Skip to content
#05SAug 12, 2026Evidence A

Kimi K3

Moonshot AI · 2.8T total (MoE, open)

The engineer’s bet: long context plus native vision

K3 brings native vision into the main model: CharXiv 84.8%, MathVision 94.3%, plus 2M context — built for reading a whole manual and its screenshots together.

TextImageVideoGUINative

Last updated Oct 18, 2026Editorial scoring

Bottom line

K3 shines not at captioning but at reading, calculating and patching code. For engineering docs, paper-figure reproduction and huge screenshot-heavy repos it beats generalists; image generation and everyday Chinese scenes are not its home turf.

Eight-dimension breakdown

editorial mapping · 0–100
Perception95Document92Chart QA93Video90Agent95Generation84Chinese93Ecosystem908D
  • Kimi K392.3
Visual perception & grounding20%
95
Document parsing & Chinese OCR18%
92
Chart & document reasoning14%
93
Video & long-horizon reasoning14%
90
GUI agents & tool use14%
95
Generation & editing6%
84
Chinese context & culture6%
93
Ecosystem, cost & availability8%
90

Weights: perception 20 / documents 18 / charts 14 / video 14 / agents 14 / generation 6 / Chinese 6 / ecosystem 8. Definitions and sources on the methodology page.

Public benchmarks

as reported, not converted
Benchmark / metricValueNote
CharXiv84.8Chart understanding
MathVision94.3Visual math reasoning
上下文2MOfficial spec

Public scores come from different versions and prompts and are not interchangeable with our composite; they are one evidence input.

Scenario mapping

five stress tests, one scale
ScenarioScoreObservation
Chinese receipt OCR90 · Clean docs fine; handwriting average
Chart data extraction95 · Excellent on paper-grade charts
30-min video QA88 · Usable, not best-in-class
Mobile GUI control94 · Strong cross-app planning
Chinese cultural context92 · Factual; weaker on memes

Strengths and limitations

Strengths

  • 2M context plus native vision reads a whole manual in one pass.
  • Strong chart and math visual reasoning for paper reproduction.
  • Open weights enable deep fine-tuning and on-prem use.

Limitations

  • Weak generation/editing; not for creative design pipelines.
  • Self-hosting 2.8T weights is impractical; open here means auditable.
  • Slow at peak; long-context requests are costly.

Price and real cost

List price

¥16/M tokens

≈ ¥16 / 1M output tokens. For 1,000 images at 1,024px, ~1,100 image tokens each and 300 output tokens, the monthly cost is about ¥9.7 (assuming input at 25% of output price).

Use your volume

About Kimi K3

Is Kimi K3 open source?

Licence: Open weights (community license). Weights are available, but check the exact terms and filing requirements before commercial use.

How much does Kimi K3 cost?

≈ ¥16 / 1M output tokens. It ranks #5 with a composite of 92.3. Real bills depend on resolution, tiling, caching and volume discounts.

What is Kimi K3 best for?

Engineering manuals and API docs QA; Paper-figure reproduction and code screenshots; Very long multimodal knowledge bases。

Why does Kimi K3 score 92.3?

The composite weights eight dimensions: perception 20%, documents 18%, charts 14%, video 14%, agents 14%, generation 6%, Chinese 6%, ecosystem 8%. Its strongest dimension is Visual perception & grounding; its weakest is Generation & editing.

Personal ranking · Not an official score

Every ranking, score and comparison on this page is an independent personal ranking, not an official score, and represents no vendor, institution or regulator. Scores are a subjective mapping of public evidence under our own weights — change the weights and the order changes. Disagree, or spotted a factual error? Contact the site owner: jizhi2024@foxmail.com — we check each report and log corrections in the changelog.