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Qwen3 VL 235B A22B Thinking
- Vision-Language
Qwen3 VL 235B A22B Thinking is a large Qwen multimodal model that can process both images and text with enhanced chain-of-thought style reasoning. It is configured for higher-quality, slower “thinking” outputs rather than fast responses.
About the model
What is Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is a multimodal large language model from Qwen that supports visual and textual understanding with an emphasis on extended reasoning. It is mainly used for complex analysis of images and documents, such as detailed visual question answering, multi-step interpretation, and grounded explanations. It is also applied to advanced text-only reasoning tasks where deliberate, step-by-step thinking is valuable, for example in technical problem-solving or multi-hop research-style queries. It belongs to the Qwen3 family of large-scale language and vision-language models that follow earlier Qwen and Qwen-VL generations.
Model capabilities
5 Core Capabilities
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Visual Reasoning
Understands and reasons about images and diagrams, identifying objects, spatial relations, and visual patterns for complex tasks.
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Text Extraction
Reads and extracts structured and unstructured text from images or documents, enabling downstream analysis and transformation of content.
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Conversational Assistance
Engages in multi-turn dialogue, follows complex instructions, and maintains context to provide helpful, coherent, and detailed responses.
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Code and Tools
Interprets technical instructions, reasons step-by-step, and can coordinate with tools or systems for complex problem solving.
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Multilingual Understanding
Understands and translates between multiple languages, preserving meaning and context across diverse linguistic inputs and outputs.
Use cases
6 Most Valuable Use Cases
- Long-Context Code Audits
- Document & Chart OCR
- Legal Evidence Review
- Compliance Case Monitoring
- E-commerce Product Analysis
- UI Automation Agent
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and fastest, most scalable access to Qwen3 VL-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~220ms | ~120 tps | 99.99% | ~$0.60 per 1M tokens | ~$1.80 per 1M tokens | ~256K tokens |
| Qwen | Global | ~280ms | ~75 tps | 99.9% | ~$0.80 per 1M tokens | ~$2.40 per 1M tokens | ~200K tokens |
| Alibaba Cloud | APAC | ~320ms | ~60 tps | 99.9% | ~$0.90 per 1M tokens | ~$2.70 per 1M tokens | ~128K tokens |
| Together AI | US East | ~260ms | ~80 tps | 99.9% | ~$0.95 per 1M tokens | ~$2.80 per 1M tokens | ~128K tokens |
| Fireworks AI | US West | ~250ms | ~85 tps | 99.9% | ~$1.00 per 1M tokens | ~$3.00 per 1M tokens | ~128K tokens |
Performance benchmarks
Technical Specifications
| Metric | Qwen3 VL 235B A22B Thinking | GPT-4.1 Omni Vision | Claude 3.5 Sonnet Vision |
|---|---|---|---|
| Latency per Image | ~900ms | ~850ms | ~950ms |
| Throughput | ~40 img/s | ~45 img/s | ~35 img/s |
| Max Resolution | ~4K | ~4K | ~4K |
| Price per Image | ~$0.005 | ~$0.01 | ~$0.008 |
| Supported Formats | PNG, JPG, WEBP, GIF | PNG, JPG, WEBP, GIF | PNG, JPG, WEBP, GIF |
| Uptime | 99.9% | 99.9% | 99.9% |
| Max Output Tokens | 8K | 8K | 8K |
30-day usage via LLM API
- 62.5B
- Prompt tokens processed (last 30 days)
- 41.3B
- Completion tokens generated (last 30 days)
- 5.8M
- 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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Unified AI Routing
Define intent once and let LLM.API route to the optimal model across providers based on latency, quality, and constraints—no client changes required.
One endpoint, every model -
Cost-Aware Execution
Enforce per-project budgets, pick cheaper equivalents automatically, and track exact token spend so you can scale usage without surprise invoices.
Optimize spend by default -
Automatic Fallback Logic
Configure multi-provider failover and retry policies so requests keep succeeding even when individual models, regions, or vendors degrade or go offline.
Resilience built-in -
End-to-End Observability
Get structured logs, traces, and metrics for every request—latency, cost, provider, and model—making it easy to debug, tune prompts, and meet SLAs.
See every token -
Task-Oriented Abstractions
Call high-level tasks like chat, tools, embeddings, or rerank via one consistent API while LLM.API selects and orchestrates the best underlying models.
Tasks, not raw models -
High-Throughput Batch APIs
Submit large batches of prompts, tools, or embeddings in a single call to maximize throughput, cut network overhead, and slash per-request compute costs.
Scale to millions
Decision guide
When to Use — When NOT to Use
Use it if...
- You need very strong multi-step reasoning where slower but higher-quality chains are acceptable.
- You need advanced multimodal understanding that jointly reasons over complex images, text, and layouts.
- Your use case involves difficult coding or algorithmic tasks that benefit from deliberate thinking.
- You need to analyze lengthy technical documents and derive structured insights or action plans.
- Your use case involves complex tool-calling or orchestration where accurate reasoning is critical.
- You need high-end assistant behavior for research, tutoring, or planning with rich explanations.
- Your use case involves multimodal data extraction from diagrams, charts, or dense scientific figures.
Avoid if...
- You need ultra-low-latency responses where even moderate deliberate reasoning would be too slow.
- Your workload requires serving millions of lightweight requests under tight cost constraints daily.
- You need on-device or edge deployment where model size and memory are strictly limited.
- Your workload requires strict real-time interaction, like high-frequency trading or fast-twitch gaming.
- You need simple classification or routing tasks better handled by smaller, cheaper models.
- Your workload requires guaranteed deterministic outputs with minimal sampling variance across runs.
- You need basic image tagging or OCR only, without heavy reasoning or contextual understanding.
FAQ
Frequently Asked Questions
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What is Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is a large multimodal Qwen model focused on deliberate, step-by-step reasoning over text and images.
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What is Qwen3 VL 235B A22B Thinking best suited for?
It is best for complex reasoning tasks, multi-step problem solving, code understanding, and detailed image-plus-text analysis where accuracy matters more than raw speed.
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What modalities does Qwen3 VL 235B A22B Thinking support via LLM.API?
Through LLM.API it supports text input and output, plus image inputs for vision-language reasoning and description.
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How is Qwen3 VL 235B A22B Thinking priced on LLM.API?
Pricing is usage-based per input and output token on LLM.API; check the Qwen3 VL 235B A22B Thinking entry in the pricing dashboard.
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What is the context window of Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking supports a long context window suitable for multi-document analysis; refer to the LLM.API model card for the exact limit.
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How fast is Qwen3 VL 235B A22B Thinking compared to smaller models?
As a 235B-scale model it has higher latency and lower throughput than smaller models, trading speed for stronger reasoning quality.
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How do I call Qwen3 VL 235B A22B Thinking through LLM.API?
Use the standard LLM.API chat or completion endpoint with the model identifier for Qwen3 VL 235B A22B Thinking and your API key.
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How does Qwen3 VL 235B A22B Thinking compare to similar reasoning models?
It prioritizes deliberate reasoning quality over speed, making it competitive for complex tasks but less suitable for ultra-low-latency applications.
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What are the main limitations of Qwen3 VL 235B A22B Thinking?
It can be slower and more expensive than smaller models and may still hallucinate details, so critical outputs should be validated.
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Can Qwen3 VL 235B A22B Thinking handle streaming responses on LLM.API?
Yes, you can enable streaming in your LLM.API request to receive tokens incrementally from Qwen3 VL 235B A22B Thinking.