LLM Reference
GAOKAO-MMactive

GAOKAO-MM

Metric: Accuracy (higher is better)Introduced: 2024

About

Multimodal extension of GAOKAO with text+image questions from Chinese college entrance exams, evaluating vision-language model capabilities.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.

How to read this benchmark

This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.

Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.

Trust when:

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • Model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Don't trust when:

  • There is only one benchmark snapshot or the dataset appears stale.
  • Benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

Current modeled score band for tracked entries is roughly 48.1 – 18.0.

Window

Feb 23

single timestamp

Mean delta

No trend

need another snapshot

Coverage

7

models in latest snapshot

Leaderboard preview (top 5)

  1. 1. GPT-4 Vision Preview48.1
  2. 2. Qwen-VL-Plus41.2
  3. 3. Gemini 1.0 Pro Vision35.1
  4. 4. InternLM XComposer2 VL 7B33.2
  5. 5. LLaVA 1.5 13B18.0