Open-source vs proprietary LLMs in production: how to decide

Open-source vs proprietary LLMs in production: what differs

The difference between an open-weight model and a proprietary LLM in production is control: with open weights you choose who hosts the model, which licence you accept and where prompts are processed, and you can change host without changing the model. Run a production workload on an open-weight model when that model clears your quality bar on your own evaluation set. Keep a proprietary API where it gives the best tested combination of quality, latency, reliability, support, cost and contractual fit.

If you are deciding whether a production workload should leave a proprietary API, this guide gives you the test and the numbers.

Open source and open-weight are not the same thing. Most models called open source publish their weights under a licence. The Open Source Initiative's Open Source AI Definition also asks for training-data information and code. For production, check the weights and licence alongside evaluation results, provenance, security, operating requirements and the host's terms.

The API is no longer the deciding point. Lyceum is a sovereign European AI inference provider for open-source models: our serverless inference serves open models through one OpenAI-compatible API, billed per token, with no base fee, on GPUs in European data centres. Epoch AI reported on 29 May 2026 that the leading open models trailed frontier closed models by an average of 4 months on its Capabilities Index. That is historical benchmark evidence, not a guarantee about a current model or workload. Our serverless models run in Paris and Finland. Because the API format is shared, the choice turns on quality, licence terms, data handling, price and exit options.

How close open-weight models are to proprietary quality

Open-weight models are close to closed models on public leaderboards. The Stanford AI Index 2025 found that the leading closed-weight model outperformed the top open-weight model by 8.04% on the Chatbot Arena Leaderboard in January 2024. By February 2025 that gap had narrowed to 1.70%.

A public benchmark is still not your workload. A leaderboard aggregates general-purpose prompts, while a production team cares about output formatting, schema compliance, its own domain and its edge cases. That is the reason for reading open-model benchmarks critically instead of accepting a single headline number.

HELM, the Holistic Evaluation of Language Models from Stanford's Center for Research on Foundation Models (2022), scores models on seven metrics, accuracy being one:

• Accuracy
• Calibration
• Robustness
• Fairness
• Bias
• Toxicity
• Efficiency

Your own evaluation set decides. Two guides help here: how to test an open-weight model before you commit, and how to find the cheapest open model that clears your quality bar.

What open models cost at 1M and 10M tokens a day

An open model on Lyceum costs between $0.20 and $3.00 per 1M input tokens for the four models below. These are list prices in US dollars, read from each model's own page on lyceum.technology on 9 October 2026:

Each price is on the model's page: GLM-5.3 Flash, MiniMax M3, GLM-5.3 model and Kimi K3.

The worked example below is an illustrative assumption, not a measurement: 80% input tokens and 20% output tokens, with no caching. Cost per 1M tokens = 0.80 × input price + 0.20 × output price. For GLM-5.3-Flash that is 0.80 × $0.20 + 0.20 × $0.50 = $0.26. The figures are this article's own arithmetic on the list prices above.

Compare the monthly figure with your own current invoice at the same volume. Caching lowers it: repeated prompt starts still in GPU memory are billed at the cached input price. Latency is something to measure on your own requests. Our public status page shows live status and per-model latency.

Open model licences, data privacy and lock-in

An open model's licence is named on its model page, and it has to be read before commercial use. On lyceum.technology (read 9 October 2026) the four models above list these licences:

• GLM-5.3-Flash: MIT
• MiniMax-M3: MiniMax Community License
• GLM-5.3: GLM-5.3 License
• Kimi-K3: Kimi K3 License

MIT and Apache 2.0 are on the Open Source Initiative's list of approved licences. A vendor's own licence has to be read before commercial use.

Data privacy depends on the host's terms, for open and closed models alike. Under Article 28(3) of the GDPR (Regulation (EU) 2016/679), a processor processes personal data only on documented instructions from the controller. The European Data Protection Board's Guidelines 07/2020 explain the controller and processor roles. Under Article 28(10), a processor that determines the purposes and means of processing itself is considered a controller for that processing. For any hosted API, read the provider's data-use and retention terms.

With us, the data question has a short answer, because we process prompts and outputs without storing them and never use them for training (trust page, read 9 October 2026). Each request leaves only a usage record: organisation, user, model, token counts and time. The Data Processing Agreement (DPA) is Annex 1 of the terms, with the customer as controller and us as processor. Serverless models run in Paris and Finland. Our trust page shows our request retention policy.

Lock-in is lower with open weights because the same model can run on another host or on dedicated GPUs. Our API keeps that lock-in low, because it is OpenAI-compatible: swap one endpoint to move in, keep your code to move out. Our fine-tuning is in early access and trains a LoRA adapter on GLM-5.3 Flash or Qwen3.8 27B. The fine-tuning page says: "The adapter it produces is yours: a small file of LoRA weights you can download and run elsewhere."

How to run open and proprietary models side by side

One OpenAI-compatible client can call both an open model and a proprietary LLM API, so a move does not have to be all at once. Switching a request means changing the base URL, API key and model ID. Use the base URL shown in your Lyceum dashboard:

import os
from openai import OpenAI

# Point standard OpenAI client at Lyceum European serverless endpoint
client = OpenAI(
    api_key=os.environ.get("LYCEUM_API_KEY"),
    base_url="https://api.lyceum.technology/openai/v1",
)

response = client.chat.completions.create(
    model="z-ai/glm-5.3-flash",
    messages=[
        {"role": "system", "content": "You are a concise production assistant."},
        {"role": "user", "content": "Parse the incoming telemetry payload into JSON."}
    ],
    temperature=0.2,
)

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

We also have an Anthropic-compatible endpoint for Claude Code, set with the endpoint, authentication and model configured:

export ANTHROPIC_BASE_URL="https://api.lyceum.technology/anthropic"
export ANTHROPIC_AUTH_TOKEN="$LYCEUM_API_KEY"
export ANTHROPIC_MODEL="z-ai/glm-5.3"
unset ANTHROPIC_API_KEY

The client code stays the same, but behaviour differs between models: system-prompt sensitivity, reasoning output and tool-calling details vary. Three guides cover the move: switching the OpenAI SDK to an open-model endpoint, what breaks when switching models and what switching inference providers costs.

When to keep a workload on a proprietary LLM API

Keep a proprietary API when it gives the best overall result for that workload, including quality, latency, reliability, support, cost and contract requirements. Decide task by task, and re-test the kept tasks when a new open-weight model is released.

When to switch from a proprietary API to open-weight models

Switch a workload from a proprietary API to an open-weight model when the model passes your evaluation set and the licence, data terms and cost all fit. High-volume tasks such as document extraction, data transformation or classification are the first candidates to test, because that is where a per-token price difference adds up. A workload is ready to move when:

• Quality: the open-weight model passes your own evaluation set for this task.
• Licence: you have read the model's licence and it covers your commercial use.
• Data: the host's data-use and retention terms, processing location and DPA meet your requirements.
• Cost: the worked example at your volume is below your current invoice.
• Rollback: you can point requests back to the previous endpoint by changing the base URL, API key and model ID.

Because both sides use the OpenAI-compatible API format, the client changes can be small, but a production switch still requires evaluation and integration checks. You can run that test on Lyceum Serverless Inference: open models through one OpenAI-compatible API, billed per token, with no base fee. Benchmark an open model on your own workload: change the base URL, API key and model ID, keep your code.