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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →If an AI gives different answers to the same question in text, image, and video tests, treat the mismatch as a warning—not proof that any one answer is correct. Check that the inputs were actually available to the model, compare controlled runs, and verify claims against the original evidence. Here’s a repeatable way to find out whether the difference comes from the prompt, the media, or the model’s interpretation.
Why can answers differ across text, images, and video?
A multimodal model may receive different evidence in each test. A text-only prompt contains what you wrote; an image adds visual details; a video may be represented through selected frames rather than every moment. The wording, surrounding context, file handling, and model settings can also affect the response.
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So a disagreement does not reveal which response is right. The image answer may be grounded in a visible detail, the video answer may have missed the relevant moment, or either response may contain an unsupported inference. Even text-only answers can be wrong. The discrepancy is a reason to investigate, not a verdict.
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How to troubleshoot an inconsistent answer
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Freeze the test case
Save the exact question, context, requested answer format, media file, model name and version, settings, and outputs. If you change several things at once, you will not know what caused the difference.
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Check what the model actually received
Confirm that the image or video uploaded and was accepted, and that the relevant image region or video moment was available. For video, check the supported duration and how the system samples frames, if that information is provided. A brief event can be missed if it does not appear in the frames the model processes.
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Run matched comparisons
Keep the underlying question and answer format the same while changing only the evidence condition: text alone, text plus image, or text plus video. If the model can return different responses across identical runs, repeat the test and record each result rather than selecting the answer you prefer.
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Ask for evidence behind each claim
Separate what the model directly observes from what it infers. For an image, ask which visible detail supports a statement; for video, ask which moment supports it. Treat an explanation as another model output, not independent verification. Check important claims against the original media or another reliable source.
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Test a set of cases, not one example
Use examples with answers you can establish independently, including text, image, and video variants where practical. Record results by task and modality, along with errors, uncertainty, and abstentions. Preserve the model version and settings so later comparisons remain meaningful.
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Describe the result narrowly
Report what happened for the tested version, settings, and examples. Avoid turning one failure or a benchmark score into a claim about every task or multimodal system.
How to tell whether an image or video answer is hallucinated
Compare each factual statement with the supplied media. A description is unsupported if it asserts a detail that is not visible or audible in the relevant material, or if it presents an interpretation as an observation. For example, “a person is holding a red bag” is a visual claim to check; “the person is leaving because they are upset” adds an explanation that may not be established by the image.
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- Break a long response into individual claims instead of judging it as a whole.
- Check whether each claim is directly observable, inferred, or unverifiable from the supplied media.
- For video, inspect the relevant time and confirm that the event appears in the frames or segment available to the model.
- Verify consequential details independently; confidence or a plausible rationale is not proof.
- Record omissions and uncertainty as well as outright false statements.
OpenAI’s 2024 GPT-4o System Card lists “ungrounded inference” among the risks it evaluated. That is evidence about the risks discussed for GPT-4o, not a guarantee about every model or current interface. OpenAI’s GPT-4o System Card.
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Benchmarks help characterize tested systems on defined tasks. They do not settle which answer is correct in an individual interaction or guarantee performance on future examples.
Image-question factuality
Google DeepMind describes FACTS Multimodal as evaluating factually accurate answers to image-based questions and visual grounding. Its 2026 benchmark report says the multimodal slice generally had the lowest scores among the slices it discussed. The slice contains 711 public and 811 private items—1,522 in total, calculated from those reported splits. The broader suite contains 3,513 examples across four benchmarks. These are figures for that published benchmark snapshot, not a forecast for every model, release, or visual task. Google DeepMind’s FACTS Benchmark Suite.
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Video hallucination and frame sampling
HAVEN, a 2025 video-hallucination benchmark by Gao and colleagues, contains 6,497 questions and reports evaluations across 16 models. It examines factors including video duration, frame count, and question length. In the tested settings, increasing duration or frame count initially improved performance, but performance declined beyond a point. That is a benchmark-specific result, not a universal instruction to use more or fewer frames. The HAVEN paper on arXiv.
Fixed-set scores versus future cases
NIST’s AI 800-3 report, published February 17, 2026, distinguishes accuracy on a fixed benchmark from generalized accuracy across similar potential test items. In practical terms, a score on a known set cannot, by itself, establish how a model will perform on all future examples. Use benchmark results as bounded evidence and test your own representative cases. NIST AI 800-3.
How to compare models fairly
Use the same cases and conditions for every model. Compare more than an overall score:
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- Factual correctness: How often are claims right for each modality and task?
- Grounding: Does the answer match the supplied image or video, or add unsupported details?
- Consistency: Do matched variants and repeated runs produce similar results?
- Video sensitivity: Do duration and frame sampling change the outcome?
- Uncertainty: Does the model flag ambiguity or abstain when evidence is insufficient?
- Reproducibility: Can you identify the model version, settings, prompt, and media used?
Keep results broken down by modality and task. An aggregate can hide a weak spot, while one test item cannot establish a general pattern.
How to report a discrepancy responsibly
Include the exact prompt, media, model version and settings, and the conflicting outputs. State whether the input was accepted and, for video, what duration or frame-processing details are known. Identify which claims you verified against the media and which remain uncertain. Limit the conclusion to the cases you tested; no single prompt or setting is established as a universal fix for inconsistent multimodal answers.
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