Open Models als Alternative zu OpenAI und Anthropic: Preise, Benchmarks, Setup

An open model is a model whose weights you can download. DeepSeek, Alibaba's Qwen, Moonshot's Kimi, Z.ai's GLM and Google's Gemma publish their weights. OpenAI published gpt-oss in 2025. You can run these models on your own servers or rent them from a hosting company.

For years, the difference between open and closed models was simple: closed frontier models scored higher on benchmarks, and open models were cheaper. But now the benchmark score difference is very small. Artificial Analysis runs every major model through the same tests. When Kimi K3 came out, it measured the difference between the best closed model and the best open model at 4 points on its index. Mozilla's State of Open Source AI report, published in July 2026, puts the difference at about 3%.

Companies now use open models in production. In Mozilla's survey, 79% of developers use open models, and 51% have put them in production. In October 2025, Airbnb's chief executive said the company's customer service agent relies a lot on Qwen: "It's very good. It's also fast and cheap."

Say you sell software to small accounting firms and want to add an assistant. It has five jobs:

Jobs 1 and 2 run thousands of times a day. Their answers are short and easy to check. Jobs 3 to 5 need a model that writes well and follows instructions. None of the five needs the most expensive model on the market. The table gives one model for each job, on a closed (frontier) API and on open models.

The table shows list prices per million tokens on 11 October 2026.

Sources: OpenAI pricing, Anthropic pricing, Keln model list. Those are standard rates for prompts of normal length (they go higher with longer prompts) on 11 October 2026.

The task: summarize a customer email thread of about 2,000 tokens into a 200-token summary.

Each model got the same email thread, word for word, and the same email summary request.

Take job 5, the support chat. It handles 5,000 requests a day. Each request sends about 2,000 tokens: the customer's message, the last few turns and one help article. The model returns about 300 tokens. In a month, that is 300 million input tokens and 45 million output tokens.

At this volume, only for this task, the switch saves a few hundred to a few thousand dollars a month. The savings grow with traffic.

You run open models on GPUs you own or rent.

Pros: your data stays on machines you fully control.

Cons: you pay for the GPUs whether you use them or not, and you handle updates and uptime. Worth it at high volume or under strict data rules.

A hosting company runs the model on GPUs reserved for you and gives you a URL. You pay per GPU hour.

Pros: only your traffic runs on your GPUs, so speed is steady, and the host company does the serving work.

Cons: you still pay for idle time, so it needs enough traffic to keep the GPUs busy.

A hosting company runs the model for many customers and charges per token. You sign up, get a key, change two lines of code.

Pros: cheapest way to start, no contract, nothing to pay when traffic is quiet.

Cons: you depend on one company, and demand for open models is high, so hosts often go down or slow at peak. Some serve a compressed copy of the model to cope, and do not tell you. You should also read the privacy page before starting: not every provider promises not to store or train on your data.

One endpoint in front of several shared providers. It picks the provider for each request: the cheapest or fastest.

Pros: it keeps working when one provider goes down or slows, and you can compare lots of models.

Cons: a gateway with 100+ models can't check each one at each provider closely, so quality and reliability can vary.

There is a newer kind of company: a reliability layer for open models. Their goal is to serve open models with the reliability of a frontier lab's API.

Keln also has zero data retention at every provider and doesn't bill for a failed request. It accepts both the OpenAI and the Anthropic request format, so you switch by changing two lines. You can also run open models in Claude Desktop ( setup guide ) and Codex ( setup guide ) through Keln.

The result is open models at up to an eighth of the frontier price, a few points behind on benchmarks, and reliability built to match OpenAI's or Anthropic's.

Most open-model providers accept OpenAI's request format. To start using open models you change three settings: the provider's address, the access key and the model name. The example below uses Keln; other providers work the same way with their own address.

Swap the provider's address and access key:

client = OpenAI ( base_url = " https://api.keln.ai/v1 ", api_key = os. environ [ " KELN_API_KEY " ], ) Add a model name:

response = client. chat. completions. create ( model = " zai-org/glm-5.3 ", messages = [{ " role ": " user ", " content ": " Sort this email: question, bug, billing or spam? " }], ) Run the product's usual requests on the open model and compare the answers with what you get today.

Move a small share of real traffic first, then the rest.

👋 Hey, I'm Kris Holysheva. I'm a co-founder at Keln, a reliability layer for open models, and I write about how teams can cut their AI bill without losing frontier-lab quality and support. Reach out with any questions.