Z.ai's open source GLM-Image beats Google's Nano Banana Pro at complex text rendering, but not aesthetics

The two big stories of AI in 2026 so far have been the incredible rise in usage and praise for Anthropic's Claude Code and a similar huge boost in user adoption for Google's Gemini 3 AI model family released late last year — the latter of which includes Nano Banana Pro (also known as Gemini 3 Pro Image), a powerful, fast, and flexible image generation model that renders complex, text-heavy infographics quickly and accurately, making it an excellent fit for enterprise use (think: collateral, trainings, onboarding, stationary, etc).

But of course, both of those are proprietary offerings. And yet, open source rivals have not been far behind.

This week, we got a new open source alternative to Nano Banana Pro in the category of precise, text-heavy image generators: GLM-Image, a new 16-billion parameter open-source model from recently public Chinese startup Z.ai.

By abandoning the industry-standard "pure diffusion" architecture that powers most leading image generator models in favor of a hybrid auto-regressive (AR) + diffusion design, GLM-Image has achieved what was previously thought to be the domain of closed, proprietary models: state-of-the-art performance in generating text-heavy, information-dense visuals like infographics, slides, and technical diagrams.

It even beats Google's Nano Banana Pro on the shared by z.ai — though in practice, my own quick usage found it to be far less accurate at instruction following and text rendering (and other users seem to agree).

But for enterprises seeking cost-effective and customizable, friendly-licensed alternatives to proprietary AI models, z.ai's GLM-Image may be "good enough" or then some to take over the job of a primary image generator, depending on their specific use cases, needs and requirements.

The Benchmark: Toppling the Proprietary Giant

The most compelling argument for GLM-Image is not its aesthetics, but its precision. In the CVTG-2k (Complex Visual Text Generation) benchmark, which evaluates a model's ability to render accurate text across multiple regions of an image, GLM-Image scored a Word Accuracy average of 0.9116.

To put that number in perspective, Nano Banana 2.0 aka Pro—often cited as the benchmark for enterprise reliability—scored 0.7788. This isn't a marginal gain; it is a generational leap in semantic control.

While Nano Banana Pro retains a slight edge in single-stream English long-text generation (0.9808 vs. GLM-Image's 0.9524), it falters significantly when the complexity increases.

As the number of text regions grows, Nano Banana's accuracy remains in the 70s, whereas GLM-Image maintains >90% accuracy even with multiple distinct text elements.

For enterprise use cases—where a marketing slide needs a title, three bullet points, and a caption simultaneously—this reliability is the difference between a production-ready asset and a hallucination.

Unfortunately, my own usage of a demo inference of GLM-Image on Hugging Face proved to be less reliable than the benchmarks might suggest.

My prompt to generate an "infographic labeling all the major constellations visible from the U.S. Northern Hemisphere right now on Jan 14 2026 and putting faded images of their namesakes behind the star connection line diagrams" did not result in what I asked for, instead fulfilling maybe 20% or less of the specified content.

But Google's Nano Banana Pro handled it like a champ, as you'll see below:

Of course, a large portion of this is no doubt due to the fact that Nano Banana Pro is integrated with Google search, so it can look up information on the web in response to my prompt, whereas GLM-Image is not, and therefore, likely requires far more specific instructions about the actual text and other content the image should contain.

But still, once you're used to being able to type some simple instructions and get a fully researched and well populated image via the latter, it's hard to imagine deploying a sub-par alternative unless you have very specific requirements around cost, data residency and security — or the customizability needs of your organization are so great.

Furthermore, Nano Banana Pro still edged out GLM-Image in terms of pure aesthetics — using the OneIG benchmark, Nano Banana 2.0 is at 0.578 vs. GLM-Image at 0.528 — and indeed, as the top header artwork of this article indicates, GLM-Image does not always render as crisp, finely detailed and pleasing an image as Google's generator.

The Architectural Shift: Why "Hybrid" Matters

Why does GLM-Image succeed where pure diffusion models fail? The answer lies in Z.ai’s decision to treat image generation as a reasoning problem first and a painting problem second.

Standard latent diffusion models (like Stable Diffusion or Flux) attempt to handle global composition and fine-grained texture simultaneously.

This often leads to "semantic drift," where the model forgets specific instructions (like "place the text in the top left") as it focuses on making the pixels look realistic.

GLM-Image decouples these objectives into two specialized "brains" totaling 16 billion parameters:

1. The Auto-Regressive Generator (The "Architect"): Initialized from Z.ai’s GLM-4-9B language model, this 9-billion parameter module processes the prompt logically. It doesn't generate pixels; instead, it outputs "visual tokens"—specifically semantic-VQ tokens. These tokens act as a compressed blueprint of the image, locking in the layout, text placement, and object relationships before a single pixel is drawn. This leverages the reasoning power of an LLM, allowing the model to "understand" complex instructions (e.g., "A four-panel tutorial") in a way diffusion noise predictors cannot.

2. The Diffusion Decoder (The "Painter"): Once the layout is locked by the AR module, a 7-billion parameter Diffusion Transformer (DiT) decoder takes over. Based on the CogView4 architecture, this module fills in the high-frequency details—texture, lighting, and style.

By separating the "what" (AR) from the "how" (Diffusion), GLM-Image solves the "dense knowledge" problem. The AR module ensures the text is spelled correctly and placed accurately, while the Diffusion module ensures the final result looks photorealistic.

Training the Hybrid: A Multi-Stage Evolution

The secret sauce of GLM-Image’s performance isn't just the architecture; it is a highly specific, multi-stage training curriculum that forces the model to learn structure before detail.

The training process began by freezing the text word embedding layer of the original GLM-4 model while training a new "vision word embedding" layer and a specialized vision LM head.

This allowed the model to project visual tokens into the same semantic space as text, effectively teaching the LLM to "speak" in images. Crucially, Z.ai implemented MRoPE (Multidimensional Rotary Positional Embedding) to handle the complex interleaving of text and images required for mixed-modal generation.

The model was then subjected to a progressive resolution strategy:

• Stage 1 (256px): The model trained on low-resolution, 256-token sequences using a simple raster scan order.

• Stage 2 (512px – 1024px): As resolution increased to a mixed stage (512px to 1024px), the team observed a drop in controllability. To fix this, they abandoned simple scanning for a progressive generation strategy.

In this advanced stage, the model first generates approximately 256 "layout tokens" from a down-sampled version of the target image.

These tokens act as a structural anchor. By increasing the training weight on these preliminary tokens, the team forced the model to prioritize the global layout—where things are—before generating the high-resolution details. This is why GLM-Image excels at posters and diagrams: it "sketches" the layout first, ensuring the composition is mathematically sound before rendering the pixels.

Licensing Analysis: A Permissive, If Slightly Ambiguous, Win for Enterprise

For enterprise CTOs and legal teams, the licensing structure of GLM-Image is a significant competitive advantage over proprietary APIs, though it comes with a minor caveat regarding documentation.

The Ambiguity: There is a slight discrepancy in the release materials. The model’s Hugging Face repository explicitly tags the weights with the MIT License.

However, the accompanying GitHub repository and documentation reference the Apache License 2.0.

Why This Is Still Good News: Despite the mismatch, both licenses are the "gold standard" for enterprise-friendly open source.

• Commercial Viability: Both MIT and Apache 2.0 allow for unrestricted commercial use, modification, and distribution. Unlike the "open rail" licenses common in other image models (which often restrict specific use cases) or "research-only" licenses (like early LLaMA releases), GLM-Image is effectively "open for business" immediately.

• The Apache Advantage (If Applicable): If the code falls under Apache 2.0, this is particularly beneficial for large organizations. Apache 2.0 includes an explicit patent grant clause, meaning that by contributing to or using the software, contributors grant a patent license to users. This reduces the risk of future patent litigation—a major concern for enterprises building products on top of open-source codebases.

• No "Infection": Neither license is "copyleft" (like GPL). You can integrate GLM-Image into a proprietary workflow or product without being forced to open-source your own intellectual property.

For developers, the recommendation is simple: Treat the weights as MIT (per the repository hosting them) and the inference code as Apache 2.0. Both paths clear the runway for internal hosting, fine-tuning on sensitive data, and building commercial products without a vendor lock-in contract.

The "Why Now" for Enterprise Operations

For the enterprise decision maker, GLM-Image arrives at a critical inflection point. Companies are moving beyond using generative AI for abstract blog headers and into functional territory: multilingual localization of ads, automated UI mockup generation, and dynamic educational materials.

In these workflows, a 5% error rate in text rendering is a blocker. If a model generates a beautiful slide but misspells the product name, the asset is useless. The benchmarks suggest GLM-Image is the first open-source model to cross the threshold of reliability for these complex tasks.

Furthermore, the permissive licensing fundamentally changes the economics of deployment. While Nano Banana Pro locks enterprises into a per-call API cost structure or restrictive cloud contracts, GLM-Image can be self-hosted, fine-tuned on proprietary brand assets, and integrated into secure, air-gapped pipelines without data leakage concerns.

The Catch: Heavy Compute Requirements

The trade-off for this reasoning capability is compute intensity. The dual-model architecture is heavy. Generating a single 2048×2048 image requires approximately 252 seconds on an H100 GPU. This is significantly slower than highly optimized, smaller diffusion models.

However, for high-value assets—where the alternative is a human designer spending hours in Photoshop—this latency is acceptable.

Z.ai also offers a managed API at $0.015 per image, providing a bridge for teams who want to test the capabilities without investing in H100 clusters immediately.

GLM-Image is a signal that the open-source community is no longer just fast-following proprietary labs; in specific, high-value verticals like knowledge-dense generation, they are now setting the pace. For the enterprise, the message is clear: if your operational bottleneck is the reliability of complex visual content, the solution is no longer necessarily a closed Google product—it might be an open-source model you can run yourself.