HackerEarth vs Karat

Invite candidates to an assessment. Invitation limits and billing are compared separately from the ability to send an invitation.

Run assessments without a separate per-test charge across LunaPrompts subscriptions.

Check understanding of AI concepts alongside practical engineering tasks.

Assess the ability to direct, evaluate, and improve AI-assisted work.

Evaluate instructions, context, constraints, iteration, and output evaluation.

Assess tool use, orchestration, reliability, and agent behavior against a task.

Evaluate retrieval, context selection, grounding, and the quality of generated answers.

Assess the environment, tools, constraints, and evaluation harness around an AI agent.

Assess feedback loops, stopping conditions, recovery, and iterative improvement.

Evaluate how candidates plan, prompt, inspect, debug, and validate AI-written code.

Evaluate applicants your team has already found, separately from discovering new talent in a sourcing network.

Discover candidates against the capabilities your role actually requires.

Move discovered talent into the screening and assessment journey.

Review identity, session activity, and authorship signals together with the assessment evidence.

Use anti-cheating AI models and session signals to identify potential unauthorized assistance for review.

Investigate assistance from tools such as Parakeet and Cluely. Detection is a review signal, not a guarantee that every tool or incident will be caught.

Surface indicators associated with ParakeetAI for reviewer investigation in the configured secure environment. Detection coverage depends on the tool, version and environment.

Review signals associated with Cluely-style real-time assistance. Compare detection and blocking methods separately in the explanations.

Use hidden-window and process signals in LunaPrompts Secure to investigate AI overlays excluded from ordinary screen capture. This does not guarantee detection of every overlay.

LunaPrompts Secure provides a native runtime and locked assessment shell for selected high-integrity assessments.

Review device-level signals alongside assessment activity in the configured secure runtime.

Check for virtual-machine or remote-desktop environments as part of the secure assessment workflow.

Evaluate implementations, correctness, debugging, and coding decisions.

LunaPrompts reports support for Java, Python, C++, and more than 100 additional languages; select the runtime required for your assessment.

Review passed tests, implementation style, and how the candidate arrives at a working solution.

Evaluate working SQL queries against the data task required by the role.

Assess how candidates combine tables, summarize data and work with analytical queries.

Ask candidates to investigate and fix a problem in code.

Use code-review tasks to assess whether candidates identify correctness or security issues in AI-generated code.

Invite candidates to role-specific practical work outside a live interview.

Review completed work and scores before deciding whom to interview.

Send assessments and return results through supported connectors. Confirm the fields and actions for your setup.

Send an assessment through the Greenhouse workflow and return results.

Assign assessments to Ashby candidates and synchronize scores.

Listed as coming soon in the enterprise integration directory. Confirm availability and scope with the team.

Get help using and configuring the platform. Dedicated engineering implementation is a separate capability.

Engineering support for custom assessment workflows and enterprise-specific requirements.

Align the task, rubric, and expected evidence to your role and engineering context.

Review, adjust, and approve the questions and evaluation criteria before inviting candidates.

Provide a job description to create a proposed assessment within minutes.

Coordinate agents to turn role requirements into a proposed assessment structure.

Combine practical, knowledge and conversational formats in one assessment journey.

Adjust question difficulty, assessment duration and scoring before publishing.

Review the assessment plan and practical workspace from the candidate’s perspective.

Reuse saved assessment templates rather than rebuilding every workflow.

Start from a proven assessment and adjust it for the next role or cohort.

Inspect skills, task results, attempts, and candidate-level performance.

Revisit the assessment activity and evidence behind a submission.

Capture keystroke speed and interaction patterns as contextual telemetry for review.

Inspect how the candidate iterates and changes their implementation.

Compare candidate results against the role and other candidates.

Review score distributions to understand an individual result in context.

Inspect section performance instead of relying only on an overall score.

Follow assessment completion across a candidate group.

Follow qualification and conversion through the hiring funnel.

Give hiring teams scores or evaluation notes they can review when deciding who progresses.

Generate evaluation assistance and recommendations for the recruiting team to review.

Adapt evaluation assistance to enterprise criteria and reviewer feedback while providing an additional assessment perspective.

Call candidates to collect basic screening information.

Conduct an initial phone interview before the assessment or later human rounds.

Coordinate candidate availability and interview slots.

Help candidates with routine information during the recruiting process.

Run practical coding work inside the configured assessment environment.

Assess AI-assisted development with Cursor in the configured environment.

Evaluate how candidates direct and review work produced with Claude Code.

Evaluate planning, implementation, and verification with Codex.

Assess AI-assisted coding with Antigravity in the configured environment.

Use questions selected for the role in a human-led or AI-led interview.

Run human-led interviews in a shared evaluation environment.

Surface possible unauthorized AI assistance during the interview for human review.

Assist the interviewer with candidate evidence and evaluation during the conversation.

Prepare questions based on the role and evidence missing from earlier assessment stages.

Bring AI screening, assessment, and human interview findings together before the hiring decision.

Add role-specific questions. Custom question authoring and randomized delivery are separate requirements.

Use randomized question delivery to reduce repeated exposure to the same assessment.

Include multiple-choice questions for role-relevant concepts and technical knowledge.

Let candidates explain an approach in text or diagrams. This basic response format does not by itself establish a dedicated system-design workspace.

Evaluate architecture, tradeoffs, constraints, and the depth of the candidate’s design decisions.

Allow more than one team member to use the platform. Included seats, permissions, and extra-user charges depend on the plan.

Add authorized team members without a per-seat charge.

Invite a candidate group to an assessment in one workflow.

Manage invitations and candidate progress across a larger group.

Run assessments and hiring campaigns for multiple roles at the same time.

Group candidates by role, team, location or hiring cohort.

Let reviewers inspect interview answers or reports, whether the conversation was conducted by a person or an AI interviewer.

Use AI interview agents to conduct candidate screening conversations.

Run agent-led L1 rounds across campus and high-volume campaigns.

Ask candidates to explain an answer during a human-led or AI-led interview. Automated drill-down into submitted work is compared separately.

Ask follow-up questions during the assessment about the candidate’s own work.

Probe understanding, tradeoffs, and authorship through explanations of the submitted solution.

Keep candidate information linked to evaluation activity. This basic record does not by itself establish a full talent CRM.

Manage candidate records in the same platform as screening and assessments.

Track candidates through your recruiting stages with the associated data and evidence.

Keep candidate information and assessment context together; agree access and retention requirements for your deployment.