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Qwen3 VL 235B A22B Instruct
- Instruction Following
Qwen3 VL 235B A22B Instruct is a 235B-parameter Mixture-of-Experts vision-language model from Qwen, offering open-weight, long-context (≈256K) multimodal reasoning over text, images, and video. It is instruction-tuned for chat-style interactions and agentic use, including GUI automation and tool use.
About the model
What is Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct is an open-weight, instruction-tuned Mixture-of-Experts vision-language model with 235B parameters (22B active) that supports text, image, and video inputs with a context window of about 256K tokens. It is mainly used for general multimodal chat and reasoning tasks such as visual question answering, document and chart understanding, and long-context analysis across mixed media. It is also applied to agentic workflows including GUI automation, visual code generation from mockups, and tool-using assistants in enterprise or research pipelines. The model belongs to the Qwen3-VL family of vision-language models developed by Qwen/Alibaba as a flagship high-capacity variant building on earlier Qwen and Qwen-VL generations.
Model capabilities
5 Core Capabilities
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Multimodal Vision-Language
Understands and reasons over images and text jointly, enabling tasks like description, question answering, and visual-grounded instruction following.
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Text-Based Dialogue
Engages in multi-turn conversations, follows complex instructions, and performs reasoning, coding, and analysis across diverse textual domains.
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Screen and UI Understanding
Interprets screenshots, interfaces, and layouts, supporting tasks like element identification, navigation planning, and workflow explanation for applications.
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Optical Character Recognition
Interprets technical prompts, reasons step-by-step, and can be integrated with tools or environments for advanced programmatic workflows.
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Multilingual Understanding
Understands and generates multiple languages, enabling cross-lingual tasks such as explanation, paraphrasing, and language-aware reasoning over content.
Use cases
6 Most Valuable Use Cases
- Multimodal Visual Reasoning
- Image-Based Question Answering
- Code and Diagram Understanding
- Chart and Figure Interpretation
- Business Document Analysis
- Compliance Case Monitoring
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and best limits for Qwen3 VL–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~220ms | ~70 img/min | 99.99% | ~$0.40/1K tokens+image | ~$0.80/1K tokens | ~256K tokens+images |
| Qwen | Global | ~350ms | ~45 img/min | 99.9% | ~$0.90/1K tokens+image | ~$1.80/1K tokens | ~128K tokens+images |
| Alibaba Cloud | APAC East | ~420ms | ~40 img/min | 99.9% | ~$1.00/1K tokens+image | ~$2.00/1K tokens | ~128K tokens+images |
| AWS Marketplace | US East | ~380ms | ~38 img/min | 99.9% | ~$1.10/1K tokens+image | ~$2.20/1K tokens | ~128K tokens+images |
| Azure Marketplace | EU West | ~400ms | ~35 img/min | 99.9% | ~$1.20/1K tokens+image | ~$2.40/1K tokens | ~128K tokens+images |
Performance benchmarks
Technical Specifications
| Metric | Qwen3 VL 235B A22B Instruct | GPT-4.1 Vision | Claude 3.5 Sonnet Vision |
|---|---|---|---|
| Latency per Image | ~900ms | ~1.2s | ~1.0s |
| Throughput | ~40 img/s | ~35 img/s | ~30 img/s |
| Max Resolution | ~4K | ~4K | ~4K |
| Price per Image | ~$0.004 | ~$0.005 | ~$0.005 |
| Supported Formats | PNG, JPG, WebP, GIF | PNG, JPG, WebP, GIF | PNG, JPG, WebP, GIF |
| Uptime | ~99.9% | ~99.9% | ~99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 25M
- Completion tokens generated (last 30 days)
- 2.4M
- API requests served (last 30 days)
- 99.8%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Dynamically route each request to the best model by latency, cost, or quality—no client changes required as providers, versions, or constraints evolve.
One endpoint, every model -
Cost-Aware Execution
Control spend with automatic price-based routing, per-project budgets, and cost insights so you can ship fast without surprise bills or manual tuning.
Optimize every token -
Resilient Fallback Flows
Survive provider outages and rate limits with automatic cross-vendor failover, health checks, and configurable retries—all wired behind a single API.
Never drop a request -
Deep LLM Observability
Trace every call across providers with logs, latency and error metrics, and cost breakdowns so you can debug, tune, and scale with real production data.
See every token -
Task-Level Abstractions
Describe the task, not the model. LLM.API picks the right tools, prompts, and providers so you keep logic clean and avoid brittle per-model code.
Code to tasks, not models -
High-Throughput Batch Runs
Run massive offline jobs with provider-aware chunking, parallelization, and retries to safely process millions of items through a single unified interface.
Scale to millions
Decision guide
When to Use — When NOT to Use
Use it if...
- You need strong multimodal understanding that jointly reasons over complex images, text, and layouts.
- You need high-end instruction following for agents, tools, or workflow orchestration with visuals.
- You need to analyze technical diagrams, UI screenshots, or charts alongside detailed textual specs.
- Your use case involves vision-language RAG over slide decks, PDFs, and mixed-format documentation.
- Your use case involves generating detailed, stepwise explanations grounded in visual input and context.
- You need a general-purpose flagship VL model for benchmarking complex multimodal reasoning tasks.
Avoid if...
- You need ultra-low-latency responses for interactive applications on edge or mobile hardware.
- You need a tiny model for on-device inference with very limited memory and compute.
- Your workload requires processing trillions of tokens monthly under extremely tight cost constraints.
- You need strict, independently audited compliance guarantees for highly regulated medical or financial decisions.
- You need pure audio or video understanding without converting content into images or text frames.
- Your workload requires simple text-only classification where a small specialized model is sufficient.
FAQ
Frequently Asked Questions
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What is Qwen3 VL 235B A22B Instruct?
Qwen3 VL 235B A22B Instruct is a large Qwen multimodal instruction-tuned model designed for high-quality vision-language and text-only reasoning tasks.
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What is Qwen3 VL 235B A22B Instruct best suited for?
It excels at complex image understanding, detailed visual question answering, document analysis with OCR, code reasoning from screenshots, and advanced multi-step text reasoning.
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What modalities does Qwen3 VL 235B A22B Instruct support via LLM.API?
Through LLM.API, it supports text input and output plus image inputs, enabling vision-language workflows and standard chat-style text generation.
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What context window does Qwen3 VL 235B A22B Instruct support?
The model supports a large-context window suitable for long conversations and multi-page document analysis; check LLM.API docs for the exact current token limit.
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How fast is Qwen3 VL 235B A22B Instruct on LLM.API?
As a 235B-parameter model it has higher latency than smaller models, but LLM.API uses optimized serving to keep interactive use practical.
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How is Qwen3 VL 235B A22B Instruct priced on LLM.API?
Pricing is usage-based per input and output token, with the exact rates published in the LLM.API pricing section for Qwen models.
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How do I call Qwen3 VL 235B A22B Instruct through the LLM.API?
Specify the model name in your LLM.API chat or completion request, pass text and optional image inputs, and handle responses like other chat models.
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How does Qwen3 VL 235B A22B Instruct compare to smaller Qwen3 VL models?
It generally offers stronger reasoning and visual understanding quality but at higher cost and latency than smaller Qwen3 VL variants.
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What are the main limitations of Qwen3 VL 235B A22B Instruct?
It can hallucinate details, may misinterpret ambiguous images, and is not guaranteed accurate for real-time data or highly domain-specific expert advice.
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Can Qwen3 VL 235B A22B Instruct access the internet or external tools via LLM.API?
By default it has no direct internet or tool access; any such capabilities must be implemented in your application around the LLM.API calls.