Migrating from OpenAI to Qwen models using OpenAI compatible APIs

Switching to Qwen models: how to move away from OpenAI without rewriting your stack

Organisations are increasingly exploring alternatives to proprietary large language model APIs because of concerns around cost, vendor lock-in, and data governance. Open-weight models from the Qwen family provide a practical path for teams that want more control over infrastructure while keeping their existing application architecture.

One advantage is that the OpenAI API format has become a common standard. Hyperfusion exposes its models through an OpenAI compatible API, so engineering teams can reuse the same client libraries and integration patterns they already use for OpenAI. In most cases, applications keep working with minimal changes while the model behind them changes.

This does not remove the need to evaluate the new model, but it significantly reduces the amount of application-level refactoring required during migration.

The limits of proprietary model APIs

OpenAI and similar providers defined the early standard for large language model APIs. As organisations scale AI workloads, several operational constraints often appear.

Cost variability

Frontier-model token prices become difficult to absorb when usage grows, especially for customer-facing products with fluctuating demand.

Vendor lock-in

Applications that rely heavily on a single proprietary provider may struggle to adapt if pricing structures, rate limits, or product policies change.

Data governance

Some organisations must run inference within specific geographic regions to satisfy regulatory or security requirements.

Open-weight models served through a compatible API let teams keep application compatibility while gaining more control over cost and data location.

Qwen on Hyperfusion

The Qwen model family is developed by Alibaba's Qwen team. Hyperfusion serves Qwen3.8-27B, a 27B dense model suited to conversation, summarisation, coding assistance and document processing. It accepts both text and image input, which makes it useful for tasks such as extracting data from scanned documents.

The same endpoint and API key also give access to gpt-oss-120b for more demanding reasoning workloads, gpt-oss-20b for fast, low-cost requests, Gemma 4 31B, and DeepSeek-V4-Flash. Moving a request from one model to another is a change of the model parameter.

Using the OpenAI compatible API with Qwen

Below is a simplified Python example using the OpenAI client library.

Example using OpenAI

from openai import OpenAI

client = OpenAI(api_key="YOUR_OPENAI_API_KEY")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Fix my code"}],
)

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

The same call on Hyperfusion

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_HYPERFUSION_API_KEY",
    base_url="https://api.hyperfusion.io/v1",
)

response = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=[{"role": "user", "content": "Fix my code"}],
)

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

The application code stays the same. The differences are the API key, the base URL and the model name.

Migrating production workloads still involves latency testing, prompt validation, monitoring and cost modelling, but none of that requires changing how your application talks to the model.

What running these models takes

Serving open-weight models in production is more than downloading the weights. A production deployment needs the following components.

GPU infrastructure

High-throughput inference runs on accelerators such as NVIDIA H100, H200 and B300 GPUs.

Optimised inference engines

Inference frameworks such as vLLM improve throughput through continuous batching and memory optimisation.

Monitoring and observability

Production systems need monitoring of latency, GPU memory, token throughput, and request queues.

Hyperfusion runs all of this in its UAE data centres and exposes the models through a single API, so your team works with an endpoint and a key instead of a GPU cluster.

Why teams are exploring open models

Infrastructure flexibility

Open-weight models are not tied to one vendor: the same model can be served by a managed platform today and on your own hardware later.

Data control

Inference can stay within a specific geographic region.

Model customisation

Open-weight models allow fine-tuning and domain adaptation for specific use cases.

Cost optimisation

For sustained high-volume workloads, open-weight model prices are a fraction of frontier-model prices.

A practical migration strategy

For most engineering teams, replacing proprietary APIs at once is not realistic. A more practical strategy is to introduce additional model providers behind a common interface.

A typical migration path includes the following steps.

• Introduce an abstraction layer for model providers
• Test open-weight models on non-critical workloads
• Benchmark latency and output quality
• Gradually shift production traffic where appropriate

This approach keeps flexibility while avoiding disruptive rewrites.

The future of model infrastructure

The AI ecosystem is moving towards interchangeable model infrastructure rather than tightly coupled APIs. Open-weight models, compatible interfaces, and regional GPU infrastructure make it possible for engineering teams to choose the model that best fits each workload.

For companies building AI products at scale, the long-term goal is not simply replacing one provider with another. The goal is a system where models can evolve without forcing large changes in the application layer.