Tryingopen Alternative: The Same Open Models, via API

Tryingopen is one of the nicest small products released this year, and this post is not going to pretend otherwise. You open it, a text box is already focused, you pick a model, you type, and an open-weight model answers. No account, no key, no onboarding, nothing saved. If you want to know what Kimi or DeepSeek or Qwen sounds like before you commit an afternoon to it, that is the shortest path there is.

The same team, Offline Base, runs a second open-models page with a stricter promise: the models there are routed only to providers with zero data retention. That page sits inside a local-first product line, where hardware you own answers first and an open model in the cloud picks up whatever outgrows it.

People land on DreamPrompting looking for an alternative to these for one specific reason, and it is almost never dissatisfaction. It is that a chat box cannot be called from code. This post is about where that wall is, what sits on the other side of it, and the two places where you should close this tab and go back to Tryingopen instead.

What Tryingopen and Offline Base actually are

At the time of writing, Tryingopen offers 15 open models from Alibaba, NVIDIA, Moonshot, DeepSeek, Google, Mistral, Meta, MiniMax, Thinking Machines and Z.ai. The interface is a single composer with a model picker, a thinking-effort control, and attachments for images and PDFs. Any conversation can be shared as a link so someone else can run the same thing. The Offline Base page is narrower and stricter: around 10 open-weight models, and the routing constraint is the product rather than a footnote.

Both are worth respecting for what they refuse to do. There is no signup wall, no email capture, no trial countdown, and no history to manage. Offline Base is also unusually straight about the edges of its own promise: it states plainly that when a model decides a question needs the web, that question goes to a search provider that sits outside the no-retention guarantee. Disclosing the hole in your own claim is rarer than it should be.

Where the wall is

A browser chat box is an evaluation tool. It answers one question extremely well: is this model any good at my kind of task. It cannot answer the question that comes immediately afterwards, which is how do I get this into the thing I am building.

You hit the wall the moment you want to run the same prompt over 400 rows of a spreadsheet, put a model behind a Discord bot, add a summarise button to your own app, wire a model into an n8n workflow, or let a coding agent call one on your behalf. None of that is a chat box's job, and neither product claims it is.

The usual next step is to go and get a key from a provider directly. That works, and then you discover the second problem: one provider's free tier is one provider's bad afternoon. You get a 429 during a demo, or a model is retired, or the free variant you pinned quietly changes its name.

What DreamPrompting is

DreamPrompting is a routing layer with an OpenAI-compatible endpoint. We hold accounts across roughly 24 upstream providers that publish a free tier, including Groq, Google AI Studio, NVIDIA NIM, Cerebras, Mistral, Cohere, SambaNova and OpenRouter. You get one key. When you send a request, it goes to a provider that can serve it, and when that provider is rate limited or erroring, the router moves to the next candidate without your code noticing.

The model families overlap heavily with what those chat boxes serve, because everyone is drawing from the same open-weight pool: Llama, Gemini, DeepSeek, Qwen, Mistral, Cohere Command and GPT-OSS. The difference is the shape of the thing. Theirs is a page. Ours is a base URL.

from openai import OpenAI

client = OpenAI(
    base_url="https://dreamprompting.com/api/v1",
    api_key="YOUR_API_KEY",
)

r = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Hello"}],
)
print(r.choices[0].message.content)

Because the endpoint speaks the OpenAI Chat Completions format, most projects need one line changed rather than a new SDK. Streaming, tool calling and image input all come along. The full documentation is here.

The honest comparison

The row that matters most

That last row is the one to read twice, and we would rather write it here than have you find it in a privacy policy later.

Tryingopen saves nothing. Offline Base goes further and constrains which providers a request may touch at all. DreamPrompting does neither. We store each API call: the messages you sent, the reply that came back, which provider served it, how long it took, your IP and user agent, and a re-encoded thumbnail of any image you attached. That is how the failover logic gets debugged and how abuse gets caught, and it is a real cost you are paying in exchange for the API.

So the rule is simple. If the content of your prompt is the sensitive part, a no-retention chat box is the better tool and you should use one. If what you need is programmatic access on a free budget, and your prompts are ordinary application traffic, the trade is usually worth it. Read our privacy policy before you decide, not after.

When you should use Tryingopen instead

Two cases, and they are common ones.

The first is evaluation. If the question in your head is "is Kimi actually better than Qwen at this", opening a chat box and pasting the same prompt into both is faster than anything involving a key, and the shareable link means you can put the result in front of a colleague without either of you signing up. We are not going to invent a reason for you to create an account first.

The second is anything you would not want logged. See the section above. That is not a hedge, it is the honest answer.

When you should use us

When the model has to run inside something. A script, a bot, a workflow, an agent, a button in your own product. When you want a request to survive one provider having a bad minute, which is what the failover is for. When you want to pin an exact model and be told with a 503 if it is unavailable rather than quietly served a different one, which is what "allow_fallback": false is for. And when the budget is zero and needs to stay zero.

There is a version of this that costs you nothing to test. Sign in with Google, copy the key, change the base URL in code you have already written, and send one request. If it does not fit, you have lost about a minute.

Both things can be good

Comparison posts usually end with a winner. This one does not, because these are not the same product and the honest answer is that most people building something will end up using both: a chat box to decide which model to use, and an API to actually use it.

If you got here from Tryingopen, go and finish your evaluation there. It is better at that than we are. Come back when you know which model you want and need it to run somewhere other than a browser tab.

Model counts, provider lists and feature details for Tryingopen and Offline Base were checked on 2026-08-18 and may have changed since. Both are independent products with no affiliation to DreamPrompting.