GPT-5.4 Mini via SERV api
$1 per 1M input tokens ($6.00 per 1M output) source (opens in a new tab)
Usage
For: Low-cost agent workloads
Limits
- 400K context
- Prepaid credits
Includes
- Reasoning compiler included in price
API included
CodingDocumented overviewLast checked Oct 1, 2026
Drop-in LLM inference API with a built-in reasoning compiler, compatible with OpenAI and Anthropic SDKs, plus agent audit dashboard.
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(open full size in a new tab)Our take: Choose OpenServ if you want to route existing OpenAI or Anthropic SDK agent traffic through a reasoning layer by changing one base URL and pay per token from prepaid credits.
Main limitation: Usage is billed from prepaid credits at per-model token prices, with no free tier stated.
Choose OpenServ if you want to route existing OpenAI or Anthropic SDK agent traffic through a reasoning layer by changing one base URL and pay per token from prepaid credits. Based on the vendor's documentation, not on a hands-on test.
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Prices checked Oct 1, 2026 on the official US pricing page. Official pricing ↗ Prices and limits change often.

No free tier stated.
$1 per 1M input tokens ($6.00 per 1M output) source (opens in a new tab)
Usage
For: Low-cost agent workloads
Limits
Includes
API included
$2.6 per 1M input tokens ($13.00 per 1M output) source (opens in a new tab)
Usage
For: Mid-tier Claude agents
Limits
Includes
API included
$1 per 1M input tokens ($4.50 per 1M output) source (opens in a new tab)
Usage
For: Cheap fast calls
Limits
Includes
API included
Prices include SERV Reasoning, so they differ from provider list prices.
OpenServ offers SERV Reasoning, an inference API that sits in front of OpenAI, Anthropic and Google models. By changing the base URL in an existing OpenAI or Anthropic SDK, requests are compiled into bounded reasoning graphs with schema-validated outputs, which the company says improves reliability and cost for agent workloads while making every step auditable. The platform also includes a console with a playground, usage and billing dashboards, a no-code agent builder, and a web3-oriented Launch product for tokenized fundraising. It is positioned at enterprises, banks and government workloads as well as agent and web3 builders.

You provide
You get back
Scenarios are worked out by our editors from the documentation; the actual result depends on your input and plan.

Accepts requests from the OpenAI and Anthropic SDKs at inference-api.openserv.ai/v1. Avoids code migration.
Offers OpenAI, Anthropic and Google models under one API key. Switch models without new vendors.
Compiles requests into bounded reasoning graphs with schema-forced outputs. Makes steps auditable.
Shows usage, cost and latency in the console. Tracks spend per project.

OpenServ (SERV Reasoning) has no public API in its official documentation. SERV Reasoning API is public. API documentation ↗
Summarized from the vendor's current policies; this is not legal advice and a vendor's badge is not an independent audit.
Prompts pass through to OpenAI, Anthropic or Google models.
"Better suited" is based on documented features and pricing, not on a head-to-head test.
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Change the base URL to inference-api.openserv.ai/v1; the SDK and prompts stay the same. source (opens in a new tab)
Per million input and output tokens by model, paid from prepaid credits; listed prices include SERV Reasoning. source (opens in a new tab)
OpenAI, Anthropic and Google models, selected by API ID. source (opens in a new tab)
Only organization owners can buy credits or manage billing, per the product pages. source (opens in a new tab)
SOC 2 and HIPAA appear as Q4 2026 roadmap items. source (opens in a new tab)
Not verified: free credits; data retention; privacy policy.
Every fact about OpenServ (SERV Reasoning) above comes from the 5 official pages listed here, last compared on Oct 1, 2026; we have not run OpenServ (SERV Reasoning) ourselves, so it has no editorial score. How we check and re-check pages.
Article updated Oct 10, 2026; prices checked Oct 1, 2026. Outbound links to vendors are plain links; we do not earn commissions. How we review · Report an error
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