Qwen3.7-Plus DashScope Routing Policy: Default Tier for Coding Agents

Alibaba Cloud's DashScope platform has quietly but definitively moved qwen3.7-plus into the recommended default balanced tier, displacing qwen3.6 models for most new workloads. For AI teams that route through DashScope — whether directly or via an OpenAI-compatible gateway — this means updating your qwen3.7-plus DashScope routing policy and re-evaluating which model ID you hand to each request class.

The New Three-Tier Routing Matrix

DashScope now positions its text-generation lineup around three routing tiers:

The key routing signal here is that qwen3.7-plus is no longer a premium-only choice. It is the explicit replacement for qwen3.6 at the balanced tier, carrying the same 1M token context window with structural output and batch inference support that qwen3.7-max still lacks.

Capability Gaps That Change Your Route Logic

Not all Qwen 3.7 models are equivalent. The capability matrix has meaningful gaps that affect routing decisions:

qwen3.7-max is the top-tier reasoning model, with a 256k thinking budget and a 1M context window. However:

• No built-in tools (no web search, no code interpreter via DashScope's managed toolset)
• Structured output (JSON mode) not supported
• Higher cost tier

qwen3.7-plus fills the gap with full feature parity for most production workloads:

• 1M context window, 64k max output, 256k thinking budget
• Built-in tools: web search, code interpreter, web scraping — all supported
• Structured output: supported
• Batch inference: supported
• 1M token context = identical to qwen3.7-max and qwen3.6-flash

qwen3.6-flash is the cost-optimized path. DashScope explicitly states its quality is close to qwen3.7-plus at lower cost, with the same context and feature support. This makes it the natural candidate for high-volume / cost-sensitive routes after validating that qwen3.7-plus meets quality bar.

For routing layers, the practical decision tree is:

1. Requires heavy reasoning, max capability → qwen3.7-max
2. Balanced: tools, structured output, batch, high quality → qwen3.7-plus (new default)
3. Cost-optimized at validated quality → qwen3.6-flash

The Responses API reasoning.effort Change

If your integration uses the Responses API format against DashScope, there is a parameter shift that matters. Thinking mode is now controlled via reasoning.effort in the Responses API path, not only via the enable_thinking parameter:

# Responses API format — DashScope
response = client.responses.create(
    model="qwen3.7-plus",
    input=[{"role": "user", "content": "Analyze this architecture diagram..."}],
    reasoning={"effort": "medium"}   # controls thinking depth on Responses API
)

For Chat Completions format, enable_thinking still applies. The reasoning.effort route is relevant for teams using the Responses-API-compatible path, where thinking mode control aligns with how OpenAI's o-series reasoning models are configured.

All Qwen 3.7 models run as hybrid models — thinking mode is flexible per request, not baked into the model variant. This is different from older Qwen releases that shipped separate "thinking" and "no-thinking" variants.

Third-Party Models Now Listed Alongside Qwen

The DashScope routing page now surfaces third-party models in the same capability table, making it a de facto multi-provider routing guide for teams on Alibaba Cloud. The currently listed third-party options are:

Note that all third-party models on DashScope currently lack built-in tools. If your application relies on managed web search or code interpreter without custom function calling, qwen3.7-plus remains the only 1M-context model in this table with those capabilities.

What to Update Before Rerouting Traffic

Model ID updates. If you are currently routing to qwen3-plus or qwen3.6-plus, the new recommended target is qwen3.7-plus. Check for the versioned snapshot alias qwen3.7-plus-2026-05-26 if you need a pinned, non-rolling version.

Cost validation. qwen3.7-plus sits at a higher price point than qwen3.6-flash. If you were using qwen3.6 primarily for cost reasons and the step up to 3.7 adds overhead, re-evaluate whether qwen3.6-flash is sufficient — DashScope itself suggests it often is.

Structured output routes. Routes that previously fell back to Qwen models specifically for JSON mode can now route to qwen3.7-plus or qwen3.6-flash with structured output natively supported. qwen3.7-max cannot serve these routes.

Thinking budget cap. All three Qwen 3.7 models expose a thinking budget parameter. The max thinking budget is 256k tokens for qwen3.7-max and qwen3.7-plus; it is 128k for qwen3.6-flash. If your routing policy sets explicit thinking budgets on DashScope requests, verify the cap per model variant.

Deprecated older model names. The DashScope page marks Qwen 3 and older series as no longer recommended for new projects. Qwen3.5 is still listed as non-deprecated but not actively recommended. If you have routes pointing to qwen3-max (not qwen3.7-max), plan a migration.

Routing Through a Multi-Provider Gateway

When routing Qwen models through an OpenAI-compatible provider layer, the model identifier on DashScope uses the same name as the direct API — qwen3.7-plus, qwen3.7-max, qwen3.6-flash — with the base URL set to DashScope's endpoint. Thinking mode parameters differ by API format (Chat Completions vs Responses API), so verify which format your gateway bridges to before sending reasoning.effort.

TheRouter's provider routing configuration supports DashScope as a named provider. See the provider routing docs for how to register DashScope as a route target and configure model-tier fallback rules.

Related

• DashScope text generation model guide
• DashScope deep thinking guide
• TheRouter provider routing docs
• DeepSeek V4 migration on DashScope