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Qwen3.5-122B-A10B
- Text Generation
Qwen3.5-122B-A10B is a 122B-parameter open-weight Mixture-of-Experts vision-language model from Qwen that activates 10B parameters per token and supports a 262K-token context window. It is designed to balance high intelligence with efficient inference for complex, long-context tasks.
About the model
What is Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B is a large Mixture-of-Experts multimodal language model from Qwen with 122B total parameters (10B active) and a native context window of around 262K tokens. It is mainly used for advanced reasoning, coding, and agentic workflows that require long-context understanding and high-quality tool use. It is also applied to multimodal vision-language tasks and multilingual chat, benefiting scenarios like document synthesis and complex analysis where long inputs and outputs are needed. It belongs to the Qwen3.5 model family, extending the Qwen and Qwen3 series of open-weight models.
Model capabilities
5 Core Capabilities
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Conversational AI
Engages in multi-turn, context-aware dialogue, following instructions, asking clarifying questions, and maintaining coherent conversations across complex topics.
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Textual Reasoning
Performs logic, analysis, and problem-solving over long texts, handling summarization, explanation, and structured outputs for varied domains.
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Multilingual Translation
Translates between major languages, preserving meaning and tone while handling everyday content and moderately technical text.
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Visual Understanding
Interprets images to identify objects, layouts, and relationships, enabling descriptions, comparisons, and simple visual reasoning tasks.
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Document OCR
Extracts machine-readable text from images of documents, such as scans or photos, supporting downstream search and analysis.
Use cases
6 Most Valuable Use Cases
- Large-Scale Code Generation
- Enterprise Document Analysis
- Customer Support Automation
- Legal Case Research Assistance
- Regulatory Change Monitoring
- Domain-Specific Text Tagging
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Qwen3.5-122B-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.30 | $0.60 | 128K |
| Qwen | Asia Pacific | ~220ms | ~35 tps | 99.9% | ~$0.80 | ~$1.60 | 64K |
| Alibaba Cloud | AP Southeast | ~260ms | ~30 tps | 99.9% | ~$0.90 | ~$1.80 | 64K |
| Fireworks AI | US East | ~200ms | ~40 tps | 99.9% | ~$0.70 | ~$1.40 | 128K |
| Together AI | US West | ~210ms | ~38 tps | 99.9% | ~$0.75 | ~$1.50 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5-122B-A10B | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~220ms | ~350ms | ~320ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.70 | $5.00 | $3.00 |
| Output Price ($/1M) | $2.10 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 60 tps | 30 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 38.5B
- Prompt tokens processed (30 days)
- 41.2B
- Completion tokens generated (30 days)
- 5.1M
- API requests served (30 days)
- 145K
- Unique developer accounts (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 latency, price, and capability—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Orchestration
Automatically balance quality and spend with per-call cost controls, model mix strategies, and real-time price visibility so you never overshoot your budget again.
Control spend by design -
Resilient Fallback Flows
Define multi-provider fallback chains that retry, downgrade, or switch models on errors or timeouts, keeping your AI features online when vendors fail.
No single point of failure -
Deep Observability
Trace every request across providers with logs, metrics, and structured events so you can debug failures, tune prompts, and optimize routes from real traffic.
See every token hop -
Task-Level Abstractions
Call high-level tasks—chat, tools, rerank, embed, image—behind one stable API while LLM.API picks and configures the best model for each job.
Think tasks, not models -
High-Throughput Batching
Send large batches of prompts, embeddings, or rerank jobs in a single call to maximize throughput, minimize overhead, and unlock bulk AI workloads efficiently.
Scale AI by the batch
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a powerful general-purpose LLM for coding help, writing, and analysis.
- You need strong multilingual understanding and generation across many non-English languages and dialects.
- Your use case involves building chatbots or agents that handle complex multi-turn conversations.
- Your use case involves code generation, explanation, and debugging across multiple popular programming languages.
- You need a capable reasoning model for data extraction, classification, and structured output generation.
- You need an open-weight model option deployable in your own infrastructure or cloud.
Avoid if...
- You need the absolute strongest reasoning and math performance available from frontier proprietary models.
- Your workload requires ultra-low latency, lightweight inference on edge or mobile-class hardware.
- You need a model with deeply integrated, fully managed tooling and ecosystem from major hyperscalers.
- Your workload requires strict enterprise certifications and compliance only top commercial vendors guarantee.
- You need highly specialized domain models, like medical or legal experts with certified datasets.
- Your workload requires extremely long-context processing beyond what current Qwen3.5 variants typically support.
FAQ
Frequently Asked Questions
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What is Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B is a large Qwen language model accessible via LLM.API, designed for high-quality reasoning, coding, and complex instruction-following tasks.
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What is Qwen3.5-122B-A10B best suited for?
It excels at multi-step reasoning, code generation and debugging, data analysis, and producing detailed technical or analytical responses from long prompts.
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What is the context window of Qwen3.5-122B-A10B on LLM.API?
Qwen3.5-122B-A10B supports up to a 32K token context window when accessed through LLM.API.
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How fast is Qwen3.5-122B-A10B in terms of latency and throughput?
As a 122B-parameter model it has higher latency than smaller Qwen models, but LLM.API parallelization keeps streaming responses reasonably fast for production workloads.
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What modalities does Qwen3.5-122B-A10B support via LLM.API?
On LLM.API, Qwen3.5-122B-A10B is used as a text-only model for prompts and completions.
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How is Qwen3.5-122B-A10B priced on LLM.API?
Pricing is usage-based per 1,000 tokens, with separate rates for prompt and output tokens, visible in the Qwen3.5-122B-A10B entry on LLM.API.
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How do I call Qwen3.5-122B-A10B through the LLM.API gateway?
Specify the model name "Qwen3.5-122B-A10B" in your LLM.API completion or chat endpoint request, along with your API key and payload.
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How does Qwen3.5-122B-A10B compare to smaller Qwen models?
Compared to smaller Qwen variants, Qwen3.5-122B-A10B offers stronger reasoning and coding quality at the cost of higher latency and token costs.
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What limitations does Qwen3.5-122B-A10B have?
It can hallucinate incorrect facts, lacks real-time knowledge, may struggle with strict numerical precision, and should not be solely relied on for safety-critical decisions.
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Can I fine-tune Qwen3.5-122B-A10B through LLM.API?
Fine-tuning availability depends on LLM.API’s current feature set; check the dashboard or documentation for whether Qwen3.5-122B-A10B supports custom training.