Curated Model Catalog
Choose from flagship, reasoning, coding and efficient open models across multiple parameter scales.
PRODUCT BENEFITS
From Model Discovery to Production in One Service
Keep teams focused on AI products while EdgeNext AI handles the model access and delivery layer.
Unified Access
Use a consistent service layer as applications move between model families and deployment needs.
Production Operations
Simplify model serving, scaling and operational management for real application traffic.
Freedom to Evolve
Evaluate and adopt new models without rebuilding the application integration every time.
CORE CAPABILITIES
A Model Catalog Built for Real Workloads
Select the right balance of quality, efficiency, architecture and license for each AI experience.
Leading Model Providers in One Catalog
Availability may vary by rollout and region
Moonshot AI
2 modelsKimi-K3-2.8T (50B)MoE · Modified MIT
Kimi-K2.6-1T (32B)MoE · Modified MIT
DeepSeek
3 modelsDeepSeek-V4-Pro-1.6T (49B)MoE · MIT
DeepSeek-V4-Flash-284B (13B)MoE · MIT
DeepSeek-V3-R1-67BDense · MIT
MI
Xiaomi
1 modelMiMo-V2.5-Pro-1.0T (42B)MoE · Xiaomi
Zhipu AI
1 modelGLM-5.2 Max-753B (40B)MoE · MIT
NVIDIA
1 modelNemotron-3-Ultra-550B (55B)MoE · NVIDIA
Meta
1 modelLlama-4-Maverick-400B (17B)MoE · Llama
Alibaba Cloud
4 modelsQwen3.5-397B (17B)MoE · Apache 2.0
Qwen3.7-Max-122BDense · Apache 2.0
Qwen3-32BDense · Apache 2.0
Qwen3-14BDense · Apache 2.0
MiniMax
1 modelMiniMax-M3-428B (23B)MoE · Community
OpenAI
2 modelsgpt-oss-120BDense · Apache 2.0
gpt-oss-20BDense · Apache 2.0
Mistral AI
1 modelMistral-Large-3-41BDense · Apache 2.0
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Microsoft
1 modelPhi-4-mini-3.8BDense · MIT
Flagship MoE Models
Large mixture-of-experts models for demanding reasoning and general intelligence workloads.
Dense Models
Predictable, broadly supported models for focused production use cases.
Multiple Parameter Scales
Match model footprint and response characteristics to each application tier.
License Visibility
Compare model licenses as part of the selection process.
Model Evaluation
Test candidate models before standardizing an application workflow.
Managed Delivery
Move selected models into a consistent production service path.
HOW IT WORKS
A Shorter Path from Model to Application
A simple lifecycle keeps model selection flexible while application integration stays stable.
APPLICATION LAYER
1AI Applications
2Agent Workflows
3Model Evaluation
Unified requests
MANAGED MODEL PLANEAI MaaS
Unified Model APICatalog SelectionManaged ServingElastic Scaling
ACTIVE POLICY PATH99.99%
Selected runtime
MODEL POOLS
AFlagship MoE
BDense Models
CEfficient Models
MODEL OPERATIONS
EvaluationUsage AnalyticsVersion ControlAvailability
Choose
Compare model families, scale and license.
Connect
Integrate through a consistent service interface.
Deploy
Move the selected model into production delivery.
Evolve
Add or change models as product needs develop.
APPLICATION SCENARIOS
Built for the AI Workloads That Matter
Use AI MaaS wherever performance, choice and production control are essential.
Enterprise Assistants
Build grounded internal copilots and knowledge experiences.
Choose the model that fits each knowledge task
Coding & Agent Workflows
Power generation, reasoning and tool-driven automation.
Move between specialized models more easily
Multilingual Products
Support global applications with a diverse model portfolio.
Serve varied language and market needs
Model Evaluation
Benchmark candidate models before a wider rollout.
Reduce integration work during selectionWHY EDGENEXT AI
Simplify the Hard Parts of Production AI
Fast-Moving Model Landscape
A curated service layer reduces the work required to evaluate every new model release.
Serving Complexity
Managed delivery removes repeated infrastructure and operational work from product teams.
Application Lock-In
Consistent access makes it easier to evolve model choices without rewriting the product.