5 best free LLM evaluation tools in 2026

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A useful free LLM evaluation tool lets you test a prompt change against cases you can run again. A free plan also needs to let the people who judge answer quality review the results. An execution allowance is less useful if those reviewers cannot join the workspace.

Braintrust is our first recommendation for teams that want to evaluate together without buying reviewer seats. Its Starter plan includes unlimited users and a configurable human review score per project. The other options below suit different constraints, including longer hosted retention, spreadsheet-style testing, and evaluations maintained in Python.

What counts as free LLM evaluation?

This guide includes ongoing hosted free plans and free software you run yourself. It does not treat a time-limited trial as a permanent free option.

A $0 platform fee does not necessarily cover inference. Running your application can incur model charges, and an LLM judge makes additional model calls. Self-hosted software also needs compute and maintenance. For hosted tools, check what each allowance counts. A score, trace, span, and computed cell measure different activity, so their quotas cannot be compared as counts of complete evaluations.

Check the linked pricing pages before choosing a plan, because allowances and billing terms can change. The comparison table collects the main free allowances; the product sections explain what those limits mean in use.

1. Braintrust

Best for: Braintrust is the strongest fit for running and reviewing repeatable evaluations with colleagues on an ongoing free plan.

Braintrust connects dataset cases, scoring, and saved experiments, so you can compare a prompt change against the same cases and review individual failures. Its playgrounds let you test prompts and models against dataset cases, run full evaluations, and inspect results side by side in a browser.

From a failed answer to a repeatable test

Suppose a support assistant gives an incorrect refund answer. Add the question and the policy-correct answer to a dataset so you can test the revised prompt against the same case. In the playground, compare the original and revised prompts against the same cases. A scorer can flag whether the answer meets your criteria; inspecting the individual output helps you judge whether the score is meaningful.

Preserve the result before your next iteration. Playground runs overwrite previous results, so select + Experiment to save a run you want to revisit. Saved experiments are retained separately from logs and playground data: Braintrust documents up to 365-day experiment retention on all plans.

Reviewing results with colleagues

Starter includes unlimited users, so a developer can invite the colleague responsible for support policy without purchasing another seat. Note the access that comes with it: Starter's only built-in permission group is Owners, which grants full access to organization data and settings, including removing members and deleting resources. If the reviewer should not hold those rights, Engineers and Viewers require Pro, and scoped project or per-member permissions require Enterprise. Its one configurable human review score per project provides a shared place to record a judgment about an answer. The support-policy reviewer and the developer can inspect the same experiment and record whether a revised answer meets the policy.

Starter does not limit the number of projects, datasets, playgrounds, or experiments, although recorded usage still counts toward its allowances.

Pros:

• No per-seat charge for adding reviewers on Starter.
• A configurable human review score lets the team record domain judgment alongside evaluation results.

Cons:

• Starter keeps logs and playground data for up to 14 days. Save important comparisons as experiments rather than relying on playground history.
• Playground annotations and dataset snapshots require a paid plan; they are separate from Starter's human review score.
• Unlimited users is not the same as least-privilege collaboration. Every Starter invitee must be an Owner, so adding a reviewer to collect a score also grants organization-wide administrative access.

Pricing: Starter has a $0 platform fee. The table below lists its monthly allowances. Usage beyond those allowances costs $4 per GB of processed data and $2.50 per 1,000 scores. Model use is charged at token rates. Built-in model access requires an owner with a work email or a payment method on file. If credits run out without on-demand usage enabled, built-in model use pauses until renewal or billing is enabled. See plans and limits for the full breakdown.

2. LangSmith

Best for: LangChain users who want to evaluate prompts alongside application traces.

Evaluation workflow: LangSmith lets you run prompt and model configurations against an existing dataset in its prompt playground. It also supports traced runs and online evaluations. A playground run creates an experiment across the dataset's examples, letting you inspect results case by case.

Pros:

• You can add an LLM-as-judge evaluator in the playground or use a custom-code evaluator. When an experiment exposes a failure, you can inspect the result, revise the prompt, and run the dataset again.

Cons:

• The Developer plan has one seat, so a shared team workspace requires a paid plan. Model and judge calls can add costs beyond the platform's trace allowance.

Pricing: Developer has no platform fee and includes the base-trace allowance shown below. Additional traces incur pay-as-you-go charges. Plus costs $39 per seat monthly and includes up to 10,000 base traces.

Also read: LangSmith vs. Braintrust

3. Comet Opik

Best for: Comet Opik suits you if you want up to 10 people on a hosted free plan and the option to self-host later.

What it is: Opik combines tracing with dataset-based experiments. In its prompt playground, you can select a dataset or test suite, try prompt and model variants, and compare their outputs side by side. Opik runs each variant against every selected item as a separate experiment, with experiments saved automatically for later review.

Pros:

• The Free Cloud plan supports up to 10 members who can review experiments together. Test suites also support assertions, which let you check expected behavior rather than relying only on visual comparison.

Cons:

• A span records a traced operation, so an evaluation with several steps can consume multiple spans. Self-hosting avoids Free Cloud's hosted usage limits, but you must provide infrastructure, maintenance, and any model calls used in evaluations.

Pricing: Free Cloud has a $0 platform fee and 60-day retention. Opik also offers free open-source software for self-hosting.

4. PromptLayer

Best for: Solo developers and small teams who want to test prompts in a spreadsheet-style interface.

What it is: PromptLayer Tables lets you add test cases from a CSV, enter them manually, or pull them from request history. You can add computed columns to run prompts, check outputs, and compare results across rows.

Pros:

• You can run a whole table or select particular rows, columns, or cells. Status, scorecards, and run history help you inspect results before changing a prompt or expanding a test set.

Cons:

• The 250-cell monthly allowance is small for repeated dataset tests: each executed computed cell consumes allowance, rather than one entire table counting as one evaluation. Use Tables for new tests because PromptLayer's legacy evaluation workflow is deprecated.

Pricing: The Free plan includes five users in one workspace, with a 10 MB limit per dataset and 10 playground runs per day. Monthly request and evaluation allowances appear below; model-provider charges may apply separately.

Also read: Braintrust vs. PromptLayer

5. Pydantic Evals

Best for: Python-first engineers who want to run repeatable evaluations in their own environment without a platform usage cap.

Evaluation workflow: Pydantic Evals lets you keep repeatable cases and scoring logic in Python without relying on a hosted evaluation service. Define cases with inputs and optional expected outputs, group them into datasets, and use evaluators to score your application's results. You can run evaluations locally, inspect terminal reports, and save results to disk.

Pros:

• The free framework lets you keep test cases and scoring logic in Python as your application changes. Optional Logfire adds visual comparisons and collaboration, but you do not need it to run evaluations.

Cons:

• Your team maintains the Python test suite and its execution environment. A colleague who needs a shared visual review interface will need an additional tool.

Pricing: The pydantic-evals package is a separate, free install, not a hosted free tier or a time-limited trial. The framework sets no usage cap, but you pay separately for your compute and any model or judge calls your evaluations make.

If you later want hosted comparisons and shared review alongside the Python suite, Braintrust has a Pydantic AI integration that traces model calls from the same application.

How the five free offerings compare

These allowances measure different activity and should not be ranked by their numerical size. Model and judge-call charges may apply separately.

Checking whether your first evaluation stays free

For a simple pilot, 50 cases tested against two prompt versions with two scores per answer produce 200 recorded scores. Reruns and human review add more. This is an illustrative score count, not an estimate of inference cost.

Run a small representative subset first. In Braintrust, go to Settings > Billing and use Usage analytics to compare the recorded data and scores with the organization's remaining monthly allowances. The scope dropdown covers the entire organization by default, so other projects' usage is included. Each recorded human or automated score counts, and processed data includes more than the final answer.

Estimate the larger run from that pilot, allowing for retries and longer responses. For inference, multiply the expected input and output tokens by the selected model's respective rates. Calculate the application's calls and any LLM judge calls separately; a count of 200 scores does not tell you either token total.

Check model billing separately: Braintrust's built-in credits should not be assumed to cover calls billed through your own provider account. Spend alerts are informational only and do not interrupt or limit usage, and they require on-demand usage to be enabled. A free-plan label alone cannot guarantee a $0 total.

Choosing a tool

Start with Braintrust if you need colleagues to review repeatable evaluations without seat fees, and save important comparisons as experiments. Consider Comet Opik for its hosted workspace with 60-day retention or LangSmith if you work alone in the LangChain ecosystem. PromptLayer fits selective tests in a table, while Pydantic Evals fits a Python workflow you run yourself.

Use the pilot to confirm that the tool fits both your review workflow and your budget before expanding the dataset.

Start free with Braintrust to run your first evaluation with unlimited reviewers.