Qwen3.7 Max
Alibaba DashScope chatQwen3.7 Max is an Alibaba DashScope chat model.It supports a 991,808-token context windowwith up to 65,536 output tokens.Input is priced at $2.50/M tokens and output at $7.50/M tokens. Capabilities include function calling, reasoning, prompt caching. Route Qwen3.7 Max via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Qwen3.7 Max spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $2.50/M input · $7.50/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Aug 6, 2026.
| Input | $2.50/M | |
| Output | $7.50/M | |
| Blended (3:1 in:out) | $3.75/M Cross-model comparable price | |
| Cached input | $0.500/M |
Context & limits
- Context window
- 991,808 tokens
- Max input
- 991,808 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 ✓ supported
- Prompt caching ✓ supported
- Reasoning ✓ supported
Strengths & caveats — when to pick Qwen3.7 Max
Data-driven from Qwen3.7 Max's percentile within chat peers.
Where it's strong
- +multi-step reasoning and analysis tasks
- +agentic workflows that depend on reliable tool calls
- +repeated long-context prompts (caching saves up to 90% on input cost)
Watch out for
- No major caveats flagged from public spec.
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Qwen3.7 Max 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.# Qwen3.7 Max 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/qwen3-7-max",
messages=[{"role": "user", "content": "Hello, Qwen3.7 Max!"}],
)
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/qwen3-7-max",
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: qwen3-7-max
provider: dashscope
weight: 80
fallbacks:
- model: claude-fable-5
provider: azure-ai-foundry
- model: claude-opus-4-6
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Compare with similar models
Grouped by Chatbot Arena tier (Qwen3.7 Max sits at 1475 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
- Qwen3.8 MaxAlibaba DashScope · $2.00/M in · $6.00/M out · 1,000,000 ctx
- Qwen3 VL 235B A22b ThinkingAlibaba DashScope · $0.400/M in · $4.00/M out · 131,072 ctx
- 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
- Gemini 3.1 Pro previewGoogle AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
25–100 ELO lower, ≤50% of price
FAQ
How much does Qwen3.7 Max cost?
Input is priced at $2.50 per 1M tokens and output at $7.50 per 1M tokens (Alibaba DashScope, last verified Aug 6, 2026).
What is the context window of Qwen3.7 Max?
Qwen3.7 Max supports a 991,808-token context window with up to 65,536 output tokens.
Does Qwen3.7 Max support function calling?
Yes — Qwen3.7 Max supports function (tool) calling.
Is Qwen3.7 Max good for production?
Qwen3.7 Max is well-suited for multi-step reasoning and analysis tasks and agentic workflows that depend on reliable tool calls.
How can I route to Qwen3.7 Max 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 Qwen3.7 Max
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.