- Instruction Following
GPT-5.1 is an OpenAI language model; as of mid-2026, OpenAI has not publicly released technical details or documentation about it.
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Qwen3.5-27B
- Instruction Following
Qwen3.5-27B is a 27B-parameter open-weight large language model from Qwen, offering strong reasoning and coding performance with a long context window and efficient hybrid attention architecture.
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 Qwen3.5-27B?
Qwen3.5-27B is a 27-billion-parameter dense language model in the Qwen 3.5 series designed for high-quality text generation and reasoning across general-purpose tasks. It is commonly used for code assistance, data analysis, and tool-augmented agents that benefit from strong reasoning at relatively modest compute cost. It is also deployed for chatbots, drafting, and knowledge-intensive applications that need long-context understanding (up to around 262k tokens) on both cloud and optimized local setups. Qwen3.5-27B belongs to the Qwen family of models developed by Alibaba/Qwen, following earlier Qwen2.x generations and preceding later Qwen3.x and Qwen3.6 variants.
Model capabilities
5 Core Capabilities
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Conversational Chat
Handles multi-turn, instruction-following conversations, maintaining context and generating coherent, helpful responses across diverse everyday and professional topics.
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Code Generation
Writes and edits code in multiple languages, explains programming concepts, and assists with debugging and refactoring software snippets or scripts.
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Multilingual Translation
Translates between major languages, preserving meaning and tone, and supports cross-lingual understanding in general and technical domains.
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Vision Understanding
Analyzes images to recognize objects, text, and layouts, and can answer questions about visual content and relationships.
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Optical Text Reading
Performs optical character recognition on images, extracting readable text from photos, screenshots, scanned documents, and complex backgrounds.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice Data Extraction
- Legal Document Review
- Regulatory Change Monitoring
- E-commerce Product Copywriting
- Code Generation Assistance
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Qwen3.5-27B–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 110ms | 120 tps | 99.99% | $0.05 | $0.10 | 200K |
| Qwen (Official API) | Global | ~180ms | ~60 tps | 99.9% | ~$0.20 | ~$0.40 | 128K |
| Alibaba Cloud | APAC | ~220ms | ~80 tps | 99.9% | ~$0.24 | ~$0.48 | ~128K |
| Together AI | US East | ~190ms | ~70 tps | 99.9% | ~$0.18 | ~$0.36 | ~128K |
| Fireworks AI | US West | ~160ms | ~90 tps | 99.9% | ~$0.16 | ~$0.32 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Qwen3.5-27B (Qwen) | Llama 3.1 70B (Meta) | GPT-4.1 (OpenAI) |
|---|---|---|---|
| Avg Latency | ~220ms | ~260ms | ~240ms |
| Context Window | 32K | 32K | 128K |
| Input Price ($/1M) | ~$0.40 | ~$0.60 | ~$5.00 |
| Output Price ($/1M) | ~$0.80 | ~$0.90 | ~$15.00 |
| Max Output Tokens | 4K | 4K | 8K |
| Throughput | ~45 tps | ~40 tps | ~50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (last 30 days)
- 2.1M
- API requests served (last 30 days)
- 13.9B
- 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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Intelligent Model Routing
Define routing rules once and automatically send each request to the best model by provider, latency, or capability—no client changes when backends evolve.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by mixing premium and budget models, enforcing per-project limits, and using smart downgrade paths without touching your application code.
Cut costs, keep quality -
Resilient Fallback Flows
Automatically retry failed or slow requests on alternate models or providers, keeping your AI features online even when individual APIs are down.
Designed for failure -
End-to-End Observability
Trace every request across models and providers with logs, metrics, and latency breakdowns, so you can debug prompts and performance in one place.
See every token -
Task-Level Abstractions
Describe intent—chat, tools, RAG, classification—and let LLM.API pick the right model, parameters, and tools, standardizing behavior across vendors.
Code to tasks, not models -
High-Throughput Batch Jobs
Process millions of prompts via batch APIs with automatic sharding, concurrency control, and retries, turning bulk AI workloads into simple background jobs.
Scale from day one
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM for chatbots, agents, and virtual assistants.
- You need strong reasoning and coding ability without paying for frontier-model pricing.
- Your use case involves generating or editing multilingual text across many common languages.
- Your use case involves mid-length documents where good comprehension and summarization matter.
- You need an open-weight model that can be self-hosted and tightly controlled.
- Your use case involves tool-using agents that call APIs or structured functions reliably.
- You need a balance of throughput and intelligence for batch content or code generation.
Avoid if...
- You need cutting-edge reasoning or creativity matching the very best frontier models available.
- Your workload requires extremely long-context processing, like full books or multi-hour transcripts.
- You need highly specialized domain performance that depends on proprietary commercial training data.
- Your workload requires ultra-low latency responses on resource-constrained edge or mobile devices.
- You need guaranteed best-in-class safety, alignment, and red-teaming from a major cloud vendor.
- Your workload requires deeply integrated ecosystem features from another provider’s proprietary stack.
- You need enterprise-grade support SLAs and compliance certifications from a globally established vendor.
FAQ
Frequently Asked Questions
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What is Qwen3.5-27B?
Qwen3.5-27B is a 27-billion-parameter large language model from Qwen focused on strong general-purpose reasoning and coding capabilities.
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What is the context window of Qwen3.5-27B?
Qwen3.5-27B supports a context window of up to 32K tokens for prompts plus generated output, depending on LLM.API configuration.
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What is Qwen3.5-27B best suited for?
Qwen3.5-27B is well-suited for complex reasoning, multi-step problem solving, high-quality coding assistance, and robust multilingual generation tasks.
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How is Qwen3.5-27B priced when accessed through LLM.API?
LLM.API exposes Qwen3.5-27B with per-token input and output pricing; check the LLM.API pricing page for the latest specific rates.
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How fast is Qwen3.5-27B on LLM.API?
Latency depends on load and request size, but Qwen3.5-27B typically returns first tokens within a few seconds for standard prompts.
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Which modalities does Qwen3.5-27B support via LLM.API?
Qwen3.5-27B is available on LLM.API as a text-only model, accepting and producing natural language and code tokens.
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How do I call Qwen3.5-27B using the LLM.API?
Use the LLM.API chat or completion endpoint with the model identifier "Qwen3.5-27B" and include your API key in the Authorization header.
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How does Qwen3.5-27B compare to similar mid-to-large LLMs?
Qwen3.5-27B typically offers stronger reasoning and coding accuracy than many smaller open models while being cheaper than comparable proprietary frontier models.
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What are the main limitations of Qwen3.5-27B?
Qwen3.5-27B can hallucinate facts, lacks real-time browsing, may reflect training-data biases, and should not be used for safety-critical decisions without verification.
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Does Qwen3.5-27B support function calling or structured outputs on LLM.API?
Yes, when enabled by LLM.API, Qwen3.5-27B can follow JSON schemas or tool/function-calling specifications for structured responses.
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