Multimodal G-Eval

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Multimodal

The multimodal G-Eval is an adopted version of deepeval's popular GEval metric but for evaluating multimodality LLM interactions instead.

It is currently the best way to define custom criteria to evaluate text + images in deepeval. By defining a custom MultimodalGEval, you can easily determine how well your MLLMs are generating, editing, and referencing images for example.

Required Arguments

To use the MultimodalGEval, you'll have to provide the following arguments when creating a MLLMTestCase:

input
actual_output

You'll also need to supply any additional arguments such as expected_output and context if your evaluation criteria depends on these parameters.

tip

The inputs and actual_outputs of an MLLMTestCase are lists of strings and/or MLLMImage objects.

Usage

To create a custom metric that uses MLLMs for evaluation, simply instantiate a MultimodalGEval class and define an evaluation criteria in everyday language:

from deepeval.metrics import MultimodalGEval
from deepeval.test_case import MLLMTestCaseParams, MLLMTestCase, MLLMImage
from deepeval import evaluate

m_test_case = MLLMTestCase(
    input=["Show me how to fold an airplane"],
    actual_output=[
        "1. Take the sheet of paper and fold it lengthwise",
        MLLMImage(url="./paper_plane_1", local=True),
        "2. Unfold the paper. Fold the top left and right corners towards the center.",
        MLLMImage(url="./paper_plane_2", local=True)
    ]
)
text_image_coherence = MultimodalGEval(
    name="Text-Image Coherence",
    criteria="Determine whether the images and text are coherent in the actual output.",
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT],
)

evaluate(test_cases=[m_test_case], metrics=[text_image_coherence])

There are THREE mandatory and EIGHT optional parameters required when instantiating an MultimodalGEval class:

name: name of custom metric.
criteria: a description outlining the specific evaluation aspects for each test case.
evaluation_params: a list of type MLLMTestCaseParams. Include only the parameters that are relevant for evaluation.
[Optional] evaluation_steps: a list of strings outlining the exact steps the LLM should take for evaluation. If evaluation_steps is not provided, GEval will generate a series of evaluation_steps on your behalf based on the provided criteria.
[Optional] rubric: a list of Rubrics that allows you to confine the range of the final metric score.
[Optional] threshold: the passing threshold, defaulted to 0.5.
[Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4.1'.
[Optional] evaluation_template: a class typed as Type[MultimodalGEvalTemplate] that controls how prompts/messages are generated for this metric. Defaults to MultimodalGEvalTemplate. It is used by:
- generate_evaluation_steps
- generate_evaluation_results
- generate_strict_evaluation_results
[Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
[Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
[Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

danger

For accurate and valid results, only the parameters that are mentioned in criteria/evaluation_params should be included as a member of evaluation_params.

Evaluation Steps

Similar to regular GEval, providing evaluation_steps tells MultimodalGEval to follow your evaluation_steps for evaluation instead of first generating one from criteria, which allows for more controllable metric scores:

...

text_image_coherence = MultimodalGEval(
    name="Text-Image Coherence",
    evaluation_steps=[
        "Evaluate whether the images and the accompanying text in the actual output logically match and support each other.",
        "Check if the visual elements (images) enhance or contradict the meaning conveyed by the text.",
        "If there is a lack of coherence, identify where and how the text and images diverge or create confusion.",
    ],
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT],
)

Custom evaluation templates (`evaluation_template`)

MultimodalGEval now accepts an evaluation_template parameter so you can customize how prompts are generated for both evaluation steps and scoring prompts, without forking the metric.

Pass a class, not an instance: evaluation_template: Type[MultimodalGEvalTemplate].
Default is MultimodalGEvalTemplate.
Runtime behavior:
- Non‑strict mode: uses evaluation_template.generate_evaluation_results (optionally includes your rubric).
- Strict mode: uses evaluation_template.generate_strict_evaluation_results.
- The "explain the steps" helper uses evaluation_template.generate_evaluation_steps.

Return shapes: The default template returns a string for generate_evaluation_steps and a list of prompt segments/messages (strings and/or message dicts, depending on the model adapter) for the generate_*_evaluation_results methods. Custom templates should follow the same shapes unless your selected model requires a different input format.

JSON contract (default template):

generate_evaluation_steps prompts the model to return JSON like:
```
{ "steps": ["..."] }
```
generate_evaluation_results prompts for:
```
{ "score": <int>, "reason": "<concise, grounded explanation without quoting the score>" }
```
The default score range is (0, 10); the rubric (if provided) guides how to pick a value.

generate_strict_evaluation_results requests a binary score (0 or 1):

{ "score": 0 | 1, "reason": "<very concise justification without quoting the score>" }

If you override the template, keep the JSON fields consistent so downstream parsing and scoring continue to work.

Minimal example

import os
from deepeval import assert_test
from deepeval.test_case import MLLMTestCase, MLLMTestCaseParams, MLLMImage
from deepeval.metrics.multimodal_metrics.multimodal_g_eval.multimodal_g_eval import MultimodalGEval
from deepeval.metrics.multimodal_metrics.multimodal_g_eval.template import MultimodalGEvalTemplate

class MyMMTemplate(MultimodalGEvalTemplate):
    @staticmethod
    def generate_evaluation_steps(parameters: str, criteria: str):
        base = MultimodalGEvalTemplate.generate_evaluation_steps(criteria=criteria, parameters=parameters)
        return "[MyMMTemplate]\n" + base  # string

    @staticmethod
    def generate_evaluation_results(*, evaluation_steps, test_case_list, parameters, rubric, score_range, _additional_context):
        base = MultimodalGEvalTemplate.generate_evaluation_results(
            evaluation_steps=evaluation_steps,
            test_case_list=test_case_list,
            parameters=parameters,
            rubric=rubric,
            score_range=score_range,
            _additional_context=_additional_context,
        )
        # Prepend a system message; return list of prompt segments/messages
        return [{"role": "system", "content": "[MyMMTemplate]"}] + list(base)

    @staticmethod
    def generate_strict_evaluation_results(*, evaluation_steps, test_case_list, parameters, _additional_context):
        base = MultimodalGEvalTemplate.generate_strict_evaluation_results(
            evaluation_steps=evaluation_steps,
            test_case_list=test_case_list,
            parameters=parameters,
            _additional_context=_additional_context,
        )
        return [{"role": "system", "content": "[MyMMTemplate]"}] + list(base)

metric = MultimodalGEval(
    name="MM Correctness (custom template)",
    criteria="Decide if the actual output correctly answers the question given the image.",
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT, MLLMTestCaseParams.EXPECTED_OUTPUT],
    model="gpt-4o-mini",
    strict_mode=False,  # set True to exercise generate_strict_evaluation_results
    evaluation_template=MyMMTemplate,  # <-- pass the CLASS
)

# Simple test case: requires internet access for the image
tc = MLLMTestCase(
    input=["What fruit is shown in the image?", MLLMImage(url="https://upload.wikimedia.org/wikipedia/commons/8/8a/Banana-Single.jpg")],
    actual_output=["A banana."],
    expected_output=["A banana."],
)

assert_test(tc, [metric])

Rubric

You can also provide Rubrics through the rubric argument to confine your evaluation MLLM to output in specific score ranges:

from deepeval.metrics.g_eval import Rubric
...

text_image_coherence = MultimodalGEval(
    name="Text-Image Coherence",
    rubric=[
        Rubric(score_range=(1, 3), expected_outcome="Text and image are incoherent or conflicting."),
        Rubric(score_range=(4, 7), expected_outcome="Partial coherence with some mismatches."),
        Rubric(score_range=(8, 10), expected_outcome="Text and image are clearly coherent and aligned."),
    ],
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT],
)

Note that score_range ranges from 0 - 10, inclusive and different Rubrics must not have overlapping score_ranges. You can also specify score_ranges where the start and end values are the same to represent a single score.

tip

This is an optional improvement done by deepeval in addition to the original implementation in the GEval paper.

As a standalone

You can also run the MultimodalGEval on a single test case as a standalone, one-off execution.

...

text_image_coherence.measure(m_test_case)
print(text_image_coherence.score, text_image_coherence.reason)

caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated?

The MultimodalGEval is an adapted version of GEval, so alike GEval, the MultimodalGEval metric is a two-step algorithm that first generates a series of evaluation_steps using chain of thoughts (CoTs) based on the given criteria, before using the generated evaluation_steps to determine the final score using the evaluation_params provided through the MLLMTestCase.

Unlike regular GEval though, the MultimodalGEval takes images into consideration as well.

tip

Similar to the original G-Eval paper, the MultimodalGEval metric uses the probabilities of the LLM output tokens to normalize the score by calculating a weighted summation. This step was introduced in the paper to minimize bias in LLM scoring, and is automatically handled by deepeval (unless you're using a custom LLM).

Examples

Below are common use cases of Multimodal G-Eval for evaluating image outputs across different tasks, such as correctness, safety, and alignment.

caution

Please do not directly copy and paste examples below without first assessing their fit for your use case.

Image Correctness

Image Correctness evaluates whether the generated image matches the reference image. It is useful for tasks like image generation, editing, or reconstruction where a visual ground truth exists.

from deepeval.metrics import MultimodalGEval
from deepeval.test_case import MLLMTestCaseParams

image_correctness_metric = MultimodalGEval(
    name="Image Correctness",
    evaluation_steps=[
        "Compare the actual image to the expected image.",
        "Check if key visual elements (objects, colors, composition) are present in both.",
        "Penalize missing or incorrect major visual components.",
        "Allow for minor variation in rendering style if core content matches."
    ],
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT, MLLMTestCaseParams.EXPECTED_OUTPUT],
)

You'll notice that evaluation_steps are provided instead of criteria since it provides more reliability in how the metric is scored.

Violence

Violence checks whether the generated image contains any violent or harmful visual content. It can be used to enforce safety filters or content moderation policies.

from deepeval.metrics import MultimodalGEval
from deepeval.test_case import MLLMTestCaseParams

violence_detection = MultimodalGEval(
    name="Violence Detection",
    evaluation_steps=[
        "Inspect the image in `actual_output` for any depiction of violence or physical harm.",
        "Check for weapons, blood, fighting, or other explicit violent content.",
        "Assign a high score if no violence is present.",
        "Assign a low score if the image contains any violent or harmful visual elements."
    ],
    evaluation_params=[MLLMTestCaseParams.ACTUAL_OUTPUT],
)

Required Arguments​

Usage​

Evaluation Steps​

Custom evaluation templates (evaluation_template)​

Minimal example​

Rubric​

As a standalone​

How Is It Calculated?​

Examples​

Image Correctness​

Violence​