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The Satellite Geo QCM leaderboard, as described in a September 22 DEV Community article by RESK, tests whether a model can select a location from a satellite or aerial image. Each question has four choices; the model’s selected answer is marked right or wrong, with no LLM judge. The published figures are the article’s reported snapshot, not verified live leaderboard values.

What a model is asked to do

Each described item pairs an aerial or satellite image with one question asking the model to choose the correct location from four options. The article says wording and options stay fixed across models, with no hints, retries, or alternate framing. This makes the comparison more controlled for this particular multiple-choice task than an open-ended task whose responses require subjective grading.

The available description does not establish the dataset’s size, image provenance, geographic coverage, train/test or contamination controls, or the exact rubric used to assign difficulty. Those details matter when judging how representative the results are.

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How answers are scored and displayed

RESK describes scoring as deterministic: a selected option is compared with the answer key and marked right or wrong. There is no LLM judge evaluating free-form text. The article says results are shown as overall accuracy and accuracy by difficulty, alongside a public verbatim response trail where readers can inspect the image, choices, and model answer.

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That trail can help readers examine apparent errors. For example, a wrong answer might reflect difficulty recognizing the location, or a mistake in reading or selecting an option. The article does not present a formal error taxonomy, so those are possible interpretations rather than established categories.

What the reported scores mean

The article’s leaderboard table reports the following accuracy percentages. These are claims from that article’s snapshot, not independently confirmed current rankings.

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DeepSeek V4 Flash Vision (exp) 98.18% Reported in RESK’s DEV Community article, posted September 22; the year is not printed on the opened page.
GLM 5.2 78.0% Reported in the same table; RESK identifies this as their own submission.

A score of 98.18% means that the article reports the model selecting the answer key’s correct location on 98.18% of the evaluated questions. It does not mean the model is 98.18% accurate at satellite-image analysis generally, nor does it establish performance on other image types or real-world geospatial work.

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What the leaderboard can—and cannot—establish

Fixed inputs and mechanical scoring make results easier to compare within the described setup. They do not prove that every source of variance or bias has been removed. The material available does not provide enough implementation detail to independently audit dataset construction, geographic balance, submission controls, or whether models may have encountered the items during training.

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The article itself cautions that the task does not measure reasoning, instruction following, or open-ended vision. Since each item offers four choices, guessing is possible. A headline score is therefore most useful when read alongside the difficulty breakdown and response trail, and when the task resembles the reader’s intended use.

  • Compare overall and difficulty-level accuracy: an aggregate can hide weaker performance on harder questions.
  • Inspect individual responses: the trail may reveal patterns that a single percentage cannot, though it does not supply a formal error classification.
  • Check task fit: success at choosing among four locations is not equivalent to interpreting arbitrary satellite imagery or carrying out geospatial analysis.
  • Check the snapshot context: the cited figures are not verified live values, and the retrieved description does not establish the current leaderboard status.
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How this differs from another geospatial benchmark

The Cloud-Based Geospatial Benchmark (CBGB) is a separate evaluation and should not be treated as another name for Satellite Geo QCM. A 2025 paper describes CBGB as 45 expert-curated Earth-observation scenarios in which LLM agents generate code to produce short numerical answers. It also compares runs with and without feedback from the execution environment. The paper reports that execution feedback generally helped, that its highest reported performance was 71%, and that reasoning variants outperformed non-thinking variants in that study. These results concern CBGB, not Satellite Geo QCM’s four-option image questions.

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