denso 27B multimodal modelo proporcionando consistent, predictable latencia y robust visión-idioma comprensión para dedicado 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
- denso arquitectura ofrece simple servicio sin especializado MoE enrutamiento software
- Stable latencia bajo heavy multi-tenant concurrency
- Clean visión-idioma alignment para visual búsqueda y clasificación
Limitations & Trade-offs
- requiere más memoria por activo token en comparación con sparse 35B-A3B MoE
- menor pico razonamiento precisión que larger sibling modelos
Editorial Assessment
un steady, dependable denso multimodal foundation para estándar producción clústeres.
Best for: dedicado autohospedado API endpoints y visión clasificación canalizaciones
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: dedicado autohospedado API endpoints y visión clasificación canalizaciones. Assessment: un steady, dependable denso multimodal foundation para estándar producción clústeres.
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:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
La capacidad de un modelo de detectar la necesidad de una función externa y emitir argumentos JSON estructurados para ejecutar API, búsquedas web o consultas a bases de datos.
Un protocolo abierto desarrollado por Anthropic que permite a los agentes de IA conectarse de forma estandarizada a sistemas de archivos locales, entornos de desarrollo y servicios externos.
Una red neuronal multimodal diseñada para interpretar, razonar y generar contenido combinando simultáneamente imágenes, diagramas, capturas de pantalla y texto en lenguaje natural.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Qwen Open Weights Repository↗