Qwen

QwenLM has released Qwen Code, an open-source AI coding agent whose entry points span the terminal, IDE plugins, a desktop app, a Web UI, SDKs and chat integrations including Telegram, DingTalk, WeChat, WeCom and Feishu. It ships with Auto-Memory, Skills, SubAgents and MCP, plus Agent Teams, Agent Arena and Dynamic Workflows, some marked experimental or opt-in. A daemon mode lets browser, editor, SDK and automation clients share one agent session, and clients can reconnect to active sessions.

QwenLM has released Qwen Code, an open-source AI coding agent whose entry points span the terminal, IDE plugins, a desktop app, a Web UI, SDKs and chat integrations including Telegram, DingTalk, WeChat, WeCom and Feishu. It ships with Auto-Memory, Skills, SubAgents and MCP, plus Agent Teams, Agent Arena and Dynamic Workflows, some marked experimental or opt-in. A daemon mode lets browser, editor, SDK and automation clients share one agent session, and clients can reconnect to active sessions.

SGLang adds NVFP4 storage to its paged KV cache, cutting per-token key-value bytes to about 56% of FP8. On Qwen3.8-27B at 32K, 160K and 1M input lengths with matched decode concurrency, peak-batch decode throughput rises about 26–30%; on agentic load beyond concurrency 12, FP8 throughput collapses while NVFP4 keeps climbing. Accuracy gaps vary by task, reaching 1.60 points on SWE-bench Verified.

SGLang adds NVFP4 storage to its paged KV cache, cutting per-token key-value bytes to about 56% of FP8. On Qwen3.8-27B at 32K, 160K and 1M input lengths with matched decode concurrency, peak-batch decode throughput rises about 26–30%; on agentic load beyond concurrency 12, FP8 throughput collapses while NVFP4 keeps climbing. Accuracy gaps vary by task, reaching 1.60 points on SWE-bench Verified.

Open models previously forced a choice between showing a chain of thought and calling tools deterministically. Qwen3-235B-A22B places its reasoning inside a separate think tag and emits the exact JSON tool call in the same completion, so downstream code can ignore or audit the trace. The model is a 235-billion-parameter mixture-of-experts with about 22 billion active parameters, hosted serverlessly on Fireworks under Apache-2.0 and callable through an OpenAI-compatible client.

Open models previously forced a choice between showing a chain of thought and calling tools deterministically. Qwen3-235B-A22B places its reasoning inside a separate think tag and emits the exact JSON tool call in the same completion, so downstream code can ignore or audit the trace. The model is a 235-billion-parameter mixture-of-experts with about 22 billion active parameters, hosted serverlessly on Fireworks under Apache-2.0 and callable through an OpenAI-compatible client.

The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.

The work builds Qwen-Planner-Agent within a closed-loop AI-for-AI framework that links data production, model training, and deployment through a shared action-feedback-verification contract: AI for Data builds a human-gated agentic data flywheel, AI for Training combines a supervised planning cold start with hybrid-environment online agentic reinforcement learning and introduces CARE to reduce reasoning and tool-use costs, and AI drives model-harness co-evolution; the agent achieves the best overall performance among all evaluated models and systems on MobilePA-Bench, improving over its base model across tool use, memory, skills, and sub-agent coordination, with further gains on non-mobile agentic benchmarks while largely preserving general capabilities.