---
title: "DreamPrompting free inference"
description: "A recurring no-cost API allowance covering 7 cataloged models."
canonical_url: "https://freeinferencing.com/provider/dreamprompting/"
md_url: "https://freeinferencing.com/provider/dreamprompting.md"
last_updated: "2026-09-19"
---

# DreamPrompting

> A recurring no-cost API allowance covering 7 cataloged models.

## Classification

- **Directory:** Current
- **Free access:** Always-free quota
- **Status:** Community service
- **Confidence:** Medium High
- **Payment card:** No
- **Account:** Required
- **Equivalent paid value:** Up to $152.19/mo
- **API endpoint:** `https://dreamprompting.com/api/v1`

## Models mentioned

- `google/gemini-3.1-flash-lite`
- `groq/llama-3.3-70b-versatile`
- `nvidia/meta/llama-3.3-70b-instruct`
- `openrouter/google/gemma-4-31b-it:free`
- `mistral/mistral-small-latest`
- `cohere/command-a-03-2025`
- `chat/ch.at`

## Limits and terms

```yaml
requests_per_minute_per_ip: 100
tokens_per_rolling_24_hours: 500000
requests_per_rolling_24_hours: 5000
max_input_tokens: 32000
max_output_tokens: 8192
```

## What happens to your prompts?

**Not private.** Prompts can be stored or publicly shared and user content may be used to improve the service without a gateway-specific no-training promise.

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: partial
  plan_scope: DreamPrompting website, public prompt-sharing platform, and free
    multi-provider API gateway.
  prompt_retention: The privacy policy describes submitted prompts as stored and,
    for sharing features, publicly visible; it does not state a separate
    API-gateway prompt TTL.
  response_retention: not_documented
  ordinary_logging: IP, device, page, account, and usage information is collected;
    API content logging is not specifically disclosed.
  model_training: No service-wide no-training commitment was found for API inputs or outputs.
  product_improvement: Collected information and user content may be used to
    provide and improve the service.
  human_or_operator_access: Stored/shared prompt content is accessible to the
    service and can be public; gateway upstreams process API requests.
  subprocessors_and_routing: The gateway advertises routing across multiple
    providers with automatic failover, so the selected upstream's policy also
    applies.
  deletion_controls: Users can remove submitted public prompts and request account
    deletion; no upstream API-request deletion control is documented.
  caveat: The legal pages focus heavily on the public prompt-sharing product and
    do not cleanly distinguish gateway traffic. Do not assume public-prompt
    deletion covers routed API content.
agreements:
  terms_of_service: https://dreamprompting.com/terms
  privacy_policy: https://dreamprompting.com/privacy
```

## Eligibility

```yaml
account_required: true
payment_method_required: false
```

## API compatibility and modalities

- **Compatibility:** OpenAI Chat Completions
- **Modalities:** Text generation


## Before you build with DreamPrompting

### 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, Medium High confidence, No payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 7 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 can be stored or publicly shared and user content may be used to improve the service without a gateway-specific no-training promise. 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 7 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

- [DreamPrompting](https://dreamprompting.com/?lang=en): Official Product
- [API docs](https://dreamprompting.com/api-docs): Official Docs
- [Models](https://dreamprompting.com/models): Official Catalog
- [Terms](https://dreamprompting.com/terms): Official Terms
- [DreamPrompting Privacy Policy](https://dreamprompting.com/privacy): Official Privacy
- [Cohere pricing](https://cohere.com/pricing): Official Pricing
- [OpenRouter cohere/command-a-03-2025 (Cohere-served)](https://openrouter.ai/cohere/command-a-03-2025): Router Pricing

## Sitemap

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