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Qwen3.5-9B
- Text Generation
Qwen3.5-9B is a 9‑billion‑parameter multimodal language model from Qwen that supports long-context reasoning over text and images. It is designed to offer strong reasoning, coding, and visual understanding capabilities in a relatively compact, efficient architecture.
About the model
What is Qwen3.5-9B?
Qwen3.5-9B is a 9B-parameter multimodal foundation model from Qwen that accepts both text and visual inputs. It is mainly used for general-purpose chat and reasoning tasks where developers want a capable but lightweight model that can run with lower latency and cost than larger LLMs. It is also applied to coding assistance, document understanding, and vision-language applications such as describing or analyzing images. Qwen3.5-9B belongs to the Qwen3.5 model family, an evolution of earlier Qwen and Qwen3-generation models that improve multimodal performance and efficiency.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, follows instructions, and maintains context to answer questions and assist with varied tasks.
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Code Assistance
Generates and explains code snippets, debugs simple issues, and helps reason about programming concepts across common languages.
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Text Translation
Translates text between multiple languages while aiming to preserve meaning, tone, and key domain-specific terminology.
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Image Understanding
Interprets input images, identifying objects and basic visual context to support downstream reasoning or description tasks.
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Visual Text Extraction
Extracts readable text from images or screenshots, enabling downstream search, analysis, or transformation of visual documents.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbot
- Invoice Data Extraction
- Legal Document Search
- Regulation Change Monitoring
- E-commerce Product Assistant
- Code Generation Helper
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Qwen3.5-9B–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~120 tps | 99.99% | $0.08 | $0.08 | 128K |
| Qwen | Asia Pacific | ~220ms | ~35 tps | ~99.5% | ~$0.25 | ~$0.25 | ~64K |
| Alibaba Cloud (DashScope) | Asia Pacific | ~210ms | ~40 tps | 99.9% | ~$0.30 | ~$0.30 | ~64K |
| Fireworks AI | US East | ~180ms | ~50 tps | 99.9% | ~$0.35 | ~$0.35 | ~128K |
| Together AI | US West | ~190ms | ~45 tps | 99.9% | ~$0.40 | ~$0.40 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5-9B | Llama 3.1 8B Instruct | Mistral-Nemo 12B Instruct |
|---|---|---|---|
| Avg Latency | ~220ms | ~230ms | ~240ms |
| Context Window | 32K | 128K | 128K |
| Input Price ($/1M tokens) | $0.20 | $0.30 | $0.35 |
| Output Price ($/1M tokens) | $0.60 | $0.60 | $0.70 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 45 tps | 40 tps | 42 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 7.8M
- API requests served (last 30 days)
- 9.6B
- Completion tokens generated (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
Dynamically route each request to the optimal model across providers based on cost, speed, or quality—without changing your code or client integration.
One endpoint, any model -
Cost-Aware Orchestration
Control spend with per-request cost caps, smart model downgrades, and transparent pricing telemetry so you can optimize budgets without sacrificing performance.
Ship fast, spend less -
Automatic Smart Fallbacks
Avoid downtime and flaky providers with configurable failover policies that instantly retry on alternative models or regions when errors, timeouts, or rate limits occur.
Resilience by default -
Full-Stack Observability
Trace every token across models, providers, and teams with centralized logs, metrics, and structured events wired for debugging, analytics, and cost governance.
See every request -
Task-Level Abstractions
Define tasks like chat, tools, RAG, or workflows once and let LLM.API handle prompts, parameters, and providers so product teams can iterate safely and faster.
Model-agnostic tasks -
High-Throughput Batch APIs
Process millions of inferences with parallelized batching, automatic throttling, and retry semantics to maximize throughput while staying within provider quotas and budgets.
Scale without throttling
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a small, general-purpose model for everyday chat and assistance tasks.
- You need cost-efficient inference for high-volume requests with moderate reasoning complexity.
- Your use case involves basic code generation, debugging, or small utility scripts.
- Your use case involves lightweight content creation like short emails, summaries, or descriptions.
- You need a compact model suitable for latency-sensitive applications on modest hardware.
- Your use case involves multilingual understanding without requiring top-tier translation quality.
- You need a model for prototyping AI features before scaling to larger systems.
Avoid if...
- You need state-of-the-art performance on complex reasoning, planning, or mathematical proofs.
- Your workload requires handling extremely long context windows with robust recall and reasoning.
- You need best-in-class coding assistance for large projects, refactors, or multi-file reasoning.
- Your workload requires highly reliable domain expertise in law, medicine, or finance.
- You need the strongest safety, alignment, and nuanced instruction-following available across models.
- Your workload requires rich multimodal capabilities like advanced image understanding or generation.
- You need cutting-edge performance in benchmark-driven research or competitive leaderboard scenarios.
FAQ
Frequently Asked Questions
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What is Qwen3.5-9B?
Qwen3.5-9B is a 9B-parameter Qwen language model optimized for fast, general-purpose text generation and reasoning through the LLM.API gateway.
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What is the context window of Qwen3.5-9B?
Qwen3.5-9B supports up to a 32K token context window for combined input and output via LLM.API.
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What is Qwen3.5-9B best suited for?
Qwen3.5-9B is best for lightweight assistants, code helpers, and analytical tasks where you need strong quality without the cost of very large models.
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How is Qwen3.5-9B priced on LLM.API?
Qwen3.5-9B usage is metered per-token for input and output; check your LLM.API pricing page for the exact current rates.
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How fast is Qwen3.5-9B in terms of latency?
Qwen3.5-9B generally returns first tokens quickly and is suitable for interactive applications, but actual latency depends on load and request size.
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What modalities does Qwen3.5-9B support on LLM.API?
On LLM.API, Qwen3.5-9B is available as a text-only model, accepting and producing UTF-8 text tokens.
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How do I call Qwen3.5-9B through LLM.API?
Specify the model name "Qwen3.5-9B" in your LLM.API chat or completion request, passing messages and parameters according to the unified API schema.
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How does Qwen3.5-9B compare to larger Qwen models?
Compared to larger Qwen models, Qwen3.5-9B is cheaper and faster but may underperform on very complex reasoning or long-context tasks.
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What are key limitations of Qwen3.5-9B?
Qwen3.5-9B can hallucinate facts, struggle with highly specialized domains, and may miss subtle long-range dependencies near its context length limit.
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Can I fine-tune or customize Qwen3.5-9B via LLM.API?
Direct fine-tuning is not exposed; instead, use system prompts, exemplars, and tools to steer Qwen3.5-9B’s behavior through LLM.API.