Qwen3.6 35B A3B API

Qwen3.6 35B A3B is a 256-expert mixture-of-experts reasoning model with 128K context, function tools, and strict structured JSON output.

Alibaba CloudTextgenerierung128K KontextVeröffentlicht 29. Juli 2026Native InferenzNeu

Über Qwen3.6 35B A3B

Qwen3.6 35B A3B is a 256-expert mixture-of-experts reasoning model with 128K context, function tools, and strict structured JSON output.

Text-only. This build does not accept image or video input, unlike the base Qwen3.6 35B A3B. Weights Served from the 2-bit eschamoe W2 build published by Escha Labs (eschalabs.com) as EschaLabs/Qwen3.6-35B-A3B-Escha-W2 on Hugging Face, under Apache-2.0. The experts are quantized to 2 bits, mixed per projection (gate_up_proj at 2-bit and down_proj at 3-bit), the dense layers are int8, and the KV cache is FP16. Escha Labs publishes the quality comparison against an FP8 baseline of the same model: parity or better on math, graduate science, tool use and long context, about 2 percent lower on broad knowledge, and about 7 percent lower on long-horizon code generation, which is the one clear gap. See the model card for the full benchmark table and protocol. Behavior Supports streaming, function tools, structured JSON output including strict schemas, and thinking mode on by default. Set enable_thinking=false for direct answers. With thinking on, the reasoning arrives in reasoning_content and the answer in content, so read both. A low max_tokens with thinking on can be spent entirely on reasoning, so allow room for the answer. Caching Automatic prefix cache reads are billed at the cached-input rate when reported. Explicit cache controls are not supported. Cancelling a streaming request mid-generation bills only the tokens produced up to that point.

Auch bekannt als Alibaba Cloud Qwen3.6 35B A3B, Qwen3.6-35B-A3B, qwen3-6-35b-a3b

reasoningfunction callingjson modecache

Qwen3.6 35B A3B Spezifikationen

Modell-ID
qwen3-6-35b-a3b
Anbieter
Alibaba Cloud
Kategorie
Textgenerierung
Veröffentlicht
29. Juli 2026
Kontextfenster
128K Token
Max. Ausgabe
16.384 Token
Eingabe
Text
Ausgabe
Text
Strukturierte Ausgabe
JSON Schema
Endpoints
POST/v1/chat/completionsPOST/v1/responsesPOST/v1/messagesPOST/v1/completionsPOST/v1beta/models/qwen3-6-35b-a3b:generateContent
Alternative Modell-IDs
qwen3.6-35b-a3bqwen/qwen3.6-35b-a3bEschaLabs/Qwen3.6-35B-A3B-Escha-W2

Qwen3.6 35B A3B API-PreiseSpare bis zu 72%

Live Pay-as-you-go-Preise aus dem EmpirioLabs-Katalog. Du zahlst nur für das, was du nutzt, ohne monatliches Minimum.

Typ
Spezifikation
Preis
Eingabe
per 1M prompt tokens
$0.248$0.07
Ausgabe
per 1M generated tokens
$1.485$0.42
Implicit cache read
per 1M cached input tokens
$0.035
Web Search (Linkup)
per call when invoked
$0.013
Auf der vollständigen Preisseite vergleichen

So rufst du die Qwen3.6 35B A3B API auf

Qwen3.6 35B A3B bedient die OpenAI-kompatible Chat Completions API. Richte ein beliebiges OpenAI SDK mit deinem EmpirioLabs API-Schlüssel auf https://api.empiriolabs.ai/v1 und verwende die Modell-ID qwen3-6-35b-a3b. Hol dir einen API-Schlüssel im EmpirioLabs Dashboard.

cURL
curl https://api.empiriolabs.ai/v1/chat/completions \
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-6-35b-a3b",
    "messages": [
      {"role": "user", "content": "Write a haiku about the ocean."}
    ]
  }'
Python (OpenAI SDK)
from openai import OpenAI

client = OpenAI(
    base_url="https://api.empiriolabs.ai/v1",
    api_key="YOUR_EMPIRIOLABS_API_KEY",
)

response = client.chat.completions.create(
    model="qwen3-6-35b-a3b",
    messages=[{"role": "user", "content": "Write a haiku about the ocean."}],
)
print(response.choices[0].message.content)
Vollständige Qwen3.6 35B A3B API-Referenz

Qwen3.6 35B A3B API-Parameter

Request-Parameter, die die Qwen3.6 35B A3B API auf EmpirioLabs unterstützt. Standardwerte gelten, wenn ein Feld weggelassen wird.

ParameterTypStandardBereich / WerteBeschreibung
temperaturenumber0.70 bis 2Sampling temperature. 0 is deterministic and 2 is maximum randomness.
top_pnumber0.950 bis 1Nucleus sampling probability mass. Lower values make outputs more focused.
max_tokensnumber40961 bis 16384Maximum output tokens. With thinking on, leave room for the answer as well as the reasoning.
stopstring--Up to 4 strings where the model will stop generating further tokens.
enable_thinkingbooleantrue-Enable the model reasoning channel before final output. Reasoning is returned in reasoning_content and bills as output tokens.
reasoning_effortenummediumnone, low, medium, high, maxReasoning effort. none disables thinking. Any other value enables it.
top_knumber201 bis 200Limit sampling to the top K candidate tokens.
min_pnumber00 bis 1Minimum probability threshold for token sampling.
presence_penaltynumber0-2 bis 2Penalize tokens that have already appeared, increasing topic diversity.
frequency_penaltynumber0-2 bis 2Penalize tokens in proportion to how often they have appeared.
seednumber-0 bis 2147483647Best-effort determinism. Output is not bit-reproducible across requests because batch composition changes accumulation order.
response_formatenum-text, json_object, json_schemaStructured output. json_object returns valid JSON. json_schema enforces your schema exactly.
web_search_linkupbooleanfalse-Optional web search powered by Linkup. When enabled, recent web sources are retrieved using your latest user message as the query and provided to the model as additional context. Adds $0.013 per call when invoked on top of the model's normal token cost. Disabled by default.

Gut zu wissen

Text-only. This build does not accept image or video input, unlike the base Qwen3.6 35B A3B.

Weights

Served from the 2-bit eschamoe W2 build published by Escha Labs (eschalabs.com) as EschaLabs/Qwen3.6-35B-A3B-Escha-W2 on Hugging Face, under Apache-2.0. The experts are quantized to 2 bits, mixed per projection (gate_up_proj at 2-bit and down_proj at 3-bit), the dense layers are int8, and the KV cache is FP16. Escha Labs publishes the quality comparison against an FP8 baseline of the same model: parity or better on math, graduate science, tool use and long context, about 2 percent lower on broad knowledge, and about 7 percent lower on long-horizon code generation, which is the one clear gap. See the model card for the full benchmark table and protocol.

Behavior

Supports streaming, function tools, structured JSON output including strict schemas, and thinking mode on by default. Set enable_thinking=false for direct answers. With thinking on, the reasoning arrives in reasoning_content and the answer in content, so read both. A low max_tokens with thinking on can be spent entirely on reasoning, so allow room for the answer.

Caching

Automatic prefix cache reads are billed at the cached-input rate when reported. Explicit cache controls are not supported. Cancelling a streaming request mid-generation bills only the tokens produced up to that point.

Qwen3.6 35B A3B API: häufige Fragen

Wie viel kostet die Qwen3.6 35B A3B API?

Auf EmpirioLabs wird Qwen3.6 35B A3B nach Verbrauch abgerechnet. Die Live-Preistabelle auf dieser Seite entspricht immer dem, was die API berechnet.

Wie groß ist das Kontextfenster von Qwen3.6 35B A3B?

Qwen3.6 35B A3B unterstützt ein Kontextfenster von 128K Token mit bis zu 16.384 Ausgabe-Token pro Antwort.

Ist die Qwen3.6 35B A3B API OpenAI-kompatibel?

Ja. Qwen3.6 35B A3B bedient die OpenAI-kompatible Chat Completions API. Bestehende OpenAI SDKs funktionieren, indem du base_url auf https://api.empiriolabs.ai/v1 setzt und als Modell-ID qwen3-6-35b-a3b verwendest.

Kann ich Qwen3.6 35B A3B im Browser testen, bevor ich integriere?

Ja. Der EmpirioLabs Playground führt Qwen3.6 35B A3B im Browser mit denselben Parametern aus, die die API bietet. So kannst du Prompts testen, bevor du Code schreibst.

Wie bekomme ich einen Qwen3.6 35B A3B API-Schlüssel?

Erstelle ein EmpirioLabs-Konto und generiere dann einen Schlüssel unter API Keys im Dashboard. Die Abrechnung erfolgt über Pay-as-you-go-Guthaben, du zahlst also nur für deine Requests.

Bereit, bessere Endpunkte zu nutzen?

Schauen Sie sich unsere Preise an oder kontaktieren Sie uns, wenn Sie Ihr eigenes Modell auf unserem Stack implementieren möchten.