Benchmark Almanac

KWV Bench

Kimi K3 64.7%
Only in system cards

Models answer atomic visual questions extracted from tasks distilled from real knowledge-work scenarios.

In-house Moonshot AI evaluation. The tasks are not public.

Made by
Moonshot AI
Released
July 2026
Kind
Documents, charts and text in images
Level
Expert
Scored
Checked automatically
Answers
Short answer
Tasks
Kept private
Strongest model tested
Kimi K3 #24 on the capability index
Last in a system card
July 2026
Last model added
July 2026
1
Model scored in lab cards
1
System card reports it, from 1 lab

Kimi K3 leads with 64.7%

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. 64.7 Kimi K3
  1. 1 Kimi K3 64.7%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Kimi K3 64.7% Jul 2026 Lab model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% Near the ceiling (85%) Kimi K3 0% 20% 40% 60% 80% 100% Near the ceiling (85%) Kimi K3

Source: Labs’ model cards

Labs report it only in system cards

We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

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.

DateDocumentScoreAs the document writes itSection
Jul 27, 2026 Kimi K3 technical report 64.7 KWV Bench (Table 3) Capabilities

More in Vision

All 281 in Vision
  • MMMU In use 11.5K college-level multimodal questions across 30 subjects. Multiple-choice and open accuracy. GPT-5.1 85.4%
  • MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. GPT-5.6 Sol 84.6%
  • CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
  • MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy. Qwen 3.8 Max 97.7%
  • MMStar In use Models answer 1,500 human-curated image questions that cannot be solved without the image, across 6 core capabilities. Scored by accuracy. Qwen 3.8 Max 85.9%
  • ChartQA In use Models answer human-written and generated questions about chart images that need visual and arithmetic reasoning. Scored by relaxed accuracy with a 5 percent numeric tolerance. Command A Vision 90.9%