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#32BMay 20, 2026Evidence B

Cloudwalk Congrong 2

Cloudwalk · Undisclosed (CV lineage; vision-tower led)

The only CV “little dragon” still shipping multimodal

The pivot product of one of the CV “little dragons”: perception 91 is its old trade, everything else is support. Composite 76.1, ranked 32nd — the lowest here — yet it remains steady on recognition-rather-than-understanding tasks like faces, receipts and ID documents. It is the only model of 32 whose perception exceeds its own composite by more than 15 points.

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Last updated Oct 18, 2026Editorial scoring

Bottom line

For recognition tasks — faces, ID documents, receipts — Congrong 2 is a cheap, steady option (¥7, perception 91). For understanding, reasoning or generation, do not pick it. Its existence is a mirror: comparisons assume “multimodal = understanding”, while much real procurement buys “recognition” — and recognition has no leaderboard, only acceptance tests.

Eight-dimension breakdown

editorial mapping · 0–100
Perception91Document78Chart QA72Video68Agent70Generation65Chinese85Ecosystem688D
  • Cloudwalk Congrong 276.1
Visual perception & grounding20%
91
Document parsing & Chinese OCR18%
78
Chart & document reasoning14%
72
Video & long-horizon reasoning14%
68
GUI agents & tool use14%
70
Generation & editing6%
65
Chinese context & culture6%
85
Ecosystem, cost & availability8%
68

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
人像识别误识率1e-6Vendor figure
输出单价¥7Industry pricing

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 OCR80 · Steady recognition, average layout
Chart data extraction68 · Not a target scenario
30-min video QA62 · No video modality
Mobile GUI control64 · Not trained for GUI
Fine-grained recognition91 · Same as our perception dimension

Strengths and limitations

Strengths

  • Perception 91: face, ID and receipt recognition stability remains near the front.
  • Deep industry channels: bank branches, public security and campus delivery networks are ready-made.
  • Moderate pricing: ¥7 industry pricing, below most score peers.

Limitations

  • Composite 76.1 is the lowest here: weak across the board except perception.
  • No video modality — long-horizon tasks are absent outright.
  • Ecosystem 68: SDK and docs target industry customers; poor developer experience.

Price and real cost

List price

¥7/M tokens

≈ ¥7 / 1M output tokens (industry pricing). For 1,000 images at 1,024px, ~1,100 image tokens each and 300 output tokens, the monthly cost is about ¥4.2 (assuming input at 25% of output price).

Use your volume

About Cloudwalk Congrong 2

Is Cloudwalk Congrong 2 open source?

Licence: Closed commercial. No public weights; access is via vendor API or an on-prem licence.

How much does Cloudwalk Congrong 2 cost?

≈ ¥7 / 1M output tokens (industry pricing). It ranks #32 with a composite of 76.1. Real bills depend on resolution, tiling, caching and volume discounts.

What is Cloudwalk Congrong 2 best for?

ID verification and receipt recognition; Bank-branch and campus recognition scenarios; Cost baseline for recognition tasks。

Why does Cloudwalk Congrong 2 score 76.1?

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.