Licensing, procurement and legal review
The licence is the reason this is possible, so we publish it
Every model here is served under an open licence that permits somebody other than the publisher to host the weights. That is not a compliance footnote — it is the mechanism the entire price difference rests on, which is why the licence is named on every model page rather than summarised in a policy.
24 models catalogued, each with a named licence; 7 callable today, and the pricing table says which is which.
- No platform fee
- OpenAI SDK compatible
- Pay as you go
- 7 models callable today
The question legal actually asks
The blocking question in a model procurement review is rarely about price and almost never about benchmarks. It is: under what right is this vendor serving these weights to us, and what happens to our data once it reaches them. Both have documentary answers, and a vendor who cannot produce them quickly is a vendor whose review stalls for a quarter.
So the answers are published rather than available on request. Every model in the catalogue names its licence on its own page. The models we refuse to serve are listed too, with the reason for each refusal, because a list of what a vendor will not do is more informative to a reviewer than a paragraph asserting that the vendor is careful.
The commercial reason for this alignment is worth stating plainly: an open licence is what makes third-party hosting lawful, third-party hosting is what puts the cost base under our control, and control of the cost base is what produces the price difference. A vendor reselling closed-weight access at a discount would have neither the right nor the margin, which is why we do not do it.
The catalogue, with licences attached
Below is every model we can serve, with its publisher, its licence and its current availability. The licence column is the one a reviewer reads first; the availability column is the one an architect reads first.
Pricing is published on the same table as the licence rather than in a separate quote, and every rate sits 36–42% below what the model's own publisher charges. Nothing on this page is contingent on a procurement conversation.
| Model | Publisher | Licence | Modality | Callable today |
|---|---|---|---|---|
| Kimi K2.6 | Moonshot AI | Modified MIT | text | Yes |
| Kimi K3 | Moonshot AI | Modified MIT | text | Yes |
| GLM-5.2 | Z.ai | MIT | text | Yes |
| GLM-5.2 Air | Z.ai | MIT | text | Not yet served |
| DeepSeek V4 Pro | DeepSeek | MIT | text | Yes |
| DeepSeek V4 Flash | DeepSeek | MIT | text | Yes |
| Qwen3 Max Instruct | Alibaba | Apache 2.0 | text | Not yet served |
| Qwen3 235B A22B | Alibaba | Apache 2.0 | text | Not yet served |
| MiniMax M2 | MiniMax | MIT | text | Not yet served |
| Llama 4 Maverick | Meta | Llama 4 Community | text | Not yet served |
| Mistral Large 3 | Mistral AI | Apache 2.0 | text | Not yet served |
| GPT-OSS 120B | OpenAI | Apache 2.0 | text | Not yet served |
| Wan 2.2 T2V A14B | Alibaba | Apache 2.0 | video | Not yet served |
| Wan 2.2 I2V A14B | Alibaba | Apache 2.0 | video | Not yet served |
| LTX-2.5 | Lightricks | LTXV Open Weights | video | Not yet served |
| HunyuanVideo 1.5 | Tencent | Tencent Hunyuan Community | video | Not yet served |
| CogVideoX-5B | Zhipu AI | Apache 2.0 | video | Not yet served |
| Mochi 1 | Genmo | Apache 2.0 | video | Not yet served |
| FLUX.2 [schnell] | Black Forest Labs | Apache 2.0 | image | Not yet served |
| Qwen-Image | Alibaba | Apache 2.0 | image | Yes |
| Stable Diffusion 3.5 Large | Stability AI | Stability Community | image | Not yet served |
| HiDream-I1 | HiDream | MIT | image | Not yet served |
| Qwen3 Embedding 8B | Alibaba | Apache 2.0 | embedding | Yes |
| BGE-M3 | BAAI | MIT | embedding | Not yet served |
Availability is stated in the table rather than discovered at call time. A model marked unavailable has a published price and no upstream serving it yet; requesting one returns an error naming the model rather than a substitution.
The cost side of the business case
A licence review usually runs in parallel with a cost approval, and the second is easier to produce evidence for. The calculator prices your volume across every model we serve against the publisher's own rate.
The result is a shareable URL, which is the format a finance approver can check rather than take on trust. It is also the format that survives being pasted into a procurement ticket.
An agent or chat loop resends the same system prompt on every turn. That repeated prefix is billed at the cached rate, and on most models that is a fifth of the standard input rate. Set this to zero if every request you send is different.
That is $7.00 a month, or $84.00 a year, on the same tokens through the same model.
An estimate, not a quote — but it is computed from the same rate table the API bills from, so the only assumptions in it are the ones you set above. Third-party figures are those providers’ own published rates; where a provider does not publish one, the cell is blank rather than guessed.
What a review needs from us, in order
Three documents, all of them already published. None of them requires a call, an NDA or a sales conversation to obtain.
- 1
The licence for each model you intend to use
Named on the model's own page and in the licence reference, with the family explained. A reviewer needs the licence name and the permission it grants for third-party hosting and for commercial use of the outputs.
- 2
The data-handling schedule
What is stored, for how long, and what is never stored. Chat and completion traffic is relayed and not retained. Asynchronous job inputs and results are held for 30 days so they can be fetched, or deleted immediately on request. Usage metadata is retained because the invoice is built from it.
- 3
The refusal list
The models we do not serve and why. A reviewer assessing supply-chain risk learns more from a specific refusal than from a general assurance, and this is the list that shows the policy is applied rather than stated.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://router.xark.io/api/v1" # ← the only line that changes
)
response = client.chat.completions.create(
model="z-ai/glm-5.2",
messages=[{"role": "user", "content": "Hello"}]
)
Licence and privacy questions that are not answered on the published pages go to affiliate@xark.io, which is a monitored address rather than a role alias that bounces.
The models we refuse, and why
This list exists because it is the most useful thing a vendor can show a reviewer. Each of these is a model customers ask for, and each is one we will not serve.
- Sora 2 — OpenAI
- Closed weights — no resale license exists
- Veo 3.1 — Google DeepMind
- Closed weights — no resale license exists
- Runway Gen-4 — Runway
- Closed weights — no resale license exists
- Kling 2.5 — Kuaishou
- Closed weights — no resale license exists
- Wan 2.5 / 2.6 — Alibaba
- API-only — weights never released, despite the open Wan 2.1/2.2 lineage
What we do with your data
We do not train on prompts or completions, ever. Chat and completion traffic is relayed to the model and not stored by us at all. The inputs and results of asynchronous jobs — video and image generation, which cannot return inline — are held for 30 days from completion so you can fetch them, or deleted immediately on request.
Usage metadata, meaning the model, token counts, latency and cost, is retained for 24 months because the dashboard and your invoice are built from it. The privacy policy carries the authoritative schedule, and where this page and that page differ, that page is the one to cite in a review.
- Contracting entity
- Innoprise Incubator INC, named identically in the terms, the privacy policy and the compliance page so a reviewer is not reconciling three documents.
- Output rights
- Determined by the licence of the model that generated them, which is why the licence is on the model page rather than abstracted into a single site-wide statement. Licence families differ on this and the difference matters.
- No silent substitution
- A request for a specific model is served by that model or fails. Substituting a model would change the licence terms of the output you receive, which is a compliance event and not merely an engineering one.
Procurement and licensing questions
The questions a review actually blocks on, answered without hedging.
What licence is each model served under?
Can we use the outputs commercially?
Why do you not offer GPT, Claude or Gemini?
Which models do you explicitly refuse to serve?
Do you train on our prompts?
How long is our data retained?
Who is the contracting entity?
Where do the weights actually run?
Which models are actually callable today?
Do you substitute a different model if one fails?
How do we verify your pricing claim independently?
Who do we contact for a security or legal question?
Is there a volume agreement for a reviewed vendor relationship?
Send the licence list to whoever has to approve it
Everything a first-pass review needs is already published, which is the point. Leave an address and we will send the summarised version in a form you can forward without editing.
We guarantee at least 20% below the model publisher's own rate on every model we serve. The smallest discount in the catalogue today is 36%, so the guarantee has room in it by design.
Related: Licence reference · Compliance · Privacy policy