Dense 27B multimodal model providing consistent, predictable latency and robust vision-language comprehension for dedicated servers.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2026-02
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- qwen3-5-27b-instruct
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 17 GB VRAM (4-bit Quantized) · 58 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Qwen3.5 27B Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Qwen3.5 27B:
Benchmark Scores & 6-Axis Capability Radar
- GPQA Diamond
- 56.5%
- MMLU-Pro
- 74%
- HumanEval
- 84%
- Chatbot Arena Elo
- 1285pts
Strengths
- Dense architecture offers simple serving without specialized MoE routing software
- Stable latency under heavy multi-tenant concurrency
- Clean vision-language alignment for visual search and classification
Limitations & Trade-offs
- Requires more memory per active token compared to sparse 35B-A3B MoE
- Lower peak reasoning accuracy than larger sibling models
Editorial Assessment
A steady, dependable dense multimodal foundation for standard production clusters.
Best for: Dedicated self-hosted API endpoints and vision classification pipelines
Compare Qwen3.5 27B
Frequently Asked Questions about Qwen3.5 27B
How much does the Qwen3.5 27B API cost per 1M tokens?+
Qwen3.5 27B by Alibaba is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Qwen3.5 27B pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Qwen3.5 27B's context window?+
Qwen3.5 27B features a 262K tokens context window, which equals approximately 196,608 English words or ~524 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Qwen3.5 27B locally on my own GPU (Open Weights)?+
Yes, Qwen3.5 27B (27B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Alibaba train on data sent to the Qwen3.5 27B API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Qwen3.5 27B perform on coding and reasoning benchmarks?+
Qwen3.5 27B achieves a composite Intelligence Index of 75/100, scoring N/A% on SWE-bench Verified, 56.5% on GPQA Diamond, 74% on MMLU-Pro, and 1285 pts on Chatbot Arena Elo.
What is Qwen3.5 27B best used for?+
Qwen3.5 27B is best suited for: Dedicated self-hosted API endpoints and vision classification pipelines. Assessment: A steady, dependable dense multimodal foundation for standard production clusters.
Alibaba Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Alibaba's flagship reasoning tier:
Qwen3.8 Max Preview
Qwen3.7 Plus
Qwen3.7 Max
Qwen3.6 Max Preview
Qwen3.6 Plus
Qwen3.6 Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Qwen3.5 27B:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A multimodal neural network engineered to jointly process, reason over, and synthesize both visual content (images, diagrams, UI screenshots, video) and natural language text within a single framework.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: Qwen Open Weights Repository↗