic and tool using systemstracks the development of models and systems separated by tooling, orchestration, and execution, rather than pure conversational interaction.
• Efficiency and pressurewhich shows the pattern of 2025 where distillation and other model development techniques are increasingly targeting small steps while maintaining competitive benchmark behavior.

LLM Training Data Scale to 2025: token scale with timeline

Dedicated visual tracks Data scale for LLM training in 2025to combine 1.4T to 36T tokens and managing the token budget a release timeline. By encoding the token scale and date in a single view, the platform makes it possible to compare how marketers have allocated training budgets over time and how the extreme scale correlates with the observed benchmark results.

Performance benchmarks: standard score benchmarks and assessments

The math section includes a Performance benchmarks watch and with Intelligence Index derived from standard test axes, incl MMLU, HumanEvalagain GSM8K. The intent is not to replace direct performance appraisals, but to provide a consistent basis for comparing vendor releases where public reporting differs in format and completeness.

The platform discloses:

• Limited performance summaries quick scan
• For benchmark columns finding trade-offs (for example, advanced coding models that differ from average cognitive performance)
• Export controls supporting a descending analysis workflow

Model leaderboard and Model Comparison: workflow for performance evaluation

To reduce the conflict of choice of models, AI2025Dev includes:

• A Model leaderboard which includes scores and metadata for a comprehensive 2025 model set
• A Model Comparison view that enables integrated evaluation across benchmarks and attributes, with search and filtering to create shortlists by vendor, brand, and availability

This workflow is designed for engineering teams that need a structured benchmarking environment before committing to integration, costing, or fine-tuning pipelines.

Top 100 references: papers, researchers, startups, and investors

Beyond model tracking, the release extends to ecosystem mapping. The platform adds “Top 100” modules that can be moved:

• Research papersit provides an entry point into the core professional work that shapes the systems of 2025
• AI researcherspresented as an unranked, evidence-based reference with a focused conference context
• AI startups and innovatorswhich allows communication between product direction and released systems
• AI investors and fundinganalysis that enables capital flows around model and instrument categories

Availability

The updated platform is now available AI2025Dev and you don’t need to register or login to access the platform. The release is designed to support both fast scanning and analyst-grade workflows, with standard schemas, typed categories, and exportable views intended for quantitative comparisons rather than narrative browsing.

Asif Razzaq is the CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, Asif is committed to harnessing the power of Artificial Intelligence for the benefit of society. His latest endeavor is the launch of Artificial Intelligence Media Platform, Marktechpost, which stands out for its extensive coverage of machine learning and deep learning stories that sound technically sound and easily understood by a wide audience. The platform boasts of more than 2 million monthly views, which shows its popularity among viewers.