Promptfoo vs DeepEval: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (93.4 vs 91.5): Promptfoo wins on declarative YAML CI/CD matrix testing and automated OWASP LLM Top-10 red-team vulnerability scanning with 26.0k+ GitHub stars (93.4), while DeepEval wins for Python engineers who want Pytest-native unit tests and 40+ RAG faithfulness/G-Eval metrics (91.5). On the October 2026 PulseForge 100-point empirical benchmark rig (LLM Evaluation, CI/CD Testing & Red-Teaming Shootout), Promptfoo scores 93.4/100 (Velocity 28.2/30, Quality 23.4/25, Efficiency 22.8/25, Privacy/SLA 19.0/20) and DeepEval scores 91.5/100 (Velocity 27.1/30, Quality 22.9/25, Efficiency 23.4/25, Privacy/SLA 18.1/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
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Which Wins for Your Specific Engineering Workload?
26,042+ GitHub stars under MIT license with zero-code YAML prompt & model matrix comparison โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Promptfoo vs DeepEval: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Promptfoo | DeepEval |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 93.4 / 100 | 91.5 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.2 / 30 โ 26,042+ GitHub stars under MIT license with zero-code YAML prompt & model matrix comparison | 27.1 / 30 โ 18,901+ GitHub stars with native `deepeval test run` Pytest CLI and Python decorator workflow |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.4 / 25 โ Automated OWASP LLM Top-10 red-teaming for prompt injection, PII leaks, and SSRF | 22.9 / 25 โ 40+ specialized RAG and agent metrics including G-Eval, Contextual Recall, and Tool Correctness |
| Pillar 3: Token & RAM Efficiency (25%) | 22.8 / 25 โ 1-command GitHub Actions CI gate with local web viewer and zero cloud lock-in | 23.4 / 25 โ Synthetic test dataset generation and self-explaining metric failure reasons |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.0 / 20 โ Repo index: promptfoo/promptfoo | 18.1 / 20 โ Repo index: confident-ai/deepeval |
| Seat Pricing & Fast-Pool Economics | 100% Free / MIT | 100% Free / Apache-2.0 |
| Verified Production Failure Mode | Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Promptfoo
93.4 / 100- 26,042+ GitHub stars under MIT license with zero-code YAML prompt & model matrix comparison
- Automated OWASP LLM Top-10 red-teaming for prompt injection, PII leaks, and SSRF
- 1-command GitHub Actions CI gate with local web viewer and zero cloud lock-in
When to Standardize on DeepEval
91.5 / 100- 18,901+ GitHub stars with native `deepeval test run` Pytest CLI and Python decorator workflow
- 40+ specialized RAG and agent metrics including G-Eval, Contextual Recall, and Tool Correctness
- Synthetic test dataset generation and self-explaining metric failure reasons
Make promptfoo/promptfoo Run 45% Cheaper Across Both Promptfoo & DeepEval
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Promptfoo vs DeepEval
Which is better in 2026: Promptfoo or DeepEval?
Promptfoo wins on declarative YAML CI/CD matrix testing and automated OWASP LLM Top-10 red-team vulnerability scanning with 26.0k+ GitHub stars (93.4), while DeepEval wins for Python engineers who want Pytest-native unit tests and 40+ RAG faithfulness/G-Eval metrics (91.5). On the October 2026 PulseForge 100-point empirical benchmark rig (LLM Evaluation, CI/CD Testing & Red-Teaming Shootout), Promptfoo scores 93.4/100 (Velocity 28.2/30, Quality 23.4/25, Efficiency 22.8/25, Privacy/SLA 19.0/20) and DeepEval scores 91.5/100 (Velocity 27.1/30, Quality 22.9/25, Efficiency 23.4/25, Privacy/SLA 18.1/20).
What is the real monthly cost and failure mode of Promptfoo vs DeepEval?
Promptfoo (100% Free / MIT) trade-off: Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. DeepEval (100% Free / Apache-2.0) trade-off: Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.
How can engineering teams synchronize rules across Promptfoo and DeepEval while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.