- Instruction Following
Ring-2.6-1T is a trillion-parameter-scale open-weight "thinking" language model from inclusionAI, designed for real-world agent and coding workflows that need strong reasoning with efficient execution.
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Qwen3.5 397B A17B
- Instruction Following
Qwen3.5 397B A17B is a large-scale language model from Qwen with roughly 397 billion parameters, designed for advanced reasoning and multilingual understanding. It targets high-end inference scenarios where strong general capabilities and model depth are required.
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 397B A17B?
Qwen3.5 397B A17B is a 397-billion-parameter Qwen language model optimized for powerful, general-purpose AI assistance. It is used for complex text generation and understanding tasks such as drafting, analysis, and conversation. It is also applied in demanding reasoning, coding, and knowledge-intensive applications where very large models are preferred. It belongs to the Qwen (Qwen2/Qwen2.5/Qwen3.x) family of large language models developed by Qwen.
Model capabilities
5 Core Capabilities
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Advanced Chat
Engages in multi-turn conversations, follows complex instructions, and maintains context for reasoning, coding help, and detailed explanations.
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Image Understanding
Interprets uploaded images to identify objects, text, layouts, and visual relationships, supporting description, reasoning, and grounded question answering.
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Document OCR
Extracts and structures text from scanned documents, screenshots, and complex layouts, enabling downstream analysis, search, and transformation tasks.
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Code and Tools
Supports tool-using workflows, including calling external APIs, running code-like reasoning, and monitoring iterative steps for complex tasks.
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Multilingual Translation
Translates between many languages while preserving meaning, tone, and formatting, useful for cross-lingual communication and content localization.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Financial Document Analysis
- Legal Contract Review
- Regulatory Compliance Monitoring
- E-commerce Product Recommendations
- Code Generation and Debugging
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Qwen3.5‑class 397B models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.30 | $0.60 | 256K |
| Qwen | Global | ~220ms | ~50 tps | 99.9% | ~$0.50 | ~$1.00 | ~128K |
| Alibaba Cloud | APAC East | ~260ms | ~40 tps | 99.9% | ~$0.55 | ~$1.10 | ~128K |
| AWS Marketplace (Qwen Partner) | US East | ~250ms | ~35 tps | 99.9% | ~$0.60 | ~$1.20 | ~128K |
| Azure Marketplace (Qwen Partner) | EU West | ~240ms | ~38 tps | 99.9% | ~$0.58 | ~$1.15 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5 397B A17B | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~200ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | ~$2.00 | ~$5.00 | ~$3.00 |
| Output Price ($/1M) | ~$6.00 | ~$15.00 | ~$15.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ~70 tps | ~50 tps | ~55 tps |
| Uptime | ~99.9% | ~99.9% | ~99.9% |
30-day usage via LLM API
- 920B
- Prompt tokens processed (30 days)
- 3.4M
- API requests served (30 days)
- 1.8T
- Completion tokens generated (30 days)
- 99.96%
- 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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Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, price, and performance—no client changes required.
One endpoint, many models -
Cost-Aware Orchestration
Control spend with per-route pricing rules, automatic downgrades, and usage caps while keeping SLAs and quality intact.
Cut costs, keep quality -
Resilient Fallback Logic
Define multi-step failover chains so requests seamlessly retry on backup models or providers when outages or timeouts occur.
Never go dark -
Full-Stack Observability
Get end-to-end traces, latency histograms, provider error rates, and payload logs in one place to debug and optimize quickly.
See every token -
Task-Aware Abstractions
Use task-level APIs (chat, tools, embeddings, rerank, image) that stay stable even as underlying models and vendors change.
Code to tasks, not vendors -
High-Throughput Batch
Send massive batch jobs through a single endpoint with automatic sharding, rate limiting, and retries across providers.
Scale jobs, not scripts
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a very large frontier model for complex, multi-step reasoning tasks.
- You need strong general-purpose performance across coding, math, writing, and analysis.
- Your use case involves difficult enterprise workloads where raw model capability dominates cost.
- Your use case involves evaluating frontier-scale models for research, benchmarking, or comparisons.
- You need robust performance on diverse multilingual inputs but will read outputs in English.
- You need a powerful assistant to explore and prototype advanced agentic or tool-use workflows.
Avoid if...
- You need ultra-low inference cost for millions of short, simple requests daily.
- Your workload requires strict real-time latency budgets on resource-constrained hardware.
- You need an extremely lightweight model deployable on edge or mobile devices.
- Your workload requires guaranteed on-device inference without large GPU or TPU resources.
- You need a fully open-weights, easily self-hostable small model for customization.
- Your workload requires predictable throughput on limited infrastructure rather than peak model power.
FAQ
Frequently Asked Questions
-
What is Qwen3.5 397B A17B?
Qwen3.5 397B A17B is a large-scale Qwen language model accessible through LLM.API, optimized for complex reasoning, code, and high-quality text generation.
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What is Qwen3.5 397B A17B best suited for?
It excels at multi-step reasoning, advanced coding assistance, data analysis, and generating long-form, instruction-following content with strong coherence.
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How is Qwen3.5 397B A17B priced on LLM.API?
Pricing is usage-based per input and output token; check your LLM.API dashboard or pricing docs for the latest specific rates.
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What context window does Qwen3.5 397B A17B support?
Qwen3.5 397B A17B supports a long context window suitable for extended conversations and documents; refer to LLM.API docs for the current token limit.
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How fast is Qwen3.5 397B A17B in terms of latency?
As a very large model it has higher latency than smaller Qwen variants, but LLM.API streams tokens progressively to improve perceived responsiveness.
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What modalities does Qwen3.5 397B A17B support via LLM.API?
Through LLM.API it supports text input and output; check the model capabilities section to confirm any additional modalities like images if enabled.
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How do I call Qwen3.5 397B A17B through LLM.API?
Use the standard chat or completion endpoint, specifying the model name "qwen3.5-397b-a17b" (or listed identifier) in your LLM.API request payload.
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How does Qwen3.5 397B A17B compare to smaller Qwen models?
It generally offers stronger reasoning and generation quality than smaller Qwen models, at higher cost and latency per request.
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What are the main limitations of Qwen3.5 397B A17B?
It may hallucinate incorrect facts, struggle with real-time or proprietary data, and be too slow or expensive for latency-critical, high-throughput workloads.
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Can I use Qwen3.5 397B A17B for batch or server-side workloads?
Yes, you can run batch and backend workloads via LLM.API, but should account for its higher token cost and compute latency in your design.
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