Infron free models offer a way to compare selected Qwen and DeepSeek endpoints without per-token charges. However, the offer has an important access condition: the published standard-free rules require an account balance of at least $5. Free usage does not consume that balance.
This distinction matters if you are planning an API experiment. A zero token price does not necessarily mean you can start with no deposit, make unlimited requests, or rely on the endpoint for production availability. Here is what the official listings show and how to turn the offer into a useful model comparison.
Verified September 26, 2026 using Infron’s public model pages and documentation. We have not created an account, deposited funds, or made authenticated API requests. Prices and access rules should be rechecked before use.
Which Infron free models are currently listed?
The following five endpoints have published zero-dollar input and output prices. Links go to the individual Infron listings. Model IDs are more useful than abbreviated names when configuring an application.
| Marketplace label | Exact model ID | Input / output per million tokens |
|---|---|---|
| Qwen3.8 Flash (Free) | qwen/qwen3.8-flash:free | $0 / $0 |
| Qwen3.8 27B Uncensored (Free) | qwen/qwen3.8-27b:free | $0 / $0 |
| Qwen3.8 27B Abliterated Cyber (Free) | qwen/qwen3.8-27b-abliterated-cyber:free | $0 / $0 |
| DeepSeek V4.1 Flash (Free) | deepseek/deepseek-v4.1-flash:free | $0 / $0 |
| DeepSeek V4 Flash 0731 (Free) | deepseek/deepseek-v4-flash-0731:free | $0 / $0 |
The two Qwen 27B entries are distinct listings. At review time, the plain qwen3.8-27b:free page was titled “Uncensored,” while the other explicitly included “Abliterated Cyber.” We have not independently verified the underlying modifications. Those names do not mean unlimited requests or an exemption from platform terms.
The Qwen 27B provider tables explicitly show $0 cache-read pricing. The Flash pages reviewed did not all display a separate cache-read field. We therefore confirm their listed input and output prices without inventing a cache-specific price entry for every endpoint.
Infron free models: balance requirements and daily limits
According to Infron’s free-model documentation, standard free endpoints require a balance of at least $5 and share an allowance of 1,000 requests per account per day. The counter resets at 00:00 UTC; other account rate limits still apply. Free requests do not deduct credits. The lineup can change, and the shared free pool has no service-level agreement.
That page also retains a separate promotion with an end date of July 28, 2026. Since that date precedes this review, we do not treat the promotion as currently active.
Separately, Infron’s pricing page lists a pay-as-you-go transaction fee of 5% plus $0.35. Review the checkout total and credited amount before funding an account. This fee is separate from the free endpoints’ token price.
How to start testing Infron free models
Infron’s quickstart describes signing in, creating an API key in the dashboard, setting up billing, and sending a request. Its documented chat-completions endpoint is https://llm.onerouter.pro/v1/chat/completions. Use the current official example for authentication and request formatting.
Copy one exact model ID from the table and begin with a short, non-sensitive prompt. Keep the key in your application’s secret configuration rather than embedding it in a public website or source repository. After the first successful call, inspect the response and usage record before running a larger comparison.
For a cost-controlled experiment, use an explicit list of allowed model IDs. If an endpoint fails, record the failure before changing models. Review any client-side fallback configuration so that a failed free request does not silently become a paid experiment. This is a suggested application design, not a claim about Infron’s default behavior.
A practical comparison with answers you can check
The following original mini-test uses fictional information and objective checks. It is designed to expose differences in extraction, instruction-following, and basic coding without relying on a vague impression of which answer sounds smarter.
| Task | Test prompt | Pass criterion |
|---|---|---|
| Structured extraction | Return only JSON with keys item, quantity, and unit_price. Text: “Order three blue notebooks at 4 dollars each.” | Valid JSON; item identifies blue notebooks; quantity is 3; unit_price is 4. |
| Respecting missing information | Use only this note: “The workshop starts at 10:00 and ends at 12:00. Room assignment is pending.” Which room hosts the workshop? | States that the room is not yet specified, without inventing one. |
| Small coding task | Write a Python function that returns unique values from a list in first-seen order. Show its result for [3, 1, 3, 2, 1]. | Returns [3, 1, 2], handles an empty list, and preserves order. |
Run the same tasks on each candidate. Save the exact prompt, model ID, output, elapsed time, and result. Judge structured responses with a parser where possible and execute generated code only in an appropriate test environment. A plausible explanation is not a substitute for the expected result.
Repeat a small number of trials before drawing a conclusion. One slow response might reflect temporary service conditions. One correct output does not establish reliability. Compare success rates and the amount of repair needed, rather than selecting the most confident-sounding response.
Testing Infron free models for agents
An agent adds more failure modes than a chat interface. It may need to choose a tool, supply valid arguments, use the returned information, and stop when the task is complete. Build a mock workflow before giving it access to real systems.
For example, provide a fictional inventory tool that returns a fixed stock count. Ask the model whether an order can be fulfilled, and check whether its answer matches the tool result. Then return an unavailable-item response and see whether the model reports the problem clearly instead of pretending the order succeeded.
- Tool choice: did it request the information needed for the task?
- Arguments: did the proposed call match the expected schema?
- Grounding: did the final answer agree with the returned data?
- Stopping: did it finish without unnecessary repeated calls?
- Failure handling: did it distinguish an unavailable service from a valid empty result?
These tests are recommendations for your evaluation process; we have not run them against the listed endpoints. For a broader scoring method, see our AI tool testing checklist.
Check provider details before choosing a model
The reviewed Qwen3.8 Flash and DeepSeek V4.1 Flash pages list a CN-region provider, while the two Qwen 27B pages list US-region providers. Their tables report zero retention and no training use. These are the platform’s published statements, not an independent privacy audit; check the selected provider’s current policy before sending sensitive material.
Use synthetic data for the first comparison. This keeps the test easy to repeat and lets you focus on answer quality without mixing in confidential source material. If the eventual application has specific data-handling requirements, evaluate those separately from token price.
When this offer is useful
Infron free models can be useful for learning an API workflow, comparing prompt designs, and prototyping with a fixed test set. The strongest result is a record showing which endpoint handles your tasks reliably—not simply a list of zero-dollar prices.
For an alternative that runs on your own hardware, our Qwen-Image-2.1 local guide covers image generation. It addresses a different workload, but illustrates the same principle: evaluate access conditions and operating effort alongside the headline price.
Sources and verification
Individual model links in the comparison table support the endpoint names and prices. Access conditions come from Free Models, transaction charges from Pricing and Fee Structure, and setup details from the official quickstart. The test prompts and review method are original editorial suggestions. No authenticated billing or performance test was performed.