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How to Access DeepSeek, Qwen and GLM Models with One API in 2026

A tested guide to accessing DeepSeek, Qwen and GLM model families through one OpenAI-compatible API endpoint using Crazyrouter.

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Crazyrouter Team
June 18, 2026 / 935 views
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How to Access DeepSeek, Qwen and GLM Models with One API in 2026

How to Access DeepSeek, Qwen and GLM Models with One API in 2026#

DeepSeek, Qwen and GLM are now part of the serious production LLM stack. The problem is that integrating every provider separately creates API-key sprawl, different SDK patterns, separate billing flows and inconsistent monitoring.

The cleaner approach is to use one OpenAI-compatible gateway and switch model IDs by task.

Unified API for Chinese LLMs

Real Crazyrouter test#

Real test evidence used in this article:

text
Base URL: https://cn.crazyrouter.com/v1
Test date: 2026-06-18T14:58:18Z
GET /v1/models: HTTP 200, 620 ms, 262 models returned
DeepSeek routes found: 2
Qwen routes found: 20+
GLM routes found: 20+

Sample model families discovered by /v1/models:

  • DeepSeek: deepseek-v4-flash, deepseek-v4-pro
  • Qwen: qwen3-vl-plus, qwen2.5-coder-14b-instruct, qwen2-vl-72b-instruct, qwen3-coder-480b-a35b-instruct, qwen3-vl-30b-a3b-instruct, qwen3-30b-a3b, qwen-plus, qwen2.5-72b-instruct
  • GLM: glm-5v-turbo, glm-4-flash, glm-4.1v-thinking-flash, glm-5-turbo, glm-5, glm-4.5-flash, glm-4.5, glm-4v

This confirms that a single /v1/models endpoint exposed DeepSeek, Qwen and GLM routes in one model list.

Why this matters#

If you build with Chinese and global LLMs, you usually want:

  • DeepSeek for cost-efficient reasoning/coding routes;
  • Qwen for broad model family coverage, coding, vision and multilingual tasks;
  • GLM for Chinese-language workflows, OCR/vision and agent tasks;
  • GPT/Claude/Gemini fallbacks for quality-sensitive tasks;
  • one billing and logging layer.

Basic OpenAI-compatible setup#

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_CRAZYROUTER_API_KEY",
    base_url="https://cn.crazyrouter.com/v1",
)

resp = client.chat.completions.create(
    model="qwen-plus",
    messages=[{"role":"user","content":"Summarize this API design in 3 bullets."}],
    temperature=0.2,
)

print(resp.choices[0].message.content)

To switch to another family, change only the model ID.

python
model="glm-4-flash"
# or model="deepseek-chat" depending on available route mapping

Model families found in this test#

DeepSeek routes#

deepseek-v4-flash, deepseek-v4-pro

Qwen sample routes#

qwen3-vl-plus, qwen2.5-coder-14b-instruct, qwen2-vl-72b-instruct, qwen3-coder-480b-a35b-instruct, qwen3-vl-30b-a3b-instruct, qwen3-30b-a3b, qwen-plus, qwen2.5-72b-instruct

GLM sample routes#

glm-5v-turbo, glm-4-flash, glm-4.1v-thinking-flash, glm-5-turbo, glm-5, glm-4.5-flash, glm-4.5, glm-4v

Model coverage evidence

Live chat-completion test results#

Tested modelHTTPLatencyPrompt tokensCompletion tokensTotal tokensNote
gpt-4o-mini2002.9s395392stop
qwen-plus2003.69s404282stop
glm-4-flash2005.54s344781stop
deepseek-chat2003.27s36180216returned reasoning tokens, empty content at max_tokens=180; useful validation/fallback example
qwen3-coder-480b-a35b-instruct20028.53s404787stop

The result shows why production teams should validate outputs, not only HTTP status. Some routes returned clean content; the DeepSeek test hit the token limit and returned reasoning tokens without final content under this constrained prompt. That is exactly the kind of case where a gateway-based fallback strategy helps.

TaskFirst routeFallback routeValidation
Low-cost summarizationQwen or GLM flash routeGPT/Gemini mini routenon-empty content
Chinese contentGLM/Qwenstronger Qwen/Claude/GPT routelanguage + facts
Coding helperQwen coder routeGPT/Claude coding routetests/build output
ReasoningDeepSeek routeGPT/Claude/Gemini routefinal answer present
Extraction JSONstable JSON routeretry with stricter schemaJSON parse

Why one API is better than separate integrations#

Separate integrations seem simple at first, but production complexity grows quickly:

  • every provider has its own API key lifecycle;
  • model IDs change;
  • rate limits differ;
  • usage fields vary;
  • fallback logic becomes duplicated;
  • finance teams lose one clean spending view.

A gateway turns this into one integration surface.

Routing workflow for DeepSeek Qwen GLM

FAQ#

Can I access DeepSeek, Qwen and GLM with one API key?#

Yes. In this test, Crazyrouter exposed DeepSeek, Qwen and GLM routes through https://cn.crazyrouter.com/v1/models.

Is the API OpenAI-compatible?#

The tested Chat Completions flow uses the OpenAI SDK with base_url="https://cn.crazyrouter.com/v1".

Which model family should I use first?#

Use Qwen or GLM for many Chinese/multilingual tasks, DeepSeek for cost-efficient reasoning experiments, and stronger GPT/Claude/Gemini routes when accuracy or formatting needs escalation.

Is HTTP 200 enough?#

No. Always validate content, JSON shape, finish reason and token limits.

Bottom line#

DeepSeek, Qwen and GLM are useful individually, but they are much easier to operate through one gateway. Crazyrouter lets developers keep one OpenAI-compatible client while routing across Chinese and global model families.

Start here: Crazyrouter

Implementation Guides

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