Chatbot Arena +
❖ This leaderboard is based on the following benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). We use 5M+ user votes to compute Elo ratings. AAII - Artificial Analysis Intelligence Index v3 aggregating 10 challenging evaluations. ARC-AGI - Artificial General Intelligence benchmark v2 to measure fluid intelligence.
GLM-4.6
❖ The GLM-4.x series models are foundation models designed for intelligent agents. GLM-4.6 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.x models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
Qwen3-VL
❖ Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date. This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
GPT OSS
❖ Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of these open models: gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
SWE-bench +
❖ SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub. Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem. SWE-bench Verified is a human-validated subset that more reliably evaluates AI models’ ability to solve issues. International Olympiad in Informatics (IOI) competition features standardized and automated grading.
Qwen3-Coder
❖ We’re announcing Qwen3-Coder, our most agentic code model to date. Qwen3-Coder is available in multiple sizes, but we’re excited to introduce its most powerful variant first: Qwen3-Coder-480B-A35B-Instruct — a 480B-parameter Mixture-of-Experts model with 35B active parameters, offering exceptional performance in both coding and agentic tasks. It sets new state-of-the-art results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use.
MTP in SGLang
❖ SGLang is the first and only open-source serving framework to support Multiple Token Prediction (MTP) in combination with Large-Scale Expert Parallelism (EP) and Prefill-Decode disaggregation. This integration delivers up to 60% higher output throughput through a new decoding paradigm, better parallelism, and more efficient resource utilization without sacrificing generation quality.
slime
❖ slime is an LLM post-training framework for RL scaling, providing two core capabilities: High-Performance Training – Supports efficient training in various modes by connecting Megatron with SGLang; Flexible Data Generation – Enables arbitrary training data generation workflows through custom data generation interfaces and server-based engines.