WorldVQA
Qwen3.7-Plus 61.1% leads since Jun 2026 In use Model names visual entities in 3,500 image-question pairs across nine categories, isolating memorized visual world knowledge. Exact-answer accuracy.
Lab-created public benchmark (Moonshot AI). Kimi K3 reports a 'ForceAnswer' variant.
In the authors’ words
We introduce WorldVQA, a benchmark designed to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs). Unlike current evaluations, which often conflate visual knowledge retrieval with reasoning, WorldVQA decouples these capabilities to strictly measure "what the model memorizes." The benchmark assesses the atomic capability of grounding and naming visual entities across a stratified taxonomy, spanning from common head-class objects to long-tail rarities. We expect WorldVQA to serve as a rigorous test for visual factuality, thereby establishing a standard for assessing the encyclopedic breadth and hallucination rates of current and next-generation frontier models.
- Level
- University Undergraduates and entry-level professionals
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Short answer A number, word or short string matched to a key.
Qwen3.7-Plus leads with 61.1%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Qwen3.7-Plus 61.1%
- 2
Step 3.7 Flash 58.1% - 3 Qwen 3.8 Max 53.2%
- 4
Seed2.1 Pro 53% - 5 Kimi K3 51%
- 6 Seed2.0 Lite (0428) 50.2%
- 7 Seed2.0 Pro 49.9%
- 8
Muse Spark Open 49.4% - 9 Kimi K2.5 46.3%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Qwen3.7-Plus | 61.1% | Jun 2026 | Model card | |
| 2 | Step 3.7 Flash | 58.1% | May 2026 | Model card | |
| 3 | Qwen 3.8 Max | 53.2% | Aug 2026 | Model card | |
| 4 | Seed2.1 Pro | 53% | Jun 2026 | Model card | |
| 5 | Kimi K3 | 51% | Jul 2026 | Model card | |
| 6 | Seed2.0 Lite (0428) | 50.2% | Apr 2026 | Model card | |
| 7 | Seed2.0 Pro | 49.9% | Feb 2026 | Model card | |
| 8 | Muse Spark Open | 49.4% | Oct 2026 | Model card | |
| 9 | Kimi K2.5 | 46.3% | Jan 2026 | Model card |
5 models have held number one since its release in Jan 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
4 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 15 Oct 2026 | Muse Spark Open | 49.4 | WorldVQA (w/ tools) |
| 3 Aug 2026 | Qwen 3.8 Max | 53.2 | WorldVQA |
| 16 Jul 2026 | Kimi K3 | 51 | WorldVQA ForceAnswer |
| 23 Jun 2026 | Seed2.1 Pro | 53 | WorldVQA |
| 1 Jun 2026 | Qwen3.7-Plus | 61.1 | WorldVQA (with search) |
| 29 May 2026 | Step 3.7 Flash | 58.1 | WorldVQA |
| 28 Apr 2026 | Seed2.0 Lite (0428) | 50.2 | WorldVQA |
| 27 Jan 2026 | Kimi K2.5 | 46.3 | WorldVQA |
4 system cards report it, from 2 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 27 Jul 2026 | Kimi K3 technical report | 51 | WorldVQA ForceAnswer (Table 2) | Capabilities |
| 23 Jun 2026 | Seed2.1 Pro model card | 53 | WorldVQA (Table 9) | Capabilities |
| 14 Feb 2026 | Seed2.0 Pro model card | 49.9 | WorldVQA (Table 8) | Capabilities |
| 30 Jan 2026 | Kimi K2.5 technical report | 46.3 | WorldVQA (Table 4) | Capabilities |
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