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After running an evaluation, the CLI provides a link to view results in the Axiom Console: 
your-eval-name (your-eval.eval.ts)

  • scorer-one 95.00%
  • scorer-two 87.50%
  • scorer-three 100.00%

View full report:
https://app.axiom.co/:org-id/ai-engineering/evaluations?runId=:run-id

Test Files 1 passed (1)
Tests 4 passed (4)
Duration 5.2 s
The evaluation interface helps you answer three core questions:
  1. How well does this configuration perform?
  2. How does it compare to previous versions?
  3. Which tradeoffs are acceptable?

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Compare configurations

To understand the impact of changes, compare evaluation runs to see deltas in accuracy, latency, and cost.

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Using the Console

Run your evaluation before and after making changes, then compare both runs in the Axiom Console: 
# Run baseline
axiom eval your-eval-name

# Make changes to your capability (update prompt, switch models, etc.)

# Run again
axiom eval your-eval-name
The Console shows both runs where you can analyze differences side-by-side.

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Using the baseline flag

For direct CLI comparison, specify a baseline evaluation ID: 
# Run baseline and note the trace ID from the output
axiom eval your-eval-name

# Make changes, then run with baseline
axiom eval your-eval-name --baseline <trace-id>
The CLI output will show deltas for each metric.
The --baseline flag expects a trace ID. After running an evaluation, copy the trace ID from the CLI output or Console URL to use as a baseline for comparison.
Example: Switching from gpt-4o-mini to gpt-4o might show:
  • Accuracy: 85% → 95% (+10%)
  • Latency: 800 ms → 1.6 s (+100%)
  • Cost per run: 0.002→0.002 → 0.020 (+900%)
This data helps you decide whether the quality improvement justifies the cost and latency increase for your use case.

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Investigate failures

When test cases fail, click into them to see:
  • The exact input that triggered the failure
  • What your capability output vs what was expected
  • The full trace of LLM calls and tool executions
Look for patterns:
  • Do failures cluster around specific input types?
  • Are certain scorers failing consistently?
  • Is high token usage correlated with failures?
Use these insights to add targeted test cases or refine your capability.

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Experiment with flags

Flags let you test multiple configurations systematically. Run several experiments: 
# Compare model and retrieval configurations
axiom eval --flag.model=gpt-4o-mini --flag.retrieval.topK=3
axiom eval --flag.model=gpt-4o-mini --flag.retrieval.topK=10
axiom eval --flag.model=gpt-4o --flag.retrieval.topK=3
axiom eval --flag.model=gpt-4o --flag.retrieval.topK=10
Compare all four runs in the Console to find the configuration that best balances quality, cost, and latency for your requirements.

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Track progress over time

For teams running evaluations regularly (nightly or in CI), the Console shows whether your capability is improving or regressing across iterations. Compare your latest run against your initial baseline to verify that accumulated changes are moving in the right direction.

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What’s next?

To learn how to use flags for experimentation, see Flags and experiments.