SiliconFlow
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Compare Qwen3-VL-8B-Instruct and gpt-oss-20b across performance, cost, capabilities, and real-world use cases. See which model fits your needs.
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Model details
Qwen/Qwen3-VL-8B-Instruct
Qwen3-VL-8B-Instruct represents the latest generation of the Qwen vision-language series, designed as a unified model that processes both static and dynamic visual media alongside text. Its architecture incorporates Interleaved-MRoPE for tracking temporal relationships across long video sequences, and DeepStack for fine-grained alignment between visual elements and textual descriptions. These design choices enable the model to handle document parsing, visual question answering, spatial reasoning, and GUI control with a single coherent framework. The model also features text-timestamp alignment for precise event localization, allowing it to index and retrieve information at second-level granularity within hours of video content.
The model achieves text understanding on par with leading language models while expanding OCR coverage to 32 languages, improving robustness under challenging conditions such as low light, blur, and tilt. Its visual agent capabilities allow it to recognize interface elements, understand functions, and complete tasks across PC and mobile environments. Developers can customize the model with their own data using LoRA-based fine-tuning, making it adaptable for specialized applications ranging from UI automation to code generation from visual inputs. The combination of a native 256K-token context window—extendable to 1M tokens—with strong multimodal reasoning makes this model well-suited for workflows that require processing lengthy documents, extended video, or complex visual-text reasoning tasks.
SiliconFlowQwen/Qwen3-VL-8B-Instructqwen
Quick Info
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- SiliconFlow
- Model key
- Qwen/Qwen3-VL-8B-Instruct
- Release date
- Oct 15, 2025
- Last updated
- Nov 25, 2025
- Input modalities
- Output modalities
- Capabilities
Cost
- Input token cost
- $0.18
- Output token cost
- $0.68
Limits
- Output tokens
- 262,000 tokens
- Context window
- 262,000 tokens
Transparent token rates
Compare Qwen/Qwen3-VL-8B-Instruct pricing
Rates are shown per one million tokens. Combined means one million input plus one million output tokens.
Latest news about Qwen/Qwen3-VL-8B-Instruct
SiliconFlow
Coverage
The official Qwen3-VL-8B-Instruct weights repository on Hugging Face, maintained by the Qwen team, documents the model as the most powerful vision-language model in the Qwen series to date. It ships in both Dense and MoE architectures with Instruct and reasoning-enhanced Thinking editions, scaling from edge to cloud de Key architectural enhancements listed on the model card include Interleaved-MRoPE for full-frequency positional encoding across time, width, and height to improve long-horizon video reasoning; DeepStack for fusing multi-level ViT features to sharpen fine-grained image-text alignment; and text-timestamp alignment that m
SiliconFlow (China)
Coverage
The ModelScope repository card for Qwen/Qwen3-VL-8B-Instruct confirms the model's creator-side provenance, listing it as an Image-Text-to-Text model under the Qwen3 VL family with 8.77B parameters distributed in Safetensors/Transformers/PyTorch formats and released under the Apache-2.0 license. The artifact size is rep As the creator-side artifact record, the ModelScope page serves as authoritative provenance for Qwen3-VL-8B-Instruct, anchoring it to the Qwen team's qwen3 vl model lineage rather than to any particular inference host. While it does not cover SiliconFlow-specific serving details, it establishes the model's identity, li
SiliconFlow
Official sourceBenchmark
Compare Qwen3-Omni-30B-A3B-Instruct and Qwen3-VL-8B-Instruct across performance, cost, capabilities, and real-world use cases. See which model fits your needs.