- Text Generation
Lyria 3 Clip Preview is Google's preview music-generation model optimized for creating short, 30‑second musical clips, loops, and previews from text or image prompts.
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Qwen3 VL 30B A3B Instruct
- Text Generation
Qwen3 VL 30B A3B Instruct is a 30B-parameter Mixture-of-Experts vision-language model from Qwen, offering strong multimodal understanding and generation with a 262K-token context window. It is instruction-tuned for chat-style use and balances high-quality reasoning with relatively efficient active parameter usage.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Qwen3 VL 30B A3B Instruct?
Qwen3 VL 30B A3B Instruct is an instruction-tuned Mixture-of-Experts vision-language model with 30B total parameters (about 3B active) and a context window of roughly 262K tokens, designed by Qwen/Alibaba for multimodal input (text and images) and text output. It is mainly used for multimodal assistants that perform detailed image understanding, visual question answering, and document/image OCR-style analysis, as well as long-context reasoning over large text and mixed media. It also powers coding help, general-purpose chat, and agent-style workflows that need function calling and robust instruction following across visual and textual tasks. It belongs to the Qwen3-VL family of models, a successor line within the broader Qwen/Qwen3 ecosystem of large language and vision-language models.
Model capabilities
5 Core Capabilities
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Vision-Language Reasoning
Understands images alongside text, enabling multimodal reasoning, description, and grounded question answering about visual content and layouts.
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OCR and Extraction
Reads text from natural images, screenshots, and documents, extracting structured information from complex layouts like forms, tables, and charts.
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Conversational Assistance
Engages in multi-turn dialogue, follows instructions, and produces detailed, context-aware responses across general knowledge and specialized domains.
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Code and Tool Use
Supports code reasoning and structured outputs suitable for integration into applications, agents, and monitoring or automation workflows.
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Multilingual Understanding
Understands and generates multiple languages, enabling cross-lingual query handling, explanations, and content transformation between languages.
Use cases
6 Most Valuable Use Cases
- Multimodal Customer Support
- Visual Invoice Understanding
- Document-Based QA Search
- Regulation Change Monitoring
- Retail Product Image QA
- Vision-Language Reasoning
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and best performance for Qwen3 VL 30B–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.20 | $0.40 | 128K |
| Qwen | APAC | ~220ms | ~45 tps | 99.9% | ~$0.35 | ~$0.70 | 64K |
| Alibaba Cloud | APAC | ~260ms | ~40 tps | 99.9% | ~$0.38 | ~$0.75 | 64K |
| Fireworks AI | US East | ~190ms | ~55 tps | 99.9% | ~$0.30 | ~$0.60 | 128K |
| Together AI | US West | ~210ms | ~50 tps | 99.9% | ~$0.32 | ~$0.64 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3 VL 30B A3B Instruct | GPT-4.1 Mini (Vision) | Claude 3.5 Sonnet (Vision) |
|---|---|---|---|
| Latency per Image | ~700ms | ~650ms | ~800ms |
| Context Window | ~40 img/s | ~45 img/s | ~35 img/s |
| Max Resolution | 4K | 4K | 4K |
| Price per Image | ~$0.002 | ~$0.0025 | ~$0.003 |
| Supported Formats | PNG, JPG, WEBP | PNG, JPG, WEBP | PNG, JPG, WEBP |
| Context Window (Tokens) | 128K | 128K | 200K |
| Max Output Tokens | 8K | 8K | 8K |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.8B
- Prompt tokens processed (30 days)
- 8.4B
- Completion tokens generated (30 days)
- 5.6M
- API requests served (30 days)
- 99.95%
- Avg uptime over 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
Automatically route each request to the optimal model across providers based on latency, cost, or quality—without changing your application code or client libraries.
One endpoint, any model -
Cost-Aware Controls
Define per-project or per-endpoint budgets and pricing policies so LLM.API selects models that hit your quality targets while keeping spend predictable and optimized.
Optimize spend by design -
Resilient Fallback Logic
Encode automatic failover rules so if a provider degrades or times out, traffic transparently fails over to backup models without impacting end-user experience.
No single provider risk -
Full-Stack Observability
Track latency, error rates, token usage, and per-model performance with structured logs and traces wired into your existing monitoring stack and alerting workflows.
See every token, trace -
Task-Native Abstractions
Use high-level task APIs for chat, embeddings, tools, and agents so your logic stays stable while models and providers change behind the scenes.
Program tasks, not models -
High-Throughput Batch
Submit massive batch jobs with built-in concurrency control, retries, and aggregation to drastically cut costs and wall-clock time for large-scale workloads.
Scale jobs, not code
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose multimodal model for both text and images.
- You need to interpret screenshots, charts, or UI mockups alongside natural language instructions.
- You need multilingual vision-language understanding for global users across many written languages.
- Your use case involves building chat-style assistants that reference uploaded pictures or diagrams.
- Your use case involves educational tools that explain images, figures, or handwritten notes.
- You need to prototype vision-enabled agents without relying on the largest frontier models.
- Your use case involves product search or tagging using both images and textual attributes.
Avoid if...
- You need state-of-the-art frontier reasoning comparable to the newest closed-source flagship models.
- You need ultra-low-latency responses for high-frequency trading, ads bidding, or real-time gaming.
- Your workload requires strict enterprise certifications, audits, or compliance guarantees from the provider.
- You need highly optimized small-footprint models for on-device or edge deployment with limited memory.
- Your workload requires very long context processing far beyond typical context window limits.
- You need guaranteed compatibility with proprietary toolchains or SDKs from other major providers.
- Your workload requires domain-specific finetuning already available in specialized open-source vision models.
FAQ
Frequently Asked Questions
-
What is Qwen3 VL 30B A3B Instruct?
Qwen3 VL 30B A3B Instruct is a 30B-parameter Qwen multimodal instruction-tuned model optimized for vision-language understanding and reasoning.
-
What modalities does Qwen3 VL 30B A3B Instruct support?
Qwen3 VL 30B A3B Instruct supports text input and output plus image input for vision-language tasks.
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How do I access Qwen3 VL 30B A3B Instruct via LLM.API?
You call the standard LLM.API chat or completion endpoint and set the model parameter to "qwen3-vl-30b-a3b-instruct".
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What is Qwen3 VL 30B A3B Instruct best suited for?
It is best for complex document and image understanding, code and data reasoning, and general-purpose chat where strong vision-language reasoning is required.
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What is the context window of Qwen3 VL 30B A3B Instruct?
Qwen3 VL 30B A3B Instruct supports up to a 32K token context window for combined prompt and response.
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How does Qwen3 VL 30B A3B Instruct compare to smaller Qwen3 VL models?
Compared with smaller Qwen3 VL models, it generally offers stronger multimodal reasoning and accuracy at higher compute cost and latency.
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What are the typical latency characteristics of Qwen3 VL 30B A3B Instruct on LLM.API?
As a 30B model, it usually has higher initial latency and lower tokens-per-second throughput than mid-sized models on LLM.API.
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How is pricing for Qwen3 VL 30B A3B Instruct handled on LLM.API?
Usage is billed by input and output tokens at the Qwen3 VL 30B A3B Instruct rate shown in your LLM.API pricing dashboard.
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Does Qwen3 VL 30B A3B Instruct support system prompts and multi-turn conversations?
Yes, it supports system messages and multi-turn conversational context within the 32K token limit.
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What are the main limitations of Qwen3 VL 30B A3B Instruct?
It can hallucinate facts, misinterpret ambiguous images, and should not be relied on for safety-critical or legally binding decisions without human review.
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