FW-Qwen3.5-9B

FW-Qwen3.5-9B

Qwen3.5 9B is a 9B-parameter causal language model with a vision encoder and hybrid Gated DeltaNet/Gated Attention architecture, built for multimodal reasoning, coding, agents, and visual understanding with a native 262,144-token context window.
Fireworks
Version: 1
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About this model

Qwen3.5-9B is a post-trained causal language model with a vision encoder, using a 32-layer hybrid stack with Gated DeltaNet and Gated Attention blocks over 9B parameters. In the Qwen3.5 family, key innovations include a unified vision-language foundation with early-fusion multimodal training, scalable RL generalization, and expanded support for 201 languages and dialects. The model accepts text and image inputs and generates text, targeting reasoning, coding, agents, and visual understanding with a native 262,144-token context window extensible to 1,010,000 tokens.

Key model capabilities

  • Unified vision-language foundation with early fusion training
  • Hybrid Gated DeltaNet and Gated Attention architecture
  • Multimodal reasoning, coding, agents, and visual understanding
  • Support for 201 languages and dialects
  • 262,144-token native context extensible to 1,010,000 tokens

Quick facts

Model providerFireworks
TypeChat completion
LifecycleGenerally available (GA)
Input typetext, image
Output typetext
Context window262.144k