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REVIEW 3 major objections 5 minor 2 cited by

MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A new 700-question map benchmark finds every tested AI model below 67% accuracy, over 20 points behind humans.

desk verdict A useful new geospatial reasoning benchmark, but the headline model-human gap rests on a human baseline that was measured under easier conditions than the models faced. read the letter →

arxiv 2501.00316 v2 pith:IQU7FBGG submitted 2024-12-31 cs.CL

Mahir Labib Dihan , Md Tanvir Hassan , Md Tanvir Parvez , Md Hasebul Hasan , Md Almash Alam , Muhammad Aamir Cheema , Mohammed Eunus Ali , Md Rizwan Parvez This is my paper · ORCID
classification cs.CL
keywords map-basedreasoninggeospatialAIbenchmarkfoundationmodelsspatialAPI-basedagentsvisualquestionansweringtripplanningunanswerablequestions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

MapEval is a benchmark of 700 multiple-choice questions drawn from real map data across 180 cities and 54 countries, testing foundation models in three modes: answering from pre-fetched map text, acting as an agent that calls map APIs, and reading map screenshots. Across 30 models, none surpasses 67% accuracy, while human performance is reported at 86.67% on the textual task and 82.23% on the visual task, implying a gap of more than 20 percentage points. The paper argues this gap concentrates in tasks that combine facts—multi-stop trip planning, straight-line distances, cardinal directions, counting points of interest, and recognizing unanswerable queries—rather than simple place recall. A sympathetic reading is that the benchmark now provides a concrete, reproducible yardstick for a largely untested capability that matters for navigation, logistics, and real-world tool use.

What carries the argument

The MapEval benchmark itself: 700 multiple-choice questions with ground truth derived from Google Maps, delivered in three coordinated modes—pre-fetched textual context, ReAct agents that call a controlled pseudo-Google Maps environment with cached API responses, and map snapshots for vision-language models. A second load-bearing mechanism is the tool-augmentation probe: a calculator implementing the Haversine formula and bearing computation, which isolates numeric geospatial computation from the language and reasoning components of the task.

What would settle it

Run the same 700 questions with a fresh, geographically diverse group of about 50 people who have not seen the dataset and who answer each question once; if their mean accuracy comes out below roughly 80% on textual and 75% on visual, the claimed over-20% model-human gap does not hold at that scale.

Watch

Extended reading notes

Core claim

The paper reports that current foundation models have not yet learned to reason over geospatial information the way map users do: the best model, Claude-3.5-Sonnet, reaches 66.33% on MapEval-Textual, 64.00% on MapEval-API, and 61.65% on MapEval-Visual, and every one of the 30 evaluated models trails the reported human scores by more than 20 points. The deficits are systematic: straight-line distance questions are answered correctly at only 51.06% by the best model, cardinal-direction accuracy varies wildly (16.67% to 91.67%), Trip planning tops out near 49%, and visual counting at a 47.73% best rate. The paper also shows that giving models a calculator for Haversine distance and bearing jumps straight-line distance accuracy from 51.06% to 85.11% and cardinal-direction accuracy to 95.83%, and that fine-tuning on 97 MCQs yields less than 5% improvement, indicating the difficulty is not merely missing task exposure.

Load-bearing premise

The human performance ceiling (86.67% textual, 82.23% visual) was measured from two team members' best attempts, so if those scores are higher than a typical unscreened user would achieve, the reported model-human gap shrinks.

Editorial extensions

If this is right

  • No current foundation model can reliably plan multi-stop trips: the best textual Trip accuracy is about 49%, far below the reported human 88%.
  • Models can read map text but cannot compute from coordinates: even the best straight-line distance accuracy is 51.06% without external tools.
  • API-based agents lose ground on Nearby and Routing relative to textual context (e.g., Claude-3.5-Sonnet drops from 73.49% to 55.42% on Nearby), so tool orchestration adds its own error.
  • Visual map reasoning degrades at zoom levels beyond 14, meaning map-specific visual complexity is an active bottleneck for VLMs.
  • Fine-tuning on 97 MapEval questions yields gains below 5%, suggesting the gap is not simply a lack of task exposure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the human baseline is confirmed by an independent, non-author participant pool, MapEval becomes a stable yardstick for geospatial AI; if not, the claimed over-20% gap may shrink.
  • The calculator result implies part of 'geospatial reasoning' is arithmetic disguised as reasoning; agents that invoke computational tools may close much of the gap without deeper spatial understanding.
  • MapEval's API and Unanswerable categories reward agents that know when to stop querying, a property that transfers beyond maps to robust tool use.
  • The MCQ format likely bounds reported accuracy relative to open-ended interaction, and the paper's own open-ended experiment shows evaluation is noisier, so absolute numbers should be read as MCQ-specific.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces MapEval, a benchmark of 700 multiple-choice questions for evaluating geo-spatial reasoning in foundation models across three tasks: textual (MapEval-Textual), API-based (MapEval-API), and visual (MapEval-Visual). The dataset spans 180 cities and 54 countries and covers categories such as place info, nearby, routing, trip planning, counting, and unanswerable queries. The authors evaluate 30 proprietary and open-source models and report that none surpasses 67% accuracy, with all models lagging over 20% behind human performance. They also provide qualitative error analyses and experiments with tool integration (calculator, Chameleon) that improve performance on specific subskills.

Significance. If the benchmark and its evaluation protocol are sound, MapEval is a valuable contribution to an under-explored area of geospatial reasoning, offering broad geographic coverage, multiple modalities, and a reproducible pseudo-API environment that supports controlled agent evaluation. The release of data and code is a strength. However, the headline claim of a >20% model-human gap depends on a human baseline that is not measured under conditions matched to the model protocols, and the accuracy tables report only point estimates without uncertainty quantification. These issues are load-bearing because the gap is the paper's central quantitative claim; they are fixable with additional data collection and analysis, so the manuscript should be revised rather than rejected.

major comments (3)
  1. [Tables 3, 4, and 5] The human baseline is not an apples-to-apples comparison with the model evaluation protocols. First, the two raters are author-affiliated team members and their reported score is their 'highest-scoring attempts,' allowing multiple tries per question, whereas each model receives a single deterministic pass. Second, for MapEval-API, the paper states that the same human score as MapEval-Textual is used, yet the API agents receive no textual context and must interact with tools; a valid human baseline must be measured with the same tool-only interface, not with pre-fetched textual contexts. Third, for MapEval-Visual, the protocol does not specify whether human raters are restricted to the static snapshot or can access the live or zoomable Google Maps URL that accompanies each image; if the latter, they have strictly more information than the VLM. Because the abstract and conclusion assert a >20% gap, these protocol mismatches could materially inflate the reported human performance and shrink or erase the headline gap. I recommend re-measuring human performance with independent, non-author raters under conditions strictly matched to each task (static image for visual, tool-only for API), reporting per-rater scores and all attempts rather than the maximum.
  2. [Appendix B.2, Filtering via LLMs] All accuracy results are reported as point estimates without confidence intervals or significance tests. With 300–400 items per task, the standard error of a proportion is roughly 2–3 percentage points, so differences of a few points (e.g., Claude-3.5-Sonnet 61.65 vs. Qwen2.5-VL-72B 60.35 in Table 5) are not meaningful. The central claim that 'none surpasses 67%' rests on top scores of 66.33 and 64.00, which are within sampling error of 67. Where the paper makes comparative claims such as 'outperforming GPT-4o and Gemini-1.5-Pro by 16% and 21%' (Section 4.2.2), the authors should provide confidence intervals or significance tests, or at minimum discuss the precision of these differences. I recommend adding Wilson intervals to Tables 3–5 and avoiding over-interpretation of small gaps.
  3. [Section 3.3] The dataset construction removes questions that 'the majority of the LLMs could easily provide the correct answer.' This design choice is defensible for building a challenging benchmark, but it directly affects the interpretation of the model-human gap: since human raters are evaluated on the filtered set, the reported human accuracy (86.67% textual, 82.23% visual) is measured on a set specifically selected to be hard for LLMs. The paper should quantify how many questions were removed, analyze the difficulty distribution before and after filtering, and discuss whether the >20% gap reflects a fundamental limitation of models rather than a benchmark calibration artifact. This is not a fatal flaw, but it is necessary to support the 'real-world' framing and to allow readers to judge the magnitude of the gap.
minor comments (5)
  1. [Table 5] The model name 'Claude-3-5-Sonnet' appears with a hyphen in Table 5 and elsewhere, while the rest of the paper uses 'Claude-3.5-Sonnet'; please standardize.
  2. [Abstract] The phrase 'most comprehensive evaluation framework for geospatial AI' is a strong claim that goes beyond the evidence presented, especially given the restriction to five Google Maps APIs and the filtering of easy questions; consider tempering the wording.
  3. [Section 3.3] The sentence 'For MapEval-API, as the questions are identical to MapEval-Textual, we report the same human performance for both' is the source of the major concern above, but even if the human score is reused by design, this should be explicitly flagged as a limitation and discussed.
  4. [Appendix G.2] The open-ended evaluation uses an automated grader (o3-mini) that the paper itself notes is unreliable for the Unanswerable category; this is a useful caveat, but the open-ended results are not used in the main claims, so the observation does not affect the main conclusions.
  5. [Figure 2] The percentages in the two pies each sum to 100%, but the visual and textual/API subcategories are not directly comparable; a note that the two pies represent disjoint subsets of 400 and 300 questions would help readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MapEval is an empirical benchmark evaluation; model and human accuracies are measured, not derived from each other or from the benchmark's construction choices.

full rationale

The paper's central claims are empirical measurements on a newly constructed benchmark, not derivations from an input assumption. The model accuracy numbers (Tables 3-5) are obtained by running 30 foundation models on MapEval items, and the human baselines (86.67% textual, 82.23% visual) are measured by independent team members, so the reported model-human gap is a comparison of two measurements rather than a quantity forced by construction. The benchmark construction does include a self-citation to the authors' MapQaTor tool for collecting textual map data and an LLM-based filtering step that removes 'too easy' items, but neither step defines the reported accuracies: the evaluation scores are external outcomes against the released dataset. The human-baseline protocol (highest-scoring attempts, same textual human score reused for the API task) raises validity questions about the size of the claimed gap, but those are measurement-validity concerns, not circularity: no equation, fitted parameter, or self-cited theorem in the paper makes the model scores equal to the inputs of the benchmark construction. No circular step can be exhibited under the required standard.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The benchmark introduces no free parameters fitted to data. It relies on several domain and design assumptions: Google Maps as ground truth, the author-team human baseline, the pseudo-API environment, and the LLM-based difficulty filter. These are reasonable but should be stated and, where possible, validated.

assumptions (4)
  • domain assumption Google Maps data (place details, hours, routes, ratings) is accurate and treated as ground truth.
    Dataset construction in Section 3.3 and Appendix B relies on MapQaTor fetching Google Maps API data; any errors propagate to questions and answers.
  • domain assumption Human performance measured by two non-annotator team members, taking their best attempt, is a fair human ceiling.
    Section 3.3 states 'two team members who did not participate in the annotation process attempt to answer the questions, and their highest-scoring attempts are reported.' Team members may be familiar with map reasoning or benchmark design, potentially inflating the human baseline.
  • ad hoc to paper Cached responses and simulated API interactions in the pseudo-Google Maps environment reflect real-world map API behavior.
    Appendix C.1 states the proxy 'mimics actual API interactions' and caches over 13,000 locations; however, realism of travel times and place lists is assumed, not validated against live APIs.
  • ad hoc to paper Removing questions that most LLMs answer correctly yields a valid difficulty distribution rather than overfitting the benchmark to model weaknesses.
    Appendix B.2 describes filtering out samples where 'the majority of the LLMs could easily provide the correct answer.' This design choice directly shapes the reported low accuracies and is not independently justified.

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Cite this review

Pith. "Pith review of MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models." pith.science (2026). https://pith.science/paper/IQU7FBGG

@misc{pith2026250100316,
  author       = {Pith},
  title        = {Pith review of: MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IQU7FBGG}},
  note         = {Machine review of arXiv:2501.00316}
}
read the original abstract

Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we introduce MapEval, a benchmark designed to assess foundation models across three distinct tasks - textual, API-based, and visual reasoning - through 700 multiple-choice questions spanning 180 cities and 54 countries, covering spatial relationships, navigation, travel planning, and real-world map interactions. Unlike prior benchmarks that focus on simple location queries, MapEval requires models to handle long-context reasoning, API interactions, and visual map analysis, making it the most comprehensive evaluation framework for geospatial AI. On evaluation of 30 foundation models, including Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro, none surpass 67% accuracy, with open-source models performing significantly worse and all models lagging over 20% behind human performance. These results expose critical gaps in spatial inference, as models struggle with distances, directions, route planning, and place-specific reasoning, highlighting the need for better geospatial AI to bridge the gap between foundation models and real-world navigation. All the resources are available at: https://mapeval.github.io/.

Figures

Figures reproduced from arXiv: 2501.00316 by the authors.

Figure 1
Figure 1. Overview of MapEval. On the left, we show the annotation process, where an expert gathers either visual snapshots or textual data from Google Maps to create multiple-choice questions with ground truth labels. On the right, we depict the evaluation process and input/output for the three benchmark tasks in MapEval. lenging for state-of-the-art foundation models. Comprising 700 unique multiple-choice questions across 1… view at source ↗
Figure 2
Figure 2. MapEval category statistics. team, achieving an initial 76% mutual agreement. At least two team members then manually verify and resolve any dis￾putes on the remaining pairs; if consensus cannot be reached (i.e., ambiguous), that pair is filtered out. To compute hu￾man scores, two team members who did not participate in the annotation process attempt to answer the questions, and their highest-scoring attempts are re… view at source ↗
Figure 3
Figure 3. Comparison between MapEval-Textual and MapEval￾API . 4.2.3. MAPEVAL-VISUAL We evaluate models on the MapEval-Visual task in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Screenshot of our Annotator Interface: MapQaTor C. Evaluation Details C.1. Pseudo-Google Maps Environment To ensure consistency between annotation and evaluation, a pseudo-Google Maps environment was developed with the following features: • Caching: Information for ove…
Figure 6
Figure 6. Figure 6: Distribution of Question Lengths (700 samples) [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Geographical Distribution of Textual and Visual Contexts. The left heatmap (a) represents the locations of places mentioned in textual contexts, while the right heatmap (b) shows the locations derived from map snapshots in visual contexts. F.1. Zoom Details In our data…
Figure 8
Figure 8. Figure 8: Zoom level 1 [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 10
Figure 10. Figure 10: Distribution of Zoom Levels (400 Visual Samples) We visualized the accuracy of various LLMs on these subcategories under the MapEval-Textual setting, with the following findings: 1. Straight-Line Distance [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Accuracy of LLMs on questions which needs Straight-Line Distance computation Claude-3.5-Sonnet Llama-3.1-70B Llama-3.2-90B GPT-4o Gemini-1.5-Pro GPT-4-Turbo Gemini-1.5-Flash Llama-3.1-8B Mistral-Nemo Qwen2.5-7B Gemma-2.0-9B Mixtral-8x7B GPT-4o-mini Phi-3.5-Mini GPT-3.…
Figure 12
Figure 12. Figure 12: Accuracy of LLMs on questions which needs Cardinal Direction computation These results highlight the limitations of current LLMs in handling fine-grained geospatial queries independently and emphasize the value of augmenting LLM capabilities with external computationa…
Figure 13
Figure 13. Figure 13: Accuracy of LLMs on Counting related questions Claude-3.5-Sonnet Llama-3.1-70B Gemini-1.5-Flash GPT-4o Llama-3.2-90B GPT-4-Turbo Mixtral-8x7B Gemini-1.5-Pro GPT-4o-mini Gemma-2.0-9B GPT-3.5-turbo 0 20 40 60 80 100 Accuracy (%) LLM LLM + Calculator [PITH_FULL_IMAGE:fi…
Figure 14
Figure 14. Figure 14: Improved accuracy of LLMs after integrating calculator to compute Straight-Line Distance setting. Thus, while MapEval fully supports open-ended evaluation, our MCQ format remains a more cost-effective and scalable solution for benchmarking LLMs. MapEval-Textual Evalua…
Figure 15
Figure 15. Figure 15: Improved accuracy of LLMs after integrating calculator to compute Cardinal Direction G.3. Fine-Tuning on MapEval To assess whether the challenges posed by MapEval stem merely from a lack of training exposure, we conducted additional experiments to fine-tune several op…
Figure 16
Figure 16. Figure 16: MapEval-Textual categorical accuracy 0-300 (52.33%) 301-600 (26.67%) 601-900 (9.67%) 901-1200 (5.67%) 1201-1500 (5.67%) 0 20 40 60 80 100 Context Length (data percentage) Accuracy (%) Claude-3.5-Sonnet Gemini-1.5-Pro GPT-4o GPT-4-turbo Llama-3.1-70B Gemini-1.5-Flash L…
Figure 17
Figure 17. Figure 17: Accuracy vs. Context Length (MapEval-Textual ) Place Info Nearby Routing Trip Unanswerable 0 20 40 60 80 100 Accuracy (%) Claude-3.5-Sonnet GPT-4-Turbo GPT-4o Gemini-1.5-Flash Gemini-1.5-Pro Llama-3.1-70B Llama-3.2-90B GPT-3.5-Turbo Mixtral-8x7B Gemma-2.0-9B GPT-4o-mi…
Figure 18
Figure 18. Figure 18: MapEval-API categorical accuracy 24 [PITH_FULL_IMAGE:figures/full_fig_p024_18.png]
Figure 19
Figure 19. Figure 19: Number of times agent stopped due to iteration limit H.3. MapEval-Visual [PITH_FULL_IMAGE:figures/full_fig_p025_19.png]
Figure 20
Figure 20. Figure 20: illustrates the performance of models on MapEval-Visual. Place info Nearby Routing Counting Unanswerable 0 20 40 60 80 100 Accuracy (%) Claude-3-5-Sonnet Gpt-4o Gpt-4-turbo Gemini-1.5-Pro Gpt-4o-Mini Gemini-1.5-Flash Qwen2.5-VL-72B Qwen2-VL-7B Llama-3.2-90B MiniCPM-V-…
Figure 21
Figure 21. Figure 21: Accuracy by Zoom Level. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs

    cs.LG 2026-02 unverdicted novelty 6.0 of 10

    MapTab is a new multimodal benchmark with 328 images and nearly 200k queries that shows current MLLMs have substantial difficulty with multi-criteria route planning when visual and tabular information must be combined.

  2. Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models

    cs.AI 2025-08 reject novelty 4.0 of 10

    A reinforcement-learning pipeline for predicting macro-actions in Honor of Kings improves action prediction accuracy, but the method is imitation of human replay labels, not the claimed environmental interaction.

Reference graph

Works this paper leans on

49 extracted references · 48 canonical work pages · cited by 2 Pith papers

  1. [1]

    Straight-Line Distance (47 questions): These questions require the computation of straight-line distances, such as ”What is the straight-line distance between the Atomium in Brussels and the Belfry of Bruges?”

    work page

  2. [2]

    Cardinal Direction (24 questions): These involve determining cardinal directions5, e.g., ”What is the direction of the Little Mermaid statue from Copenhagen Central Station?”

    work page

  3. [3]

    Zoom level 1 Figure 9

    Counting (23 questions): Questions involving counting entities, such as ”How many convenience stores are there within a 400 m radius of the Tokyo Tower?” 5https://en.wikipedia.org/wiki/Cardinal_direction 19 MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models Figure 8. Zoom level 1 Figure 9. Zoom level 16 3 6 9 12 15 18 21 0 30 60...

    work page

  4. [4]

    option_no

    310 km Calude-3.5-Sonnet: To answer this question, I’ll need to calculate the straight-line distance between the two points using their given coordinates. I’ll use the Haversine formula for this calculation. Eiffel Tower: (48.8584, 2.2945) Mont Saint-Michel: (48.6361, -1.5115) After performing the calculation, the approximate distance is 284 km. Based on ...

    work page

  5. [6]

    We can see that all models struggled, with the best accuracy being only 51.06%

    Straight-Line Distance: Figure 11, illustrates the accuracy on straight-line distance related questions. We can see that all models struggled, with the best accuracy being only 51.06%

    work page

  6. [7]

    Here LLMs showed significant variability

    Cardinal Direction: Figure 12, illustrates the accuracy on cardinal-direction related questions. Here LLMs showed significant variability. While Claude-3.5-Sonnet achieved 91% accuracy, Gemma-2.0-27B scored only 16.67%

    work page

  7. [8]

    option_no

    Counting: Figure 13, illustrates the accuracy on counting related questions. In this case, Claude-3.5-Sonnet underper- formed compared to the open-source Gemma-2.0-27B (60.87% accuracy). We identified a scope for improvement in these areas and enhanced the models’ capabilities by integrating external tools (e.g., a calculator) specifically designed for ca...

    work page

  8. [9]

    Big Pita - Open: Monday: 10:30AM-9:00PM , Tuesday: 10:30AM-9:00PM, Wednesday: 10:30AM-9:00PM, Thursday: 10:30AM-9:00PM, Friday: 10:30AM-9:00PM, Saturday: 10:30AM-9:00PM, Sunday: 10:30AM-9:00PM

    work page

Show all 49 references
  1. [10]

    Latitude: Seurasaari (60.1856) is slightly north of Helsinki Central Station (60.1719) 2

    South Calude-3.5-Sonnet: To determine the direction of Seurasaari Open-Air Museum from Helsinki Central Station, I’ll compare their coordinates: Seurasaari Open-Air Museum: 60.1856, 24.8846 Helsinki Central Station: 60.1719, 24.9414 Analyzing the coordinates: 1. Latitude: Seur...

  2. [11]

    A&W Canada - Open: Monday: Open 24 hours , Tuesday: Open 24 hours, Wednesday: Open 24 hours, Thursday: Open 24 hours, Friday: Open 24 hours, Saturday: Open 24 hours, Sunday: Open 24 hours

  3. [12]

    Quik Sushi - Open: Monday: 9:00AM-7:00PM , Tuesday: 11:30AM-7:30PM, Wednesday: 9:00AM-7:00PM, Thursday: 9:00AM-7:00PM, Friday: 9:00AM-7:00PM, Saturday: 9:00AM-7:00PM, Sunday: 9:00AM-7:00PM

  4. [14]

    Pizza Pizza - Open: Monday: 11:00AM-1:00AM , Tuesday: 11:00AM-1:00AM, Wednesday: 11:00AM-1:00AM, Thursday: 11:00AM-1:00AM, Friday: 11:00AM-2:00AM, Saturday: 11:00AM-2:00AM, Sunday: 11:00AM-1:00AM

  5. [17]

    Market Street Catch - Open: Monday: 11:00AM-8:00PM , Tuesday: 11:00AM-9:00PM, Wednesday: 11:00AM-9:00PM, Thursday: 11:00AM-9:00PM, Friday: 11:00AM-10:00PM, Saturday: 11:00AM-10:00PM, Sunday: 11:00AM-8:00PM

  6. [18]

    These are: A&W Canada (open 24 hours), Quik Sushi (open 9:00 AM - 7:00 PM), and Big Pita (open 10:30 AM - 9:00 PM)

    5 Calude-3.5-Sonnet: Based on the given information, 3 nearby restaurants are open on Monday. These are: A&W Canada (open 24 hours), Quik Sushi (open 9:00 AM - 7:00 PM), and Big Pita (open 10:30 AM - 9:00 PM). All other listed restaurants are either closed on Monday or their M...

  7. [19]

    American Wing Cafe (1000 5th Avenue, New York)

  8. [20]

    option_no

    Churrasco Of The St Lawrence - Open: Monday: Closed, Tuesday: 7:00AM-7:00PM, Wednesday: 7:00AM-7:00PM, Thursday: 7:00AM-7:00PM, Friday: 7:00AM -7:00PM, Saturday: 7:00AM-7:00PM, Sunday: 10:00AM-5:00PM. Query: How many nearby restaurants of St. Lawrence Market are open on Monday...

  9. [21]

    The Great Hall Balcony Cafe (1000 5th Avenue 2nd Floor, New York)

  10. [22]

    Petrie Court Cafe (1000 5th Avenue, New York)

  11. [23]

    option_no

    Starbucks (1142 Madison Avenue, New York) ..... Query: Today is Monday. I have an afternoon free in New York and plan to visit The Metropolitan Museum of Art for 3 hours, followed by a 30-minute coffee break at a nearby cafe, and then spend 1 hour in Central Park (New York, NY...

  12. [24]

    (3.00PM - 6.00PM) Visit The Metropolitan Museum of Art → (6.02PM - 6.32PM) Have coffee at The Great Hall Balcony Cafe → (6.38PM - 7.38PM) Spend time in Central Park

  13. [25]

    (3.00PM - 6.00PM) Visit The Metropolitan Museum of Art → (6.08PM - 6.38PM) Have coffee at Central Park cafe → (6.42PM - 7.42PM) Spend time in Central Park

  14. [26]

    (3.00PM - 6.00PM) Visit The Metropolitan Museum of Art → (6.05PM - 6.35PM) Have coffee at Starbucks → (6.39 PM - 7.39PM) Spend time in Central Park

  15. [27]

    Calude-3.5-Sonnet: Option 3 is the most suitable schedule based on the given information

    (2.00PM - 5.00PM) Visit The Metropolitan Museum of Art → (5.09PM - 5.39PM) Have coffee at Matto Espresso → (5.47PM - 6.47PM) Spend time in Central Park. Calude-3.5-Sonnet: Option 3 is the most suitable schedule based on the given information. The Metropolitan Museum of Art is ...

  16. [28]

    Via Damansara Link/Lebuhraya SPRINT/Sistem Penyuraian Trafik Kuala Lumpur Barat/E23 | 21 mins | 15.2 km - Head east on Persiaran Tropicana toward Persiaran Tropicana

  17. [29]

    Nearby Cafes of Petaling Jaya are (sorted by distance in ascending order):

    Via Damansara Link/Lebuhraya SPRINT/Sistem Penyuraian Trafik Kuala Lumpur Barat/E23 and Jalan Maarof | 25 mins | 15.2 km - Head east on Persiaran Tropicana toward Persiaran Tropicana .... Nearby Cafes of Petaling Jaya are (sorted by distance in ascending order):

  18. [30]

    - Rating: 5

    Dareia Coffee - Address: Ps2-13 Lumi Tropicana, Persiaran Tropicana, Tropicana, Petaling Jaya. - Rating: 5. (7 ratings). .... Nearby Cafes of Federal Territory of Kuala Lumpur are (sorted by distance in ascending order):

  19. [31]

    option_no

    Gwaku Coffee - Address: ULG Lobby Menara TH 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, Kuala Lumpur. - Rating: 4.2. (62 ratings). .... Query: Find a good coffee shop on the left side of my driving path from my home near Petaling Jaya to my office in Kuala Lumpur....

  20. [32]

    action" values:

    Coffeeloca Calude-3.5-Sonnet: The Lemon Tree is a good coffee shop option on the left side of the driving path from Petaling Jaya to Kuala Lumpur. It is located at the Ground Floor of Menara Lien Hoe, which is along Persiaran Tropicana. This cafe is on the initial part of the ...

  21. [33]

    action":

    No Calude-3.5-Sonnet: To answer this question accurately, I’ll need to check the opening hours of The Great Pyramid of Giza. Let me start by getting the place ID for the location. Action: ‘‘‘ { "action": "PlaceSearch", "action_input": { "placeName": "The Great Pyramid of Giza"...

  22. [34]

    action":

    Four Calude-3.5-Sonnet: To answer this question accurately, I’ll need to calculate the travel times between these locations and the time spent at each place. Let’s break this down step by step. First, I’ll need to get the place IDs for all the locations mentioned. Action: ‘‘‘ ...

  23. [35]

    Khaosan Tokyo Origami to Senso-ji Temple: 4 minutes

  24. [36]

    Time at Senso-ji Temple: 1 hour

  25. [37]

    action":

    Total time so far: 1 hour 4 minutes At this point, it’s 8:04 am, and we’ve used 1 hour 4 minutes of the available 3.5 hours. The next destination, Shibuya Crossing, is 2 hours 52 minutes away on foot, which would exceed the remaining time (2 hours 26 minutes) even before spend...

  26. [38]

    Baltusrol Golf Club

    Baltusrol Golf Club 3.East Orange Golf Course 4.Reeves-Reed Arboretum Gemini-1.5-Pro: The golf club located at the intersection of Springfield and Houdaille Quarry is "Baltusrol Golf Club". You can clearly see the label for Baltusrol Golf Club positioned right above Springfiel...

  27. [39]

    Maison William Caldwell Cottingham

    Circle Stone 2.secret pool 3.Maison William Caldwell Cottingham 4.Poste de cavalerie du Service de police de la Ville de Montreal Gemini-1.5-Pro: The correct answer is "Maison William Caldwell Cottingham". The map shows the location of "Mount Royal Park". Just to the east of t...

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    Secondary school No. 25

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    67 TOMAS ESPORA

    PROVINCIAL SCHOOL No. 67 TOMAS ESPORA

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    Province of Buenos Aires SCHOOL No. 507

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    0) Unanswerable

    SCHOOL No.5 Province of Buenos Aires Gemini-1.5-Pro: The answer is "0) Unanswerable". The map provided does not show any schools. Therefore, it is impossible to determine which school is closed to Club Atletico Puerto Comercial based solely on the provided image. GPT-4o-mini: ...

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    23 km". Therefore, the correct answer is

    27.6km Gemini-1.5-Pro: The image clearly shows the total distance as "23 km". Therefore, the correct answer is "1) 23 km". GPT-4o-mini: Based on the provided information and the image, the distance from Narayana Hospital, Chunavati, Howrah, to Rajpur Sonarpur is approximately ...

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    1. Square Hospital

    Bangladesh Medical College Hospital 4.IBN Sina Specialized Hospital Gemini-1.5-Pro: The best option is "1. Square Hospital". Here’s why: Familiarity: Staying with a healthcare provider you’re familiar with is generally a good idea, especially for something like checking blood ...

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    Proximity: IBN Sina Specialized Hospital is located close to Abahani Club Field, making it convenient to access right after your game

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    Square Hospital

    Alternatives: The other options may be farther away or may not be as specialized for such treatments. Calude-3.5-Sonnet: The most suitable option for checking your blood pressure after playing at Abahani Club Field would be: "Square Hospital". Explanation:

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    You mentioned that you usually get your treatment at Square Hospital, which is visible on the map in the upper right corner

  37. [49]

    Square Hospital is relatively close to both Hotel Nandini (where you’re staying) and Abahani Club Field (where you’ll be playing)

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    Llama-3-VILA1.5-8B: The answer is 4

    While Bangladesh Medical College Hospital and IBN Sina Specialized Hospital are also visible on the map and are valid medical facilities, Square Hospital is your usual treatment location, making it the most suitable choice for continuity of care. Llama-3-VILA1.5-8B: The answer...

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    1) Car Museum

    Car Museum 2.Sheikh Abdulla bin Jassim Al-Thani Palace 3.National Museum of Qatar Playgrounds 4.National Museum of Qatar Gemini-1.5-Pro: The correct option is "1) Car Museum". From the image, you can see the "Qatar Flagpole" location marked. The **"Car Museum" ** is located ju...

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    {Place A} serves dinner, lunch, vegetarian food

    Springer, 2007. Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., et al. Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923, 2025. Balsebre, P., Huang, W., and Cong, G. Lamp: A language model on the map. CoRR, 2024. Basta...

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    URL https://mistral.ai/news/ mistral-nemo/. AI@Meta. Llama 3 model card. 2024. URL https://github.com/meta-llama/llama3/ blob/main/MODEL_CARD.md. Anthropic. Claude 3.5 sonnet. https://www. anthropic.com/news/claude-3-5-sonnet , 2024. Auer, S., Bizer, C., Kobilarov, G., Lehmann...

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.