Qwen3.7 Max

Alibaba DashScope chat

Qwen3.7 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.

Pricing source: litellm Last verified: Aug 6, 2026 View source ↗
Cost calculator

Estimate Qwen3.7 Max spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0991,808
400
065,536
5,000
01,000,000
cached @ $0.5000/M
Per request
$0.0105
in $0.007500 · out $0.003000
Per day
$52.50
5,000 requests
Per month
$1,598
152,188 requests

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.

Chatbot Arena ELOgeneral· overall↑3% vs peers
Captured Aug 8, 2026
Try it

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.

SDK
Native Future AGI client (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"))
Set 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).

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.