Evaluating LLMs on Long-Horizon Coding and Visual Generation

Over the past six months, the focus of model capability optimization has quietly shifted. The earlier approach of relying primarily on massive pre-training data in hopes of emergent capability is giving way to more refined post-training work. This shift is especially visible in a recently released flagship model update: the headline parameter count is no longer the main story. The more important change lies in the training strategy. By investing more heavily in post-training, the model builds stronger壁垒 in vertical areas such as coding, 3D content generation, and financial research.

Evaluation paradigm shift: from benchmark scores to long-horizon tasks

When evaluating a new-generation model, it is necessary to move beyond the single dimension of benchmark scores. Long-horizon task performance has become a key measure of model capability. A long-horizon task refers to work that requires the model to maintain consistency and logical coherence across a complex chain of multiple steps and multiple tool calls. For example, building an interactive 3D world from a single HTML file—with dynamic water level changes, complex light reflections, and a large scene layout, all without external dependencies—is an extremely demanding test environment.

To examine this new dimension, we compared two major versions of a model in an advanced reasoning mode on a desktop client, focusing on the real effect of training methodology changes. The practical comparison covered the following points:

• Scene composition: how the model handles visual hierarchy and focal points.
• Physical material simulation: how well the model has learned implicit knowledge such as ambient occlusion and material properties.
• Dynamic linkage logic: how accurately water level changes are linked to scene elements such as boats, waterlines, and plazas.
• Rendering mechanism differences: how physically accurate water reflections are relative to rendering cost.

First-screen visual narrative: three core differences between V1 and V2

Using a compound prompt involving a giant circular water gate, a terraced sea city, and tidal elevation changes, the two model versions showed significant differences:

Comparison dimension	V1 (older version)	V2 (new version)
Composition logic	Stacked layout; the giant ring was visually crowded out by redundant mountains	Organically integrated the giant ring, bridge openings, and beacon tower into a coherent visual narrative
Visual anchor	No clear focal point; the core building cluster was pushed to the edge	Used the concentric circular steps of the central plaza as the anchor
Material performance	Simple geometric placement	Created material contrast between the cool tone of oxidized copper and the warm tone of stone

V2 demonstrated a deeper learned understanding of scene composition and ambient occlusion, rather than simply placing geometric objects.

Water reflections: from analytic rendering to a mirror camera mechanism

Water rendering is a core indicator of the technical gap between the two model generations.

• V1 approach: used analytic rendering, simulating the water surface by mathematically combining sky color, sun highlights, and light streaks. The performance cost was very low, but the reflections did not reflect the real-time position changes of objects on shore, lacking spatial realism.
• V2 approach: introduced a mirror camera mechanism. When rendering a frame, buildings, boats, and other entities are first redrawn from a symmetrical viewpoint into an off-screen buffer, then mapped onto the water surface mesh through texture sampling. This dual-pass rendering significantly increases rendering load, but produces physically correct reflection results. This has substantial value for high-fidelity scenarios such as financial visualization or architectural walkthroughs.

Loss of detail: the trade-off of shifting from a generalist to a specialist

While V2 pursued top-tier visual performance, it also exposed weaknesses in validating specific physical parameters.

When testing the high-tide inundation logic, we performed a visual verification based on the source code: each step was approximately 0.3 meters high, with nine steps totaling 2.7 meters, and the maximum water level was set to 1.8 meters above the mean waterline. The result showed that although V2 looked visually impressive overall, its water level logic was inaccurate—only about the lowest four steps, or 1.2 meters, were below the waterline at high tide, far from the prompt requirement that “most stone steps” should be submerged. By contrast, V1 placed the maximum water level precisely at the outermost step.

This reveals a deeper contradiction in current large models: achieving an ideal balance across thread-level concurrency control for water level linkage, visual aesthetics for composition, and physics engine behavior for collision detection remains a major challenge. V2 chose to prioritize solving the problem of looking more professional, while deferring extreme logical precision. For developers, V2-type models are better suited to artistic creation and early-stage concept design, where some tolerance for error is acceptable. For CAD drafting or strict engineering calculations, traditional algorithmic verification is still necessary.

Industry observation: diminishing returns from distillation and a new competition

According to recent public research, many small and medium-sized teams rely on distillation to extract knowledge from very large models and train smaller ones. However, as leading models improve, the marginal benefit of simple knowledge distillation is declining rapidly. The next competitive frontier is shifting from memorizing knowledge points to complex logical reasoning and dynamic multimodal environment interaction.

The V2 model represents a different path: abandoning the pursuit of a universal low-cost approach and instead investing substantial post-training resources in domain-specific reinforcement. This heavier back-end approach sacrifices some generality, but builds a strong moat in high-value scenarios such as coding assistance and 3D asset production. For enterprise users, the productivity gains from such models in specialized scenarios are difficult to match with general-purpose APIs.

Conclusion and developer action items

When evaluating a new model, do not be misled by an impressive first screen. First-screen quality determines the lower bound of user experience, but what ultimately determines whether a project can ship is back-end stability under long-running operations, complex state transitions, and strict logical verification.

For production-grade development, the following automated regression checks are recommended:

1. Physical parameter validation: automatically compare outputs involving dimensions, water levels, gravity, and other numerical values against preset source code parameters.
2. Logical consistency checks: verify whether states across a multi-step pipeline contradict each other, such as water level linkage versus object positions.
3. Rendering result comparison: for visual generation tasks, use screenshot hashing or pixel-level difference detection to confirm that repeated runs produce stable results.

Going forward, whether vendors will use a mixture-of-experts architecture to dynamically call different sub-models for visual generation versus logical computation at inference time is the most notable technical direction to watch. Until then, rigorous secondary verification remains a necessary safeguard against the occasional failure of large-model perfectionism. Production deployment should also include an assessment of data security and compliance risks to ensure that sensitive information is not leaked through model calls.