Modelos

Explora 7 modelos LLM canónicos de todos los proveedores

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Mostrando 1–7 de 7 modelos

GLM-5.2

China

Z.ai's (formerly Zhipu AI) flagship open-weight coding model with a 1M-token context window. Mixture-of-Experts architecture with 753B total parameters and ~40B active per request, featuring two cost-balancing reasoning modes. Tops several coding benchmarks while remaining a fraction of the cost of comparable proprietary frontier models. MIT-licensed weights.

Contexto
1.0M
Publicado
jun 2026

Ring-2.6-1T

China

InclusionAI's (Ant Group) trillion-parameter open-weights reasoning model with 63B active parameters per token. Built for real-world agent workflows with adaptive reasoning-effort modes. Features hybrid linear and MLA attention architecture with MIT license.

Contexto
131K
Publicado
may 2026

DeepSeek V4 Pro

China

DeepSeek's flagship V4 model with 1.6T total parameters (49B activated). MoE architecture supporting 1M token context. Closes the gap with frontier proprietary models on reasoning and coding benchmarks.

Contexto
1.0M
Publicado
abr 2026

DeepSeek V4 Flash

China

DeepSeek's efficient V4 model with 284B total parameters (13B activated). Optimized for speed and cost-efficiency while maintaining strong performance. Supports 1M token context window.

Contexto
1.0M
Publicado
abr 2026

MiMo-V2.5-Pro

China

Xiaomi's flagship 1.02T-parameter Mixture-of-Experts model with 42B active parameters, built on a hybrid-attention architecture with 3-layer Multi-Token Prediction. Designed for complex agentic tasks, software engineering, and long-horizon instruction following with a 1M-token context window.

Contexto
1.0M
Publicado
abr 2026

DeepSeek V4

China

DeepSeek's fourth-generation model with improved mixture-of-experts architecture, enhanced reasoning and coding capabilities, and stronger multilingual performance. Competitive with frontier proprietary models.

Contexto
256K
Publicado
feb 2026

DeepSeek R1

China

Modelo de DeepSeek centrado en el razonamiento y entrenado mediante aprendizaje por refuerzo para tareas complejas de varios pasos. Destaca en problemas de matemáticas, ciencia y programación que requieren razonamiento en cadena.

Contexto
131K
Publicado
ene 2025