Qwen3 Max preview
Alibaba DashScope chatQwen3 Max preview is an Alibaba DashScope chat model.It supports a 258,048-token context windowwith up to 65,536 output tokens. Capabilities include function calling, reasoning. Route Qwen3 Max preview via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Qwen3 Max preview yet. If you have a source from Alibaba DashScope's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Aug 6, 2026.
| Input | — | |
| Output | — |
Context & limits
- Context window
- 258,048 tokens
- Max input
- 258,048 tokens
- Max output
- 65,536 tokens
- Modalities
- text
Capabilities
- Function calling ✓ supported
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning ✓ supported
Strengths & caveats — when to pick Qwen3 Max preview
Data-driven from Qwen3 Max preview's percentile within chat peers.
Where it's strong
- +multi-step reasoning and analysis tasks
- +agentic workflows that depend on reliable tool calls
Watch out for
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Qwen3 Max preview via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Qwen3 Max preview via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="dashscope/qwen3-max-preview",
messages=[{"role": "user", "content": "Hello, Qwen3 Max preview!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="dashscope/qwen3-max-preview",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: qwen3-max-preview
provider: dashscope
weight: 80
fallbacks:
- model: gemini-3-1-pro-preview
provider: google
- model: claude-opus-5
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Compare with similar models
Grouped by Chatbot Arena tier (Qwen3 Max preview sits at 1435 ELO).
- Qwen-Plus (2025-07-14)Alibaba DashScope · $0.400/M in · $1.20/M out · 129,024 ctx
- Qwen-Turbo (2025-04-28)Alibaba DashScope · $0.0500/M in · $0.200/M out · 1,000,000 ctx
- Qwen3.7 MaxAlibaba DashScope · $2.50/M in · $7.50/M out · 991,808 ctx
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 1,000,000 ctx
- Gemini 3.1 Pro previewGoogle AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
- Claude Opus 5Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.7Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Gemini 3 Pro PreviewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
≥30 ELO higher
- Claude Fable 5Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctx
- Claude Opus 4.6Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6 (2026-02-05)Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 1,000,000 ctx
FAQ
How much does Qwen3 Max preview cost?
Public per-token pricing for Qwen3 Max preview is not yet published. Submit a source on this page to help us add it.
What is the context window of Qwen3 Max preview?
Qwen3 Max preview supports a 258,048-token context window with up to 65,536 output tokens.
Does Qwen3 Max preview support function calling?
Yes — Qwen3 Max preview supports function (tool) calling.
Is Qwen3 Max preview good for production?
Qwen3 Max preview is well-suited for multi-step reasoning and analysis tasks and agentic workflows that depend on reliable tool calls. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
How can I route to Qwen3 Max preview with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Qwen3 Max preview
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Start here — vendor docs are the only authoritative spec.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.