Included models
The two models every OpenLLM account can use without a provider, Qwen 3.7 Plus and DeepSeek V4.1 Flash, with their monthly allowances and how usage counts.
Every account can use two hosted models without connecting a provider. OpenLLM pays the upstream provider, up to a monthly allowance that depends on your plan.
| Model id | Name | Served by | Context window |
|---|---|---|---|
openllm/qwen/qwen3.7-plus | Qwen 3.7 Plus | Alibaba DashScope | 250,000 input tokens |
openllm/deepseek/deepseek-v4.1-flash | DeepSeek V4.1 Flash | OpenRouter | 250,000 input tokens |
Both support chat, tool calls, streaming, reasoning, and image input. They run on the hosted gateway. A request sent to the daemon on your device is forwarded to the gateway for these models.
Monthly allowance
Allowances are counted in units, explained in the next section.
| Model | Free | Trial | Pro |
|---|---|---|---|
openllm/deepseek/deepseek-v4.1-flash | 2,000,000 | 4,000,000 | 20,000,000 |
openllm/qwen/qwen3.7-plus | 666,667 | 1,333,333 | 6,666,667 |
Each model has its own allowance. Both reset on the 1st of each month at 00:00 UTC. For what each plan includes, see Plans and limits.
How usage is counted
One unit is one fresh input token. Other tokens are weighted by their list price relative to input:
| Token type | deepseek-v4.1-flash | qwen3.7-plus |
|---|---|---|
| Fresh input, including cache writes | 1 unit | 1 unit |
| Cached input (cache reads) | 0.02 units | 1 unit |
| Output | 4 units | 4 units |
For example, a DeepSeek request with 10,000 fresh input tokens, 50,000 cached input tokens, and 2,000 output tokens uses 10,000 + (50,000 × 0.02) + (2,000 × 4) = 19,000 units.
Only successful requests count. Usage on the included models never counts toward the Free plan's monthly token cap. If a Free account hits that cap on other models, though, every request is blocked until it resets, including requests to the included models.
Context window
OpenLLM treats both models as having a 250,000-token input window, even though the upstream models accept more. Under Hop to larger context, the default, a request that's clearly larger than that skips these models when another model comes after them in the chain. See Context overflow.
When the allowance is spent
Before each request, OpenLLM removes included models whose allowance is spent. Routing falls through to the next model in the chain, as if the included model weren't there.
If no other model is left, for example when you call openllm/qwen/qwen3.7-plus directly, the
request fails with 403. The gateway currently reports this with the type
subscription_requires_daemon and a message about subscription providers. See
Errors.
To keep working after the allowance runs out, add a model from one of your own providers to the chain. See Fallback chains.
Where they appear in your chains
On a new account, the built-in chains start with the included models:
ultraandplusstart withopenllm/qwen/qwen3.7-plus, thenopenllm/deepseek/deepseek-v4.1-flash.litestarts withopenllm/deepseek/deepseek-v4.1-flash.
Models from providers you connect follow them. Once you edit a chain, it's yours and OpenLLM doesn't reorder it. See Aliases.
See what's left
The Plan tile in the app's sidebar shows Included models: one bar for both models, and a line per model with the units left and the monthly limit. A model's numbers turn red when its allowance is used up.
Who serves them
- Qwen 3.7 Plus runs on Alibaba DashScope, on OpenLLM's DashScope account.
- DeepSeek V4.1 Flash runs through OpenRouter, on OpenLLM's OpenRouter account. OpenLLM limits OpenRouter to endpoints priced at or below DeepSeek's list price.
Your prompts and responses pass through OpenLLM's servers to these providers. For what is stored and for how long, see Privacy.
Related
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Add a custom endpoint
Connect a public OpenAI- or Anthropic-compatible endpoint, such as OpenRouter or your own gateway, and choose which of its models OpenLLM can route to.
Routing and fallback
How OpenLLM turns aliases like ultra into fallback chains, which rate limits (429) and errors move a request to the next model, and how to pin a model.