Large Language Model Full Release: Capability Boundaries and Model Selection Guide

1. Background: A Flagship Model Arrives, but the Benchmarks Tell a Mixed Story

A major cloud provider recently released the full version of its latest large language model, officially positioning it as “entering the ranks of the strongest open-source models for the first time.” However, third-party evaluation results were less consistent. On a composite intelligence index, the model scored approximately 11 points lower than a competing model released in the same period, and its hallucination rate was flagged as “high.”

This raises a question that deserves serious attention from developers: What does the gap between official scores and third-party evaluations actually mean? Where are the real capability boundaries of this model?

This article analyzes the model’s parameter architecture, pricing strategy, and third-party evaluation results, then presents two hands-on tests to provide a neutral and reusable evaluation framework.

2. Model Overview: Parameters, Positioning, and Usage

2.1 Parameter Scale and Architecture

The full-release model has a total of 770B parameters and uses a Mixture of Experts (MoE) architecture, activating roughly 49B parameters per inference.

For context, the previous model in the same series had fewer than 300B parameters. This release jumps to approximately 2.6 times that size, making it the largest model the provider has ever built. The core advantage of MoE architecture is that total parameters determine knowledge capacity, while activated parameters determine per-inference computational cost. A 49B activation count means its inference overhead is roughly comparable to a mid-sized dense model, while its knowledge coverage approaches that of much larger models.

Key supporting specifications:

• Context length: 1 million tokens (via API). Long-document analysis and large code repository comprehension can be completed in a single pass without complex chunking and stitching.
• Vision understanding: Introduced for the first time. The model can interpret images, analyze charts, and understand screenshot content.
• Computer Use capability: First-time support for executing cross-application tasks by combining command-line and graphical interface operations.

The latter two capabilities represent the most significant additions over the earlier preview version. The preview was essentially a text-only model with strong language abilities; the full release can now see and act.

2.2 Pricing and Usage

API pricing remains unchanged from the preview version:

Billing Item	Price (per million tokens)
Input	6 CNY
Output	18 CNY
Cache hit	0.3 CNY

This pricing is inexpensive for a flagship model. By comparison, a competing model’s API output costs approximately 10 USD per million tokens — roughly four times as much.

The simplest way for regular users to access the model is through the provider’s AI coding tool or workspace, where the model can be selected from a dropdown list. During the full-release launch window, a limited-time free quota is typically available, with a consumption multiplier of 0.00x.

3. Official Scores vs. Third-Party Evaluations: Which Should You Trust?

This is an old question, but it resurfaces with every new model release.

The logic behind official scores is to achieve the best possible results on standard benchmarks and demonstrate the model’s upper capability limits. This is not inherently problematic, but two points require attention. First, official scores usually involve prompt tuning, so default real-world performance may be lower. Second, benchmarks themselves may suffer from training data contamination.

The logic behind third-party evaluations is to test under conditions closer to real-world usage, often focusing on stability, hallucination rates, and edge cases. However, third-party evaluations also have limitations: test sets are typically small, scoring criteria can be subjective, and different organizations may reach contradictory conclusions.

The right approach for developers is to consider both but fully trust neither. Official scores tell you what a model can do; third-party evaluations tell you where it might fail. The final decision must be based on hands-on testing in your own business scenarios.

In this evaluation, a third-party organization reported a composite intelligence index 11 points lower than a competing model and flagged the hallucination rate as “high.” Both signals deserve careful attention — especially the hallucination rate. In scenarios requiring factual accuracy, such as document Q&A or code generation, a high hallucination rate can significantly increase human verification costs.

4. Hands-On Test 1: 3D Scene Generation

4.1 Test Design

The model was given a natural language prompt to generate a 3D Gold Miner–style scene, including elements such as a mine shaft, miner, winch, and hook, in a low-poly cartoon style.

4.2 Observations

The model generated runnable 3D scene code in a single pass. All major elements were present, and the low-poly style largely matched the description. Compared with a competing model released in the same period:

• Generation speed: Similar for both models, with the full-release model slightly faster.
• Element completeness: The full-release model was more accurate in the geometric structure of the winch and hook, while the competitor produced better layering in the mine shaft.
• Code runnability: The full-release model had a slightly higher first-pass success rate. The competitor required minor adjustments in one or two places.
• Style consistency: The full-release model maintained the low-poly style more consistently, while the competitor drifted toward a more realistic style on some elements.

4.3 Conclusion

For tasks that require spatial reasoning combined with code generation, the full-release model demonstrated capabilities consistent with its flagship positioning. However, evaluations of this type of task are inherently somewhat subjective, and results may vary significantly under different prompts.

5. Hands-On Test 2: Hand-Drawn Sketch to Web Page

5.1 Test Design

The model was given a hand-drawn sketch of a web page layout and asked to generate the corresponding HTML/CSS code while preserving the layout structure and visual hierarchy.

5.2 Observations

This test directly assesses vision understanding capabilities. Results:

• Layout recognition: The model correctly identified four main regions: header, sidebar, main content area, and footer.
• Element mapping: Buttons, input fields, and image placeholders in the sketch were correctly converted to corresponding HTML elements.
• Style fidelity: Color and spacing accuracy was approximately 80%. Some spacing required manual adjustment.
• Responsive handling: The model automatically added basic responsive breakpoints, although the thresholds needed adjustment based on actual requirements.

5.3 Comparison with Text-Only Models

Text-only models cannot perform this task — they cannot see the sketch. The addition of vision understanding transforms the “design-to-code” workflow from requiring manual description of the design to directly supplying an image. The efficiency gain is clear, but the current version’s fidelity is not yet at the level of “no manual adjustment needed.”

6. Hallucination Rate: A Signal Worth Taking Seriously

The “high hallucination rate” flagged in third-party evaluations deserves closer discussion.

Hallucinations typically occur in the following scenarios:

1. Factual Q&A: The model may fabricate non-existent data, citations, or events.
2. Code generation: It may call non-existent APIs or use incorrect parameter signatures.
3. Long-document analysis: It may mix up information from different locations within a long context.

Recommended practices for the full-release model:

• Perform secondary verification on factual outputs.
• Always run tests after code generation.
• For long-document analysis, require the model to cite original source passages for key conclusions.

7. Summary: Who Is This Model For?

Well-suited scenarios:

• Tasks requiring long-context processing. The 1 million token context length is a concrete advantage.
• Combined vision understanding and code generation tasks, such as sketch-to-web conversion and chart analysis.
• Cost-sensitive flagship-level applications. API pricing is roughly one-fourth that of competing models in the same period.
• Automation tasks requiring computer-use capability.

Scenarios requiring caution:

• Use cases demanding extremely high factual accuracy, given the elevated hallucination rate.
• Scenarios requiring deep integration with a specific competitor’s ecosystem.
• Production environments with stringent inference stability requirements. A small-scale canary deployment is recommended first.

Selection guidance:

Do not rely solely on benchmark scores or pricing. Run comparison tests using real cases from your own business, focusing on output stability, hallucination rate, long-context performance, and total cost, including human verification overhead. Model selection has never been about choosing “the strongest model.” It is about choosing “the model that best fits my scenario.”