---
title: "FreeInference.org free inference"
description: "A recurring no-cost API allowance covering 8 cataloged models."
canonical_url: "https://freeinferencing.com/provider/freeinference_org/"
md_url: "https://freeinferencing.com/provider/freeinference_org.md"
last_updated: "2026-09-19"
---

# FreeInference.org

> A recurring no-cost API allowance covering 8 cataloged models.

## Classification

- **Directory:** Current
- **Free access:** Always-free quota
- **Status:** Available now
- **Confidence:** High
- **Payment card:** No
- **Account:** Required
- **Equivalent paid value:** Not quantifiable
- **API endpoint:** `https://freeinference.org/v1`

## Models mentioned

- `glm-5.1`
- `minimax-m2.5`
- `minimax-m3`
- `qwen3.6-35b`
- `diffusiongemma`
- `deepseek-v4-flash`
- `glm-5.3-flash`
- `bge-m3`

## Limits and terms

```yaml
public_numeric_limits: false
official_text: Generous quota
```

## What happens to your prompts?

**Not private.** Prompts and responses may be logged and reused for service research, improvement, and published derived datasets.

Privacy is audited separately from price. Review the current governing terms before sending sensitive or regulated data.

## Data governance and agreements

```yaml
data_governance:
  review_status: reviewed
  plan_scope: FreeInference.org service, including local inference servers and
    requests routed to remote model providers.
  prompt_retention: All prompts and responses may be logged, stored, hashed,
    redacted, or otherwise processed according to operator configuration and
    service needs; no fixed maximum retention period is stated.
  response_retention: Same broad logging and retention policy as prompts.
  ordinary_logging: Requests are analyzed for operations, security, debugging,
    research, service improvement, usage statistics, and routing metrics.
  model_training: The terms authorize research and derived-data use but do not
    clearly state whether retained prompt or response content may train a model.
    Users should not treat this as a no-training commitment.
  product_improvement: Logs and derived data may be used to improve and analyze
    the service. Sanitized or anonymized prompts, responses, metrics, and other
    derived datasets may be published or open-sourced for reproducible research.
  human_or_operator_access: Operators may process retained content for research,
    security, debugging, improvement, and analysis; sanitization is performed
    where feasible but is not guaranteed to remove sensitive information.
  subprocessors_and_routing: Requests may be sent to remote model providers, which
    can process prompts, responses, metadata, and usage information under their
    own policies.
  deletion_controls: not_documented
  caveat: The terms explicitly warn users not to submit sensitive, confidential,
    regulated, secret, credential, or unauthorized data. Sanitization is not a
    privacy guarantee.
privacy_caveat: Prompts and responses are logged; anonymized derivatives may be open-sourced.
agreements:
  terms_of_service: https://freeinference.org/terms
```

## Eligibility

```yaml
account_required: true
payment_method_required: false
intended_use: research_and_education
```

## API compatibility and modalities

- **Compatibility:** OpenAI Compatible, Anthropic Compatible
- **Modalities:** Model inference


## Before you build with FreeInference.org

### Read the classification narrowly

This record describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every account and region. “Always-free quota” should be interpreted together with the Current directory placement, High confidence, No payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 8 model IDs. Match the exact ID against the provider's current catalog before using it in code because zero-price routes, aliases, context limits, and feature support can rotate while an older documentation page remains online.

### Match the quota to the workload shape

Translate the allowance into peak requests per minute, input and output tokens, concurrency, retries, and every daily or monthly ceiling that applies to the intended account. The first limit reached by the workload is the practical ceiling. A large token pool can still fail interactive bursts, and a high request limit can still fail long-context work. Include tool calls and retry traffic, test the largest realistic payload, and treat research, experimental, and community access as conditional on their eligibility and fair-use terms.

### Preserve billing and privacy boundaries

The recorded payment-card requirement is No, and account access is required. Confirm both in the live signup flow, set provider-side budgets when charges are possible, and observe the balance or usage fields after a complete request. The prompt-handling classification is **Not private** because Prompts and responses may be logged and reused for service research, improvement, and published derived datasets. Re-read the terms for the exact route and plan before sending sensitive, regulated, or proprietary content.

### Follow the evidence, then re-check it

This record links 3 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest and most specific governing document or live catalog when sources disagree. A dated finding can become stale even when the page remains online, so retain the source and snapshot that supported the decision and submit a correction when a provider changes a material term.

### Plan fallback without policy drift

A fallback should preserve modality, context length, streaming, structured output, tools, safety controls, and data terms, not only API syntax. Decide which errors may retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. Rotating aliases and free pools can change behavior without changing the endpoint, so retain the response model field and test at least one substitute route before the primary offer becomes unavailable.

### Monitor the offer as a dependency

Capture the model ID, response model, rate-limit headers, usage fields, latency, HTTP status, and provider request identifier. Watch authorization failure, quota exhaustion, catalog removal, policy revision, and balance movement as separate failure modes. Re-check the live catalog and governing sources on a schedule proportionate to the workload's importance, and keep an owner and exit path for any production dependency on volatile free capacity.

### Test one complete request before scaling

Start with the smallest permitted request using the exact credential, model ID, endpoint, region, and account type intended for deployment. Record the status, headers, usage fields, response model, latency, and dashboard balance movement. Then exercise an invalid model, quota exhaustion, or rate limit so failure is explicit and cannot silently switch to a paid route. Validate streaming, structured output, and tool calls separately because a free model can expose fewer features than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and do not send sensitive data until the observed route matches the reviewed agreement.

## Primary sources

- [FreeInference home](https://freeinference.org/): Official Product
- [Models](https://doc.freeinference.org/models): Official Docs
- [FreeInference.org Terms of Service](https://freeinference.org/terms): Official Terms

## Sitemap

See the full [semantic sitemap](/sitemap.md) for every page and markdown mirror.