---
title: "Arcee Open Models API Beta free inference"
description: "$5 for new accounts; it does not recur. See verified limits, model IDs, privacy terms, and primary sources for Arcee Open Models API Beta."
canonical_url: "https://freeinferencing.com/provider/arcee_open_models_api_beta/"
md_url: "https://freeinferencing.com/provider/arcee_open_models_api_beta.md"
last_updated: "2026-09-19"
---

# Arcee Open Models API Beta

> $5 for new accounts; it does not recur.

## Classification

- **Directory:** Current
- **Free access:** One-time trial
- **Status:** Current trial
- **Confidence:** High
- **Payment card:** Yes
- **Account:** Required
- **Equivalent paid value:** $5.00 once
- **API endpoint:** `https://api.arcee.ai/api/v1`

## Models mentioned

- `deepseek/deepseek-v4-flash-latest`
- `trinity-large-thinking`
- `thinkingmachines/inkling-small`
- `deepseek/deepseek-v4-pro`
- `zai-org/glm-5.2`
- `moonshotai/kimi-k3`

## Limits and terms

```yaml
signup_credit_usd: 5
recurrence: false
expiry: not_publicly_documented
scope: shared_across_beta_catalog
```

## What happens to your prompts?

**Not private.** Standard terms allow storage and processing of inputs/outputs, broad aggregate/research use, and model development or training on content that is neither personal nor confidential; third-party hosts may independently process content.

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: Self-service Open Models API beta; third-party model-host terms may
    additionally apply.
  prompt_retention: No fixed TTL is published; terms authorize storage, hosting,
    and processing of inputs and outputs.
  response_retention: No fixed TTL is published; outputs receive the same content-processing grant.
  ordinary_logging: Request-content logging scope and retention are not specifically bounded.
  model_training: Arcee receives a perpetual right to use content that is neither
    personal information nor confidential for model development and training.
  product_improvement: Aggregate metrics may be used perpetually for analytics,
    research, benchmarking, marketing, and service/model improvement.
  human_or_operator_access: Operator access is not bounded to narrow purposes in the public terms.
  subprocessors_and_routing: Third-party model hosts may access, use, and store
    inputs under their own terms.
  deletion_controls: General privacy rights exist, but no inference-specific
    deletion SLA is published.
agreements:
  terms_of_service: https://www.arcee.ai/terms-and-conditions
  privacy_policy: https://www.arcee.ai/privacy-policy
```

## Eligibility

```yaml
account_required: true
payment_method_required: true
signup_open_to_anyone: true
```

## API compatibility and modalities

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


## Before you build with Arcee Open Models API Beta

### 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. “One-time trial” should be interpreted together with the Current directory placement, High confidence, Yes payment-card status, and the plan-specific sources below.

### Resolve the live model route

The snapshot records 6 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 Yes, 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 Standard terms allow storage and processing of inputs/outputs, broad aggregate/research use, and model development or training on content that is neither personal nor confidential; third-party hosts may independently process content. 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 5 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

- [Open Models API beta](https://www.arcee.ai/blog/open-models-api-beta): Official Announcement
- [First API call](https://docs.arcee.ai/api-reference/your-first-api-call): Official API Docs
- [Terms and Conditions](https://www.arcee.ai/terms-and-conditions): Official Terms
- [Privacy Policy](https://www.arcee.ai/privacy-policy): Official Privacy
- [Arcee Python SDK](https://github.com/arcee-ai/arcee-python): Official Repository

## Sitemap

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