masivo abierto multimodal MoE modelo supporting joint visión-idioma razonamiento con un ultra-efficient 17B activo parámetro huella.
Technical Specifications
- Provider
- Alibaba
- Category
- open-source
- Release Date
- 2026-02
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- 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)
- qwen3-5-397b-a17b-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
- 246 GB VRAM (4-bit Quantized) · 854 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 397B-A17B 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 397B-A17B:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 58%
- GPQA Diamond
- 70%
- MMLU-Pro
- 83.5%
- HumanEval
- 94%
- Chatbot Arena Elo
- 1385pts
Strengths
- Unites nativo visión procesamiento con de frontera-clase codificación y matemático razonamiento
- Remarkably bajo inferencia costo para un nearly 400B modelo due un 17B activo enrutamiento
- sólido de código abierto alternative un propietario multimodal de frontera systems
Limitations & Trade-offs
- requiere sustancial unified memoria hardware (minimum 4x A100/H100 80GB)
- Quantized variants puede show slight degradation en precise UI coordinate prediction
Editorial Assessment
un tour de force en de pesos abiertos AI que brings multimodal de frontera power un en-premise clústeres.
Best for: autohospedado multimodal agentes, empresarial técnico documento procesamiento, y privado investigación
Compare Qwen3.5 397B-A17B
Frequently Asked Questions about Qwen3.5 397B-A17B
How much does the Qwen3.5 397B-A17B API cost per 1M tokens?+
Qwen3.5 397B-A17B 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 397B-A17B 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 397B-A17B's context window?+
Qwen3.5 397B-A17B 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 16,384 tokens.
Can I run Qwen3.5 397B-A17B locally on my own GPU (Open Weights)?+
Yes, Qwen3.5 397B-A17B (397B total (17B active MoE)) 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 397B-A17B 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 397B-A17B perform on coding and reasoning benchmarks?+
Qwen3.5 397B-A17B achieves a composite Intelligence Index of 86/100, scoring 58% on SWE-bench Verified, 70% on GPQA Diamond, 83.5% on MMLU-Pro, and 1385 pts on Chatbot Arena Elo.
What is Qwen3.5 397B-A17B best used for?+
Qwen3.5 397B-A17B is best suited for: autohospedado multimodal agentes, empresarial técnico documento procesamiento, y privado investigación. Assessment: un tour de force en de pesos abiertos AI que brings multimodal de frontera power un en-premise 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 397B-A17B:
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.
Un modelo de IA entrenado para invertir cómputo adicional en pensar y validar hipótesis lógicas antes de responder, diseñado para problemas complejos de ingeniería y matemáticas.
Una técnica de razonamiento paso a paso donde el modelo genera pensamientos intermedios antes de formular la respuesta final, mejorando radicalmente la resolución de problemas matemáticos y lógicos.
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.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.