G-Eval vs DeepEval Metrics in 2026: Where Each Fits

G-Eval is an LLM-as-a-judge metric from the G-Eval paper (Liu et al., 2023): the judge turns your criteria into chain-of-thought evaluation steps, then scores the output with token-probability weighting. The DeepEval G-Eval metric, GEval, sits next to DAG, RAG, agent, multi-turn and safety metrics, so the real choice is which metric fits each criterion.

This guide maps when G-Eval is the right ruler, when a built-in DeepEval metric does the job better, and when neither fits. It then gives a five-metric suite per task type and shows where Future AGI’s Turing models and BYOK judges score the same criteria on production traces.

TL;DR on when each metric wins

• Use G-Eval for subjective custom criteria not covered by built-in metrics: brand voice, domain-specific helpfulness, custom rubrics.
• Use DAG for criteria with clear branching logic where you want hard-coded leaf-node scores.
• Use RAG metrics (Faithfulness, Answer Relevancy, Contextual Recall and Precision) for retrieval-augmented Q&A.
• Use agent metrics (Task Completion, Tool Correctness, Step Efficiency, Plan Adherence) for tool-using agents.
• Use conversational metrics (Knowledge Retention, Role Adherence, Conversation Completeness, Turn Relevancy) for multi-turn chatbots and copilots.
• Use safety metrics (Bias, Toxicity, PII Leakage, Role Violation) for compliance gates.
• Use FutureAGI local metrics when latency and cost matter and the surface is structural.
• Use FutureAGI Turing models for cloud-grade scoring at production scale.
• Use BYOK LiteLLM judges when you want to control the judge model identity, cost, and policy.

How G-Eval, DeepEval, and Future AGI fit together

G-Eval is the chain-of-thought rubric pattern at the core. DeepEval wraps it into a metric family. Future AGI’s eval SDK adds local metrics, Turing models and BYOK LLM judges behind one evaluate() call, and attaches the scores to traced spans.

What the DeepEval G-Eval metric actually is

G-Eval is a metric, not a framework. The DeepEval docs describe it as a custom metric, best for “subjective criteria like correctness, coherence, or tone”, that “first generates a series of evaluation steps, before using these steps in conjunction with information” in test cases for evaluation. The mechanism in DeepEval’s implementation:

1. Evaluation step generation. Natural language criteria are transformed into structured evaluation steps via the LLM.
2. Judging. An LLM judge assesses outputs using these steps.
3. Scoring. Results are weighted by token-level log-probabilities to produce final scores.

The token-level log-probability weighting is the move that distinguishes G-Eval from a naive “ask the LLM for a score” approach. It produces continuous scores rather than buckets, which avoids the failure mode of an LLM judge that always returns 7 out of 10.

Confident-AI’s version of G-Eval extends the original paper with rubrics that confine scores to explicit ranges from 0 to 10, and with manual evaluation steps for consistency. The “form-filling paradigm” itself comes from the original paper, and DeepEval uses it to put several test case fields, such as input, actual output and expected output, into one evaluation prompt.

A DeepEval G-Eval example

DeepEval’s docs list three mandatory GEval arguments: name, criteria and evaluation_params. Pass evaluation_steps instead of criteria when you want the same steps on every run; the docs say to pass one or the other, never both. A Rubric list confines the judge to score ranges from 0 to 10, and the final score still lands between 0 and 1 with a default pass threshold of 0.5.

from deepeval.test_case import LLMTestCase, SingleTurnParams
from deepeval.metrics import GEval
from deepeval.metrics.g_eval import Rubric

correctness = GEval(
    name="Correctness",
    criteria="Determine whether the actual output is factually correct based on the expected output.",
    evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT, SingleTurnParams.EXPECTED_OUTPUT],
    rubric=[Rubric(score_range=(0, 4), expected_outcome="Incorrect."),
            Rubric(score_range=(5, 10), expected_outcome="Mostly or fully correct.")],
)
case = LLMTestCase(input="Who wrote Hamlet?", actual_output="Shakespeare.", expected_output="William Shakespeare.")
correctness.measure(case)
print(correctness.score, correctness.reason)

For multi-turn chats, ConversationalGEval takes the same arguments with MultiTurnParams and reads every turn of a ConversationalTestCase as one conversation history. Run measure() on one case, as above, or pass the metric to evaluate() with a list of test cases.

What G-Eval is good at

• Custom subjective criteria. Brand voice, tone, domain-specific helpfulness.
• Rubric clarity. The chain-of-thought decomposition forces you to articulate criteria, which catches sloppy rubrics.
• Continuous scores. Token-level weighting gives finer-grained outputs than naive scoring.
• Bias mitigation. Confident-AI argues G-Eval addresses inconsistent scoring (via decomposition), lack of fine-grained judgment (via probability normalization), verbosity bias (via customizable criteria), and narcissistic bias (via consistent rubrics).

What G-Eval is not good at

• Replacing well-defined metrics. If the surface is RAG faithfulness, the Faithfulness metric is more honest than a hand-rolled G-Eval rubric. If it is tool correctness, use Tool Correctness.
• Determinism. G-Eval is non-deterministic by construction. Pin the judge model and rubric to bound the variance, but do not call it deterministic.
• Cheap inference. Each G-Eval call is an LLM call, often with a long rubric. Sample by failure signal in production.
• Adversarial pressure. A user who knows the rubric can game it. G-Eval is for scoring, not for safety enforcement.

What DeepEval’s other metrics cover

DeepEval’s metrics introduction (Python v4.3.3) sums up its metrics in eight tabs: Custom, AI Agents, Voice Agents, Chatbots (multi-turn), RAG, Safety, Image, and Others. The list below covers all but Voice Agents and Others.

1. Custom Metrics: G-Eval. Free-form rubric LLM judge.
2. Custom Metrics: Conversational G-Eval. G-Eval applied to multi-turn dialogue.
3. Custom Metrics: DAG. Decision tree with LLM branching at internal nodes and hard-coded scores at leaves. More deterministic than G-Eval.
4. RAG Metrics. Faithfulness, Answer Relevancy, Contextual Relevancy, Contextual Recall, Contextual Precision. Each is an LLM judge with a specific rubric for retriever or generator components.
5. Agent Metrics. Tool Correctness, Task Completion, Argument Correctness, Step Efficiency, Plan Adherence, Plan Quality. Task Completion, Step Efficiency, Plan Adherence and Plan Quality score the full trace; Tool Correctness and Argument Correctness score single tool-calling decisions.
6. Chatbot (Multi-turn) Metrics. Knowledge Retention, Role Adherence, Conversation Completeness, Turn Relevancy. Score conversations as a whole.
7. Safety Metrics. Bias, Toxicity, Non-Advice, Misuse, PII Leakage, Role Violation. LLM judges focused on security dimensions.
8. Image Metrics. Image Coherence, Image Helpfulness, Image Reference, Text-to-Image, and Image-Editing. LLM judges with multimodal capability.

Scoring metrics output a 0 to 1 score with a written reason and pass at a threshold that defaults to 0.5. The structural decision in 2026 is which metric category fits your task. G-Eval is the fallback when the rest do not fit, not the default.

JevEval and Arena G-Eval next to G-Eval

DeepEval now lists two more custom metrics beside G-Eval and DAG. JevEval scores bounded questions you write with Jev, a “System One” model that returns calibrated probabilities instead of generated text, so no LLM is involved and the same case gets the same score. Arena G-Eval runs G-Eval criteria as a blinded, pairwise judge that picks the better of several test cases, which suits prompt or model comparisons.

Where FutureAGI’s Turing models and local metrics fit

FutureAGI’s eval SDK ships three execution paths behind one evaluate() call, installed with pip install agent-learning-kit. Pass no model for local metrics, a Turing model name for cloud scoring, or any LiteLLM model string for a custom judge:

• Local metrics. Local heuristic metrics that run in under a millisecond with no API key, with optional DeBERTa-based NLI scoring for hallucination checks. They cover string and similarity checks, hallucination, JSON validation, RAG metrics, and agent and function-call scoring. The right tool when latency and cost matter and the surface is structural.
• Turing models. Cloud eval models, from turing_flash at about 1 to 2 seconds to turing_large at about 3 to 5 seconds, purpose-built for production scoring. Useful when local metrics are not enough but a frontier-model BYOK judge is too expensive for the volume.
• BYOK LLM-as-judge. Custom LLM judges through any LiteLLM-supported model: OpenAI, Anthropic and Gemini models, or local models through Ollama. Use when you need a specific judge identity, want full control over cost and policy, or are running in a regulated environment that mandates a specific provider.

The pattern that works for most production teams: deterministic local metrics first as cheap fail-fast gates, Turing models for high-volume cloud scoring, and BYOK frontier judges reserved for adjudication on disputed scores. A production stack often runs DeepEval in CI for pytest gates, FutureAGI local metrics on every span in production for cheap structural checks, and FutureAGI Turing or BYOK judges for semantic scoring on sampled traffic.

Judge cost vs coverage across eval tiers

Structural checks run first and cost the least. Future AGI Turing models add semantic scoring in about 1 to 5 seconds a call, depending on the tier, billed in AI credits, and frontier judges handle the hardest calls, where a pinned rubric matters most.

A working pattern for a 2026 eval suite

The Confident-AI “5 metric rule” pairs 1-2 custom metrics (G-Eval or DAG) with 2-3 generic RAG, agent or conversational metrics, five at most. It is a sensible default for most production stacks. A concrete instance for each common task type:

RAG Q&A endpoint

• Faithfulness (built-in, RAG)
• Answer Relevancy (built-in, RAG)
• Contextual Recall (built-in, RAG)
• G-Eval brand voice (custom)
• G-Eval helpfulness (custom)
• Plus deterministic checks: JSON schema validation if the output is structured, regex for forbidden phrases, exact match for canonical answers if applicable.

Tool-using agent

• Task Completion (built-in, agent)
• Tool Correctness (built-in, agent)
• Argument Correctness (built-in, agent)
• G-Eval domain accuracy (custom)
• Step Efficiency (built-in, agent)
• Plus deterministic checks: tool argument schema validation, function-call parser success.

Multi-turn support chatbot

• Conversation Completeness (built-in, conversational)
• Knowledge Retention (built-in, conversational)
• Role Adherence (built-in, conversational)
• G-Eval outcome accuracy (custom, domain-specific: ticket resolved, claim filed)
• Turn Relevancy (built-in, conversational)
• Plus deterministic checks: PII redaction regex, forbidden-phrase regex, response-format validation.

Code or SQL generation

• AST equality and parser success (deterministic)
• Unit test pass rate (deterministic)
• G-Eval style and explanation (custom)
• Faithfulness against the requirements (custom RAG-style)
• Embedding similarity (deterministic with pinned model)

The discipline that matters more than which exact five metrics: pin the judge model and rubric, gate the build on regressions, and maintain the dataset like a piece of code. A judge or rubric change can shift scores, so re-baseline after every change, as you would after a code change.

Common mistakes when running G-Eval and DeepEval metrics

Teams get into trouble with G-Eval when they use it where a built-in metric already exists, or run it without a pinned judge and a written rubric. Six mistakes keep showing up:

• Defaulting to G-Eval for everything. If the surface has a built-in metric (RAG, agent, conversational), use it. The math is research-backed. Save G-Eval for the criteria the built-ins do not cover.
• Skipping the rubric work. A G-Eval that says “score 0 to 1 for helpfulness” is not a rubric. The chain-of-thought decomposition is what makes G-Eval useful; do the rubric work.
• Not pinning the judge model. A judge model upgrade can shift scores measurably. Pin model id, version, temperature, and rubric text.
• Using the same model as judge and as production agent without controls. Self-judging has known biases. Mix judge models for high-stakes scoring.
• Running G-Eval on every production trace. Token cost adds up. Sample by failure signal, length, or user segment.
• Confusing G-Eval with DeepEval. Many teams pitch “we use G-Eval” when they mean “we use DeepEval.” Get the names right; it matters in procurement.

How FutureAGI implements G-Eval-style metrics

FutureAGI is the production-grade LLM evaluation platform built around the rubric-first scoring this post described. traceAI is Apache-2.0, and the platform has an Apache-2.0 core you can self-host. Four pieces carry G-Eval-style scoring from a notebook to production traffic:

• G-Eval-style rubrics: write grading criteria in plain English and score them with any LiteLLM model through evaluate(prompt=criteria, output=answer, engine="llm", model="gpt-6-luna").
• Built-in metric library: local and Turing metrics such as faithfulness, answer relevancy, groundedness, task completion, toxicity and data privacy compliance.
• Judge layer: turing_flash returns cloud eval results in about 1 to 2 seconds, and BYOK LLM-as-judge has $0 platform cost.
• Tracing: traceAI is built on OpenTelemetry and instruments AI apps in Python, TypeScript, Java, and Go, with manual spans in C#. Evals run on traced spans, historic or continuous, with results per span in Observe.

Beyond the eval surface, Future AGI also ships persona-driven simulation, prompt and agent optimizers, the Agent Command Center gateway with routing across 100+ providers, and runtime guardrails for PII and prompt injection. Free and Pay-as-you-go plans start at $0, with SOC 2 Type II, HIPAA BAA and SAML SSO as paid add-ons (pricing).

Most teams running G-Eval-style rubrics in production also adopt three or four ancillary tools: one for the rubric runtime, one for traces, one for the gateway, one for guardrails. FutureAGI is the recommended pick because the rubric library, judge, trace, simulation, gateway, and guardrail surfaces all live on one platform with an Apache-2.0 core you can self-host; the loop closes without stitching.

Picking the right ruler for each criterion

Reach for a built-in DeepEval metric wherever one matches the surface, and keep G-Eval or DAG for the one or two criteria nothing else measures. Pin the judge model so scores stay comparable from run to run. For the wider tool choice, read DeepEval Alternatives, Deterministic LLM Eval Metrics, and Best LLM Evaluation Tools.

Score your first G-Eval-style rubric in Future AGI and watch the results land on your production traces.

Sources

• G-Eval paper (Liu et al., 2023)