Arcee Open Models API Beta
$5 for new accounts; it does not recur.
https://api.arcee.ai/api/v1Models mentioned
deepseek/deepseek-v4-flash-latesttrinity-large-thinkingthinkingmachines/inkling-smalldeepseek/deepseek-v4-prozai-org/glm-5.2moonshotai/kimi-k3Equivalent paid value
How this was valued: Face value of the single shared beta signup credit; six model alternatives draw down the same balance and are not summed.
Limits and terms
- Signup Credit USD
- 5
- Recurrence
- No
- Expiry
- not publicly documented
- Scope
- shared across beta catalog
What happens to your prompts?
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.
- 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.
Governing documents
- Terms Of Service
- https://www.arcee.ai/terms-and-conditions
- Privacy Policy
- https://www.arcee.ai/privacy-policy
Eligibility
- Account Required
- Yes
- Payment Method Required
- Yes
- Signup Open To Anyone
- Yes
Primary sources
Before you build with Arcee Open Models API Beta
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label 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 region.
Resolve the live model route
This snapshot records 6 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.arcee.ai/api/v1, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. A payment method is required. A zero-cost allowance can still be useful, but protect the account with provider-side budgets or alerts before sending production traffic. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “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. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 5 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.