#ai gateway

How to Use Crazyrouter for AI Coding Tools and Agents in 2026

AI coding tools are no longer just chat boxes. Claude Code, Codex-style agents, Cursor workflows, OpenClaw agents and internal automation scripts all need reliable model access, fallback, usage tracking and payload compatibility.

Crazyrouter can act as the API layer behind those tools: one key, one OpenAI-compatible base URL, many model routes.

Real API evidence

Real test evidence used in this article:

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`

The model list included coding-relevant routes such as `qwen3-coder-480b-a35b-instruct`, GPT routes, Claude routes, Gemini routes, Qwen routes and GLM routes.

Basic configuration pattern

Most AI coding tools that support an OpenAI-compatible endpoint need three values:

Base URL: https://cn.crazyrouter.com/v1 API key: YOUR_CRAZYROUTER_API_KEY Model: choose a model ID from /v1/models

Example Python client:

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="qwen3-coder-480b-a35b-instruct", messages=[ {"role":"system","content":"You are a careful coding assistant."}, {"role":"user","content":"Refactor this function and explain the tests."}, ], max_tokens=600, )

Coding-agent routing map

| Agent task | Suggested route type | Why |

|---|---|---|

| Small code edits | fast/cheap coding-capable model | keep latency low |

| Large refactor | stronger coding/reasoning route | reduce broken changes |

| Test generation | model with structured output discipline | easier validation |

| RAG answer | retrieval-aware route | factual grounding matters |

| Tool planning | stronger reasoning route | agent steps compound errors |

| Review pass | different model family | catches blind spots |

Why agents need gateways

Agent workflows are expensive because they multiply calls:

plan -> inspect files -> edit -> run tests -> fix -> review -> summarize

If every step uses the strongest model, cost explodes. If every step uses the cheapest model, quality collapses. A gateway lets you route by task.

Test results across routes

| Tested model | HTTP | Latency | Prompt tokens | Completion tokens | Total tokens | Note |

|---|---:|---:|---:|---:|---:|---|

| `gpt-4o-mini` | 200 | 2.9s | 39 | 53 | 92 | stop |

| `qwen-plus` | 200 | 3.69s | 40 | 42 | 82 | stop |

| `glm-4-flash` | 200 | 5.54s | 34 | 47 | 81 | stop |

| `deepseek-chat` | 200 | 3.27s | 36 | 180 | 216 | returned reasoning tokens, empty content at max_tokens=180; useful validation/fallback example |

| `qwen3-coder-480b-a35b-instruct` | 200 | 28.53s | 40 | 47 | 87 | stop |

The test shows a key coding-agent lesson: model availability is not enough. Agents must check content shape, finish reason and token limits. A route can return HTTP 200 but still require fallback or retry.

Practical setup for tools

Cursor-like IDE tools

Use Crazyrouter as the OpenAI-compatible endpoint when the tool supports custom base URL. Put the base URL in the model provider settings, then choose model IDs that match task cost and quality.

Crazyrouter vs Vercel AI Gateway: Pricing, Models and Use Cases in 2026

If you are comparing **Crazyrouter vs Vercel AI Gateway**, the real question is not which homepage sounds better. The practical question is: which gateway fits your production workflow, model coverage, pricing expectations, deployment stack and migration path?

Vercel AI Gateway is attractive for teams already building on Vercel and the AI SDK. Crazyrouter is attractive for teams that want broad model access, OpenAI-compatible routing and a gateway that is not tied to one frontend hosting platform.

Quick verdict

Use **Vercel AI Gateway** when your app is already deeply built around Vercel, the Vercel AI SDK, and you want a framework-native gateway experience.

Use **Crazyrouter** when your priority is broad model families, one OpenAI-compatible base URL, model experimentation across GPT, Claude, Gemini, Qwen, DeepSeek, GLM, image and video routes, and a gateway layer that can be used from any backend.

Real Crazyrouter API evidence

Real test evidence used in this article:

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 matters because gateway comparison should not stop at feature tables. A gateway has to list models, accept real payloads, return usage data and let developers switch model IDs without rewriting the app.

Pricing comparison: what to actually compare

Do not compare only the marketing plan price. For an AI gateway, compare:

1. raw model route price;

2. gateway markup or credit model;

3. retries and failed outputs;

4. latency impact;

5. whether usage data is returned consistently;

6. engineering time saved by one API layer;

7. whether the gateway locks you into one deployment platform.

Vercel AI Gateway is easiest to evaluate inside a Vercel stack. Crazyrouter is easiest to evaluate as an OpenAI-compatible API endpoint:

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="gpt-4o-mini", messages=[{"role":"user","content":"Say hello."}], )

Model coverage and routing

Crazyrouter returned `262` models in this test, including GPT, Claude, Gemini, Qwen, DeepSeek, GLM, Kimi, image-generation and video-generation routes.

A practical routing map:

| Workload | Gateway requirement | Why it matters |

|---|---|---|

| SaaS chatbot | cheap fast model + fallback | reduce cost per conversation |

| Coding agent | stronger reasoning/coding route | code quality varies by task |

| RAG app | stable JSON + long context | retrieval output must be validated |

| Image/video product | multimodal model access | text-only gateways are not enough |

| Internal automation | logs, usage and model switching | operations teams need visibility |

Production test results

| Tested model | HTTP | Latency | Prompt tokens | Completion tokens | Total tokens | Note |

|---|---:|---:|---:|---:|---:|---|

| `gpt-4o-mini` | 200 | 2.9s | 39 | 53 | 92 | stop |

| `qwen-plus` | 200 | 3.69s | 40 | 42 | 82 | stop |

| `glm-4-flash` | 200 | 5.54s | 34 | 47 | 81 | stop |

| `deepseek-chat` | 200 | 3.27s | 36 | 180 | 216 | returned reasoning tokens, empty content at max_tokens=180; useful validation/fallback example |

| `qwen3-coder-480b-a35b-instruct` | 200 | 28.53s | 40 | 47 | 87 | stop |

The DeepSeek route example is especially useful: HTTP 200 does not automatically mean usable final content. It returned reasoning tokens and hit the output limit with empty final content under this constrained prompt. Production gateways need validators, fallbacks and task-specific max token settings.

Crazyrouter vs Vercel AI Gateway: side-by-side

| Dimension | Crazyrouter | Vercel AI Gateway |

|---|---|---|

| Best fit | Multi-model API access from any stack | Vercel-native AI SDK apps |

| Migration style | OpenAI-compatible base URL | Vercel AI SDK/provider integration |

| Model strategy | Broad model families and multimodal routes | Gateway inside Vercel ecosystem |

| Lock-in risk | Lower if you keep OpenAI-compatible client code | Higher if app logic is Vercel-specific |

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.

Real Crazyrouter test

Real test evidence used in this article:

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

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.

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`

Live chat-completion test results

| Tested model | HTTP | Latency | Prompt tokens | Completion tokens | Total tokens | Note |

|---|---:|---:|---:|---:|---:|---|

| `gpt-4o-mini` | 200 | 2.9s | 39 | 53 | 92 | stop |

| `qwen-plus` | 200 | 3.69s | 40 | 42 | 82 | stop |

| `glm-4-flash` | 200 | 5.54s | 34 | 47 | 81 | stop |

| `deepseek-chat` | 200 | 3.27s | 36 | 180 | 216 | returned reasoning tokens, empty content at max_tokens=180; useful validation/fallback example |

| `qwen3-coder-480b-a35b-instruct` | 200 | 28.53s | 40 | 47 | 87 | stop |

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.

Recommended routing pattern

| Task | First route | Fallback route | Validation |

|---|---|---|---|

| Low-cost summarization | Qwen or GLM flash route | GPT/Gemini mini route | non-empty content |

| Chinese content | GLM/Qwen | stronger Qwen/Claude/GPT route | language + facts |

The production AI market has already split.

Vercel published usage data from 200,000+ teams on its AI Gateway (April 2026). Anthropic takes 61% of spend. Google takes 38% of volume. OpenAI tripled its spend share in one month.

58.9% of tokens now come from agentic workloads. 3.5% of requests trigger fallback, protecting roughly 5% of cost.

The teams running at scale are routing 30+ models by task type. The ones still on a single provider are overpaying on some tasks and underperforming on others.

Model selection is a routing problem now, not a one-time decision.

Hydropower stations play a vital role in clean energy production, where the core equipment—turbines and generator units—directly impacts generation efficiency and operational safety. Traditional monitoring systems rely heavily on centralized servers for data processing, resulting in significant transmission delays, limited real-time capabilities, and challenges in promptly detecting equipment anomalies.

The rapid advancement of artificial intelligence (AI) and edge computing has made on-site intelligent analysis and decision-making via the ARM AI Edge Box a key trend in upgrading hydropower stations to smarter, more efficient operations.

System Solution

The ARM AI Edge Box is deployed directly at the hydropower unit site, integrating with vibration sensors, water pressure sensors, and current/voltage acquisition modules via industrial interfaces such as RS485, CAN, and Ethernet.

Locally, the system handles data acquisition, AI inference, and initial decision-making. Critical data and alerts are then transmitted to the cloud platform using protocols like MQTT and Modbus/TCP, enabling seamless edge-cloud collaboration.

Key monitoring parameters include:

Turbine Vibration Status: Real-time detection of rotor, bearing, and blade vibrations to identify imbalances and mechanical looseness.

Water Pressure Fluctuations: Monitoring inlet and outlet pressures to prevent water hammer damage from anomalies.

Generator Efficiency Analysis: AI-driven calculation of hydraulic-to-electrical energy conversion efficiency to optimize unit performance.

Temperature and Energy Consumption: Tracking equipment temperature rises and energy ratios for enhanced energy management.

AI Edge Intelligence Features

Equipped with a high-performance Neural Processing Unit (NPU), the ARM AI Edge Box supports leading AI frameworks like TensorFlow Lite and ONNX. It enables local execution of algorithms for fault detection, trend forecasting, and energy optimization.

Even in offline or high-latency network conditions, the system operates autonomously, ensuring continuous monitoring and alerting.

Core advantages of edge intelligence:

Real-Time Response: On-device analysis eliminates delays from cloud processing.

Bandwidth Efficiency: Only essential data and events are uploaded.

Predictive Maintenance: AI models forecast equipment degradation for proactive servicing.

Local Autonomy: Supports on-site logic control and adaptive operations.

Cloud and Platform Collaboration

The edge box integrates with the cloud monitoring platform in a closed-loop architecture:

Edge Layer: Collects and analyzes data on-site.

Cloud Layer: Aggregates data, visualizes trends, and deploys operational policies.

User Interface: Web or mobile apps provide real-time views of unit status and health scores for maintenance teams.

This edge-cloud synergy boosts system flexibility and overall intelligence.

Application Value

Enhances operational safety and reliability of hydropower units.

Minimizes unplanned outages and equipment failures.

Optimizes generation efficiency and energy utilization.

Reduces manual inspections and maintenance costs.

Enables remote monitoring and smart scheduling.

Summary

Leveraging ARM architecture, the AI Edge Box delivers a high-performance, low-power intelligent monitoring solution tailored for hydropower stations. By combining local AI analytics with cloud-based management, it provides comprehensive oversight of turbine vibrations, water pressure, and generation efficiency—empowering the digital transformation of legacy hydropower systems.