Qwen has a broad model lineup and a mature integration path, but it is not the default answer for every workload. Teams that switch models usually do so because of a concrete problem in one dimension: the cost of long documents, code pass rates, multimodal input, regional access, or a wish to spread vendor risk.

This article does not rank the vendors. It breaks the decision down by Chinese writing, code, long context, multimodal input, agents, access methods, and pricing structure, then adds a scenario table and a migration checklist. For models and prices, use the model pricing page.

Note: Qwen, Tongyi Qianwen, Alibaba Cloud, Model Studio, and DashScope are trademarks of Alibaba Group and its affiliates. DeepSeek, GLM, Kimi, and other names are trademarks of their respective owners. This article is a technical selection discussion only; it implies no affiliation, agency, or partnership with those companies. Capabilities, prices, and terms change, so check the official documentation before integrating.

1. First confirm the model is actually the problem

Before changing vendors, rule out four non-model causes: prompts without acceptance criteria, poor retrieval recall, sending every request to the most expensive model, and sharing one key across several services. Once these are fixed, the conclusion that "we must switch models" often disappears.

If a switch is still justified, the signal usually falls into one of these categories: a missing capability such as visual input, runaway cost on long-context tasks, unstable access in a particular region, a single-vendor outage that affects the whole business, or a compliance requirement to route data through another path. Validate the signal with a week of error-rate, rework-rate, and latency data.

2. How the four candidates differ

Model family Typical focus Multimodal Access method Billing traits
Qwen Broad lineup covering text, vision, and long context Relatively complete vision coverage Alibaba Cloud Model Studio / DashScope, OpenAI-compatible Token-based, with tiered pricing on some models
DeepSeek Cost control for text reasoning and coding tasks Not the main focus; see the official model list Own API, OpenAI-compatible Cached input priced differently; output billed separately
GLM Chinese-language tasks and multimodal combinations Text, vision, and image generation Own open platform, OpenAI-compatible Token-based, plus coding-style subscriptions
Kimi Long-context reading and agent-style tasks See the official model list Own open platform, OpenAI-compatible Token-based; measure the cost of long inputs

This table only shows where each vendor puts its resources; it is not a quality ranking. The variation within one model across different prompts is often larger than the average gap between models.

3. Chinese writing: build an evaluation set by genre

Chinese writing has no single metric. Marketing copy is judged on conversion, official documents on format, contracts on clause consistency, and spoken scripts on rhythm. Qwen and GLM have deep Chinese corpus experience, DeepSeek's reasoning helps with structurally complex drafts, and Kimi is better suited to reading long source material before rewriting it.

Draw 30 tasks from real business work, covering short copy, long-form writing, rewriting, and multi-round revision, then run a blind review with two raters. Split the score into factual accuracy, tone fit, structural completeness, and usability rate, so that "reads smoothly" cannot hide factual errors. Test numbers, names, and quotations separately; this is where incidents are more likely than in style.

4. Code and engineering tasks: let test pass rates speak

Code scenarios need further division: single-line completion, repository Q&A, unit-test generation, code review, and cross-file edits place very different demands on context and tool calling. All four vendors offer coding-oriented models; check the official documentation for exact names and versions.

Do not evaluate only whether the code "looks right." Run unit tests and static checks, and record first-pass rate, number of repair rounds, and the share of newly introduced defects. For repository Q&A, also measure file-location accuracy. If you rely on tool calls, confirm that the target model supports the JSON Schema, parallel calls, and streaming responses you need; otherwise migration will stall in the adaptation layer.

5. Long context and multimodal: calculate input cost first

Long context has two layers: whether the material fits, and whether the model can answer accurately once it does. Fitting an entire annual report into the model does not mean it can cite a specific page correctly. In testing, place the key sentence at the beginning, the middle, and the end, and ask once in each position.

Cost also needs a separate calculation. Some models use tiered pricing for very long inputs, so the unit price may rise as the input grows; cache hits can reduce the cost of repeated material. Test the final choice with your own document lengths and question patterns.

On multimodal input, Qwen's vision models have relatively broad coverage, and GLM combines vision with image generation. Whether DeepSeek and Kimi offer comparable interfaces depends on their official model lists. If you only need OCR or table extraction, a dedicated OCR service may be more reliable.

6. Agents and tool calls: stability beats leaderboards

Agent failures are often not caused by a model being unintelligent, but by malformed tool calls, missing parameters, or giving up midway. During selection, run a real 15-to-20-step workflow and count invalid tool calls, task completion rate, and average steps. That is more useful than a general leaderboard.

All four vendors offer function calling, but parameter naming, parallel calls, and streaming event structures differ. If you already have an agent framework, prefer interfaces with mature adapters and clear error messages. For multi-model routing, standardize the tool protocol at the gateway layer so you do not write a separate adapter for every model.

7. Access and regions: three configuration mistakes

All four provide OpenAI-compatible interfaces, so migration usually means changing only the base URL, API key, and model name. Qwen has one extra caveat: its API keys are bound to a region, and Beijing and Singapore keys cannot be mixed. A base URL that does not match the key returns 401 immediately.

The code below puts all four configurations in one table, so switching models only requires changing environment variables:

import os
from openai import OpenAI

PROFILES = {
    "qwen": {
        "base_url": os.environ.get(
            "QWEN_BASE_URL",
            "https://dashscope.aliyuncs.com/compatible-mode/v1",
        ),
        "model": os.environ["QWEN_MODEL"],
    },
    "deepseek": {
        "base_url": os.environ.get("DEEPSEEK_BASE_URL", "https://api.deepseek.com/v1"),
        "model": os.environ["DEEPSEEK_MODEL"],
    },
    "glm": {
        "base_url": os.environ.get("GLM_BASE_URL", "https://open.bigmodel.cn/api/paas/v4"),
        "model": os.environ["GLM_MODEL"],
    },
    "kimi": {
        "base_url": os.environ.get("KIMI_BASE_URL", "https://api.moonshot.cn/v1"),
        "model": os.environ["KIMI_MODEL"],
    },
}

def client_for(name: str):
    profile = PROFILES[name]
    client = OpenAI(
        api_key=os.environ[f"{name.upper()}_API_KEY"],
        base_url=profile["base_url"],
    )
    return client, profile["model"]

Do not hard-code model names in your code, and do not copy them from old tutorials. Vendors retire versions and change what aliases point to, so confirm model names against the official documentation and the pricing page.

8. Pricing models: compare "cost per qualified task"

All four bill by token, but the structures differ: separate input and output rates, cache discounts, tiered long-context pricing, batch discounts, subscription plans, and free allowances all affect the bill. Comparing only the listed price per million tokens can lead to the opposite conclusion.

A more reliable method is to count input, output, retries, and failed requests, then divide by the number of qualified tasks. For tasks with long outputs and many retries, the real cost can be two or three times the list price. To consolidate billing, you can connect through the ZSQH AI Gateway; see model pricing for current rates, which follow vendor adjustments.

9. Scenario selection table

Scenario Suggested starting point Notes
Chinese marketing, support, summaries Qwen or GLM Blind-test on the same sample; do not choose by reputation
Code completion, unit tests, review DeepSeek or a Qwen coding model Judge by test pass rate and rework rounds
Long-document review, research synthesis Kimi or a Qwen long-context model Test accurate citation and input cost together
Image, screenshot, and table understanding Qwen vision or GLM vision Confirm resolution and file-size limits first
Multi-step agent workflows Any, after stress-testing tool calls Focus on invalid calls and recovery ability
A single entry point for multiple models API gateway Unified keys, billing, logs, and fallback

10. Migration checklist

  1. Inventory every call site and mark interfaces that use vendor-specific parameters.
  2. Pin a snapshot of the current model version and keep a rollback configuration.
  3. Build an evaluation set of 30 to 50 real tasks and write down acceptance criteria.
  4. Run old and new models side by side with identical parameters, prompts, and output limits.
  5. Canary 5% to 10% of traffic and watch pass rate, p95 latency, and error rate.
  6. Configure a fallback model and automatic switching, and define which errors trigger it.
  7. Split monitoring by model, scenario, and version, with separate counts for 429s and timeouts.
  8. Update documentation and billing definitions with model names, regions, and known differences.

11. FAQ

1. Do I have to replace the Qwen API?

Not necessarily. First rule out prompt, retrieval, concurrency, and network issues. Switching models brings clear benefits only when a capability gap, cost structure, or compliance requirement cannot be solved in place.

2. Which of DeepSeek, GLM, and Kimi is best at Chinese?

There is no single answer. Writing, code comments, and long-document synthesis are different tasks, and the same model can vary widely across them. A blind review with real samples is more reliable than a leaderboard.

3. How much code changes when switching models?

If the provider is OpenAI-compatible, usually only the base URL, key, and model name change. If you use private parameters, multimodal fields, or a specific tool-call format, you need an adaptation layer and regression tests.

4. Can a long-context model replace RAG?

Not simply. Long context suits one-off reading and cross-paragraph reasoning, while RAG suits large knowledge bases and frequently updated material. The two can work together: retrieve first to narrow the scope, then analyze in depth.

5. How do I manage multiple models consistently?

A common approach is a gateway that unifies keys, billing, logging, and model routing, so the application sees one OpenAI-compatible endpoint. The trade-off is an extra dependency; verify failover and quota management before committing.

Next steps

Create a test key on the ZSQH AI Gateway, connect Qwen and the candidate models to the same endpoint, run one comparison round on the same task set, and then calculate cost against model pricing. Model availability and regional restrictions are governed by the official documentation.