- Instruction Following
Qwen3.6 35B A3B is an open-weight, multimodal Mixture-of-Experts model with 35 billion parameters (about 3 billion active per token), designed for long-context reasoning, coding, and vision-language…
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Qwen3.5 Plus 2026-04-20
- Instruction Following
Qwen3.5 Plus 2026-04-20 is a large-scale, proprietary multimodal language model from Qwen (Alibaba) that offers a 1M-token context window and strong reasoning and vision capabilities for advanced agentic workflows.
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 Qwen3.5 Plus 2026-04-20?
Qwen3.5 Plus 2026-04-20 is an updated April 2026 release of Qwen’s flagship Qwen3.5 Plus multimodal language model with a 1M-token context window. It is mainly used for complex text and code generation tasks that benefit from long-context understanding, such as processing large document collections or repositories in a single session. It is also used for multimodal reasoning over text and images in applications like visual question answering, data analysis, and tool-using AI agents. It belongs to the Qwen3.5 family of models, an evolution of earlier Qwen and Qwen2 generations from Alibaba.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, follows complex instructions, and maintains conversational context across diverse general-purpose assistant tasks.
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Code Reasoning
Understands and generates source code, explains programming concepts, and helps debug or refactor code in multiple languages.
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Image Understanding
Interprets images, identifying objects, scenes, and relationships to answer questions or provide descriptions about visual content.
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Text Translation
Translates between multiple languages, preserving meaning and tone while adapting phrasing to sound natural in the target language.
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Document OCR
Extracts machine-readable text from images or scanned documents, enabling search, editing, and downstream processing of visual text content.
Use cases
6 Most Valuable Use Cases
- Long Document Analysis
- Multimodal Content Review
- Legal Case Summaries
- Regulatory Change Monitoring
- Coding Agent Workflows
- Customer Support Automation
Transparent pricing
Cost Comparison
LLM API offers the lowest token prices and best performance for Qwen3.5-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.10 | $0.30 | 256K |
| Qwen (Official) | Global | ~220ms | ~40 tps | ~99.9% | ~$0.20 | ~$0.60 | ~200K |
| Alibaba Cloud AI | APAC | ~260ms | ~35 tps | ~99.9% | ~$0.22 | ~$0.65 | ~128K |
| OpenRouter | Global | ~240ms | ~30 tps | ~99.8% | ~$0.25 | ~$0.70 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5 Plus 2026-04-20 | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~320ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.40 | $5.00 | $3.00 |
| Output Price ($/1M) | $1.20 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | 60 tps | 40 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (last 30 days)
- 2.4B
- Completion tokens generated (last 30 days)
- 12.3M
- API requests served (last 30 days)
- 98.9%
- Average uptime over 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 cost, latency, and quality—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Optimization
Control spend with per-request cost policies, dynamic model selection, and real-time price visibility so you can scale usage without surprise bills or manual tuning.
Lower cost, same output -
Resilient Fallback Logic
Define provider- and model-level fallback chains so requests transparently fail over on errors, rate limits, or outages—no custom retry code needed.
Stay online by default -
End-to-End Observability
Inspect every call with traces, costs, latencies, and model choices in one place, making it easy to debug prompts and optimize performance in production.
See every token -
Task-Native Abstractions
Use high-level task APIs for chat, tools, RAG, and structured outputs so you can swap models and providers without rewriting business logic.
Code to tasks, not models -
High-Throughput Batch
Submit large batches with automatic chunking, concurrency control, and retries to process millions of requests efficiently while respecting rate limits across providers.
Scale jobs, not scripts
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a balanced general-purpose model for chatbots, agents, and everyday productivity.
- You need solid English and Chinese capabilities for bilingual products or localization workflows.
- Your use case involves code assistance, reviews, and small-to-medium feature implementation tasks.
- Your use case involves data extraction or light analysis on moderately long business documents.
- You need a cost-effective model for iterative prototyping and internal developer tooling.
- Your use case involves multi-turn application logic where reliability matters more than raw creativity.
- You need structured JSON-style outputs and are comfortable enforcing schema validation in your stack.
Avoid if...
- You need frontier-level reasoning comparable to the very best closed-source flagship models.
- Your workload requires extremely long-context processing on hundreds of pages in a single call.
- You need highly specialized domain reasoning, such as cutting-edge legal or medical analysis.
- Your workload requires ultra-low latency responses for real-time interactive or on-device scenarios.
- You need the strongest possible performance on complex multi-step math and theoretical proofs.
- Your workload requires tight integration with a specific proprietary ecosystem this provider does not support.
- You need robust multimodal capabilities beyond text, such as advanced image or video understanding.
FAQ
Frequently Asked Questions
-
What is Qwen3.5 Plus 2026-04-20?
Qwen3.5 Plus 2026-04-20 is a general-purpose large language model by Qwen exposed through the LLM.API unified AI gateway.
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What is Qwen3.5 Plus 2026-04-20 best suited for?
Qwen3.5 Plus 2026-04-20 is best for robust text generation, coding assistance, and instruction-following tasks where strong reasoning and reliability matter.
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What is the context window of Qwen3.5 Plus 2026-04-20?
Qwen3.5 Plus 2026-04-20 supports a context window of up to 128K tokens via LLM.API, depending on your configured limits.
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Does Qwen3.5 Plus 2026-04-20 support images or other modalities?
Qwen3.5 Plus 2026-04-20 is text-only through LLM.API and does not support image, audio, or video inputs.
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How is Qwen3.5 Plus 2026-04-20 priced on LLM.API?
On LLM.API, Qwen3.5 Plus 2026-04-20 is billed per token, with separate input and output token rates defined in your LLM.API pricing plan.
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How fast is Qwen3.5 Plus 2026-04-20 in terms of latency?
Typical end-to-end latency is comparable to other mid-sized hosted LLMs, but depends on prompt length, output size, and current LLM.API load.
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How do I call Qwen3.5 Plus 2026-04-20 via LLM.API?
Use the LLM.API chat or completions endpoint and set the model parameter to "Qwen3.5 Plus 2026-04-20" in your request payload.
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How does Qwen3.5 Plus 2026-04-20 compare to similar models on LLM.API?
Qwen3.5 Plus 2026-04-20 targets a balance of quality and cost, often cheaper than flagship frontier models but stronger than lightweight baselines.
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What are the main limitations of Qwen3.5 Plus 2026-04-20?
It can hallucinate facts, lacks real-time knowledge beyond its training cutoff, and should not be solely relied on for safety-critical decisions.
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Can Qwen3.5 Plus 2026-04-20 access external tools or the internet through LLM.API?
Tool use or browsing is only available if you implement those capabilities application-side; the base model has no built-in external access.
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