No. 24 of 30 ·AI LLM Evaluation Tools

OpenAI Evals

Web · Self-hosted · API

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Freedom report

One barScore 5.5

Free tierNo free tier on record
Open codeNo open-source code on record
Runs widely1 of 6 device platforms
DocumentedPlans, terms and facts published

OpenAI Evals is ranked #24 of 30 in AI LLM evaluation tools on Freedom251. It runs on API, Self-hosted, Web.

Compared on AI LLM evaluation tools

Evaluation methods: basic exact/match evaluations, model-graded evaluations, custom evaluation logic, academic benchmarks, meta-evaluations
Model support: OpenAI API models and custom CompletionFunction implementations
Safety evaluations: Yes
Deployment: hybrid
Prompt versioning: Yes
API access: Yes

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The record

Purpose: Evals test model outputs against style and content criteria that you specify.
Dashboard and API: You can configure evals in the OpenAI dashboard or programmatically with the Evals API.
Test data: An eval run uses a test dataset representing the kind of data you expect your prompt to handle.
Third-party providers: The listed third-party model providers are Google, Anthropic hosted on AWS Bedrock, Together, and Fireworks.
Third-party eligibility: Using third-party models requires an OpenAI organization at usage tier 1 or higher and an admin to enable the feature and accept its usage disclaimer.
Third-party spend limits: OpenAI currently covers third-party inference costs up to monthly limits of $5, $25, $50, $100, and $200 for usage tiers 1 through 5, respectively.
Custom endpoint security: Custom model endpoints must use HTTPS, and OpenAI says it encrypts API keys supplied for them.
External model limits: Tool calls are currently not supported when running evals with external models.
Data handling: Calls to external models pass data to third parties and are subject to different terms and weaker safety guarantees than calls to OpenAI models.
Deprecation: OpenAI says the Evals platform becomes read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026.
Alternative: OpenAI recommends trying Datasets for a more iterative environment to experiment while building an eval.
Dashboard: Evals can be configured and run directly in the OpenAI Dashboard.
Private evaluations: Users can build private evals from their own data without exposing that data publicly.
Evaluation criteria: An eval uses a data-source schema and testing criteria consisting of graders that determine whether model output is correct.

Where it ranks on Freedom251

Best AI LLM Evaluation Tools in 2026#24 of 30

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Sources

developers.openai.com/api/docs/guides/evals· checked 30 Sept 2026
developers.openai.com/api/docs/guides/external-models· checked 30 Sept 2026
github.com/openai/evals· checked 1 Oct 2026
developers.openai.com/api/docs/guides/evaluation-getting-star· checked 1 Oct 2026
developers.openai.com/api/docs/guides/your-data· checked 1 Oct 2026
openai.com/security-and-privacy/· checked 1 Oct 2026