Compare/Devin vs GLM-5V-Turbo

AI tool comparison

Devin vs GLM-5V-Turbo

Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.

D

Developer Tools

Devin

Autonomous AI software engineer by Cognition

Skip

33%

Panel ship

Community

Paid

Entry

Devin is an autonomous AI agent that can plan, code, debug, and deploy entire features independently. It operates in its own sandboxed environment with terminal, editor, and browser. Targets long-running, complex engineering tasks.

G

Developer Tools

GLM-5V-Turbo

Turn wireframes into production code — 200K context, scores 94.8 on Design2Code

Ship

75%

Panel ship

Community

Paid

Entry

GLM-5V-Turbo is a multimodal vision-language model from Zhipu AI (international brand: Z.ai) purpose-built for converting visual designs into executable code. Released April 3, 2026, it's optimized specifically for the design-to-code pipeline that's becoming central to AI-assisted frontend development. The model features a 200K token context window with 128K max output — enough to hold an entire design system plus generate substantial implementation code in a single call. Input support spans images, video, and text. The CogViT vision encoder was trained from scratch alongside the language model rather than bolted on post-training, which Zhipu claims is why it achieves 94.8 on the Design2Code benchmark vs. Claude Opus 4.6's 77.3 (their own testing). GUI agent workflows are a first-class use case, with strong results on AndroidWorld and WebVoyager benchmarks. Pricing is competitive at $1.20/M input tokens and $4/M output tokens, with free web access at chat.z.ai for exploration. For teams already doing design-to-code workflows with Figma exports and Claude, GLM-5V-Turbo is a direct challenger worth benchmarking — especially given the claimed 17-point lead on the primary evaluation.

Decision
Devin
GLM-5V-Turbo
Panel verdict
Skip · 1 ship / 2 skip
Ship · 3 ship / 1 skip
Community
No community votes yet
No community votes yet
Pricing
$500/mo Team
$1.20/M input · $4/M output
Best for
Autonomous AI software engineer by Cognition
Turn wireframes into production code — 200K context, scores 94.8 on Design2Code
Category
Developer Tools
Developer Tools

Reviewer scorecard

Builder
45/100 · skip

At $500/mo it needs to replace at least 10 hours of developer time per month. In my testing, I spent more time reviewing and fixing its output than I saved. Not there yet.

80/100 · ship

A 17-point lead on Design2Code over Claude Opus, a 200K context window, and $4/M output pricing — that's a compelling combination for any team that's making Figma-to-code a production workflow. I'd run my own evals before fully committing, but the numbers are hard to ignore.

Skeptic
45/100 · skip

The marketing writes checks the product can't cash. 'Autonomous software engineer' implies reliability that doesn't exist. It's a talented intern that needs constant supervision.

45/100 · skip

Benchmark numbers from the lab that made the model are the weakest possible signal. Design2Code is also a narrow, academic benchmark — real production design-to-code involves design tokens, component libraries, and business logic that no benchmark captures. Verify independently before switching.

Futurist
80/100 · ship

Devin is early but directionally correct. The autonomous agent approach will win eventually. Cognition has the best shot at getting there first. Invest in the future, not the present.

80/100 · ship

Non-US labs that train vision and language from scratch together rather than compositing them are doing architecturally interesting work. GLM-5V-Turbo signals that the design-to-code paradigm is mature enough to warrant specialized models, which will accelerate the displacement of traditional frontend development.

Creator
No panel take
80/100 · ship

As someone who lives in Figma, having a model that genuinely understands design intent rather than just pixel positions is exciting. The 200K context means I could potentially load an entire component library and get contextually appropriate implementations rather than generic code.

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