qwen3.5:cloud

Cloud tags

Qwen 3.5 also ships local sizes from 0.8B to 122B (including MLX variants) for on-device use — the cloud tags serve the 397B flagship on Ollama's hosted infrastructure.

Highlights

• Unified vision-language foundation — early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding.
• Efficient hybrid architecture — Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
• Scalable RL generalization — reinforcement learning scaled across million-agent environments for robust real-world adaptability.
• Global linguistic coverage — support for 201 languages and dialects with nuanced cultural and regional understanding.

Benchmark highlights

Best practices

• Multilingual workloads — 201 languages and dialects make it a strong default for global deployments.
• Vision tasks — strong OCR, document understanding, and video understanding alongside general vision.
• Coding agents — solid SWE-bench Verified and Terminal Bench results for agentic coding.