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Description
Gemini 4 is Google’s upcoming next-generation AI model family and the future successor to its current Gemini 3.x lineup. Google has confirmed that it has started pre-training Gemini 4, describing it as its most ambitious pre-training run so far. The model has not been officially launched, so there are no public API endpoints, pricing details, model cards, benchmark results, or release dates available yet. Gemini 4 is expected to build on the direction of recent Gemini models, including stronger coding, reasoning, multimodal performance, computer use, and agentic workflow support. Google’s current Gemini releases already emphasize efficiency, lower latency, better tool use, and enterprise deployment, and Gemini 4 is likely to extend those priorities at a larger scale. The model may eventually support developers building AI agents, enterprise copilots, coding tools, research assistants, multimodal apps, and knowledge-work automation. It will likely play a role across Google AI Studio, the Gemini API, Gemini Enterprise, Google Cloud, and consumer-facing Gemini experiences once released. For now, Gemini 4 should be treated as a confirmed future model in training rather than an available product. By representing Google’s next major frontier model effort, Gemini 4 signals the company’s continued push toward more capable AI systems for developers, enterprises, and everyday users.
Description
In honor of Archimedes, whose 2311th anniversary we celebrate this year, we are excited to introduce our inaugural Mathstral model, a specialized 7B architecture tailored for mathematical reasoning and scientific exploration. This model features a 32k context window and is released under the Apache 2.0 license. Our intention behind contributing Mathstral to the scientific community is to enhance the pursuit of solving advanced mathematical challenges that necessitate intricate, multi-step logical reasoning. The launch of Mathstral is part of our wider initiative to support academic endeavors, developed in conjunction with Project Numina. Much like Isaac Newton during his era, Mathstral builds upon the foundation laid by Mistral 7B, focusing on STEM disciplines. It demonstrates top-tier reasoning capabilities within its category, achieving remarkable results on various industry-standard benchmarks. Notably, it scores 56.6% on the MATH benchmark and 63.47% on the MMLU benchmark, showcasing the performance differences by subject between Mathstral 7B and its predecessor, Mistral 7B, further emphasizing the advancements made in mathematical modeling. This initiative aims to foster innovation and collaboration within the mathematical community.
API Access
Has API
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Integrations
Airtrain
Amazon Bedrock
BlueGPT
Codestral Mamba
Continue
GMTech
Graydient AI
Ministral 8B
Mistral NeMo
NexalAI
Integrations
Airtrain
Amazon Bedrock
BlueGPT
Codestral Mamba
Continue
GMTech
Graydient AI
Ministral 8B
Mistral NeMo
NexalAI
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Open source
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
gemini.com
Vendor Details
Company Name
Mistral AI
Founded
2023
Country
France
Website
mistral.ai/news/mathstral/