Altar-1Aikido Security
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Mistral NeMoMistral AI
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Related Products
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About
Aikido Altar is an open-weight security model built to bring frontier-grade defensive security intelligence into infrastructure organizations' control. It is designed for sovereign security environments where sensitive source code, architecture documentation, vulnerability findings, and other internal context cannot be sent to third-party inference services. Altar is based on GLM-5.3 and uses quantization and expert pruning to reduce the model from 1.51 TB at full precision to 328 GB while preserving most of the parent model’s reasoning and security capabilities. It retains 168 of the original 256 routed experts per backbone expert layer and uses a W4A16 representation, making deployment more practical for agentic security workloads with large and growing context windows. Expert selection was calibrated using internal pentesting traces and multilingual data, with no customer data involved, to preserve cybersecurity, coding, and language understanding.
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About
Mistral NeMo, our new best small model. A state-of-the-art 12B model with 128k context length, and released under the Apache 2.0 license. Mistral NeMo is a 12B model built in collaboration with NVIDIA. Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. As it relies on standard architecture, Mistral NeMo is easy to use and a drop-in replacement in any system using Mistral 7B. We have released pre-trained base and instruction-tuned checkpoints under the Apache 2.0 license to promote adoption for researchers and enterprises. Mistral NeMo was trained with quantization awareness, enabling FP8 inference without any performance loss. The model is designed for global, multilingual applications. It is trained on function calling and has a large context window. Compared to Mistral 7B, it is much better at following precise instructions, reasoning, and handling multi-turn conversations.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Security teams and regulated organizations needing to run AI-powered vulnerability research, code analysis, remediation, and pentesting entirely within infrastructure they control
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Audience
Users looking for a language model tool to power their AI-driven applications
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
$350 per month
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Open source
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationAikido Security
Founded: 2022
Belgium
www.aikido.dev/blog/aikido-altar-open-weight-ai-sovereign-security
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Company InformationMistral AI
Founded: 2023
France
mistral.ai/news/mistral-nemo/
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AlternativesNo Alternatives
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Categories |
Categories |
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Integrations
Agent Control
Not Supported
Diaflow
Not Supported
HoneyHive
Not Supported
JavaScript
Not Supported
Lunary
Not Supported
Mathstral
Not Supported
Microsoft Foundry Models
Not Supported
Mistral AI
Not Supported
NexalAI
Not Supported
OpenLIT
Not Supported
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Integrations
Agent Control
Supported
Diaflow
Supported
HoneyHive
Supported
JavaScript
Supported
Lunary
Supported
Mathstral
Supported
Microsoft Foundry Models
Supported
Mistral AI
Supported
NexalAI
Supported
OpenLIT
Supported
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