Post-Training Evolution of Chinese LLMs: From Base Model Breakthroughs to Visual Coding in Practice

Over the past year, the focus of competition among open-source large language models has been shifting. Early on, the contest centered on parameter scale and the general knowledge capabilities of pre-trained base models. But as base model performance approaches its ceiling, what truly separates the leaders is now the quality of reinforcement learning in the post-training stage, along with the completeness of the tooling ecosystem built around the model. Using a recent flagship model iteration as an observation sample, this article examines the technical logic behind the pragmatic “no more parameters, more compute” route, and offers selection and deployment advice for developers and enterprises.

1. Timeline Review: What Happened in Three Months

To understand a version leap, you first need to see its rhythm of evolution. Below is the iteration trajectory of a typical flagship model (expressed in relative time):

Phase	Key Action
T0	Released a very large open-source base model with parameters in the trillions, showing strength in demanding scenarios such as operator optimization and compiler theory, but underperforming on real-world end-task benchmarks
T0+2 months	Released a desktop coding assistant that wraps command-line capabilities into a GUI: built-in browser, visual annotation, and Plan/Goal dual modes, paving the way for long-horizon Agent tasks
T0+2 months+2 days	The official team teased the new version with a pi-digit easter egg, sparking community interpretation
T0+3 months−2 days	An anonymous model performed strongly on a public arena and was identified by the community through output fingerprinting as a new member of the same family
T0+3 months	Officially released the post-trained version, promising to open the full weights for a limited period

The timeline reveals a key signal: during those three months in between, no bigger base model was built. Instead, two things happened in parallel—rapidly iterating the coding toolchain on the periphery (sandbox, terminal, browser tools), while internally running nearly a hundred days of reinforcement learning post-training on the base model. Together, these account for the entire leap.

One sentence summarizes this evolution:

New version = the original base model’s full-strength skeleton + joint reinforcement learning retraining across 100+ domains + roughly a one-third cut in API pricing

2. Three Dimensions of Capability Gains

2.1 Composite Intelligence Index: A Generational Jump

Public blind-test data shows the new version improved by about 11 points on a composite intelligence index compared with the initial base model, entering the global top tier. Importantly, this gain did not come from parameter expansion but from joint reinforcement learning in post-training—reward modeling and policy optimization spanning more than 100 task domains—so that reasoning chain length, instruction-following stability, and multi-step task decomposition all improved together.

From an engineering perspective, there are several reusable lessons in this approach:

• Multi-domain coverage of reward signals: single-domain RL tends to create capability imbalance; joint training mitigates the “narrowing as you train” problem;
• Credit assignment for long-horizon tasks: for Agent-type tasks, the final-outcome reward must be decomposed into intermediate steps, otherwise the model never learns to plan;
• Base model quality determines the ceiling: post-training searches within the base model’s capability space; if the base is too weak, RL returns saturate quickly.

2.2 Visual Aesthetics and Frontend Generation: An Underrated Differentiator

The most visible difference in hands-on testing appeared in vision-related tasks: in frontend scenarios such as UI layout generation, color schemes, and component hierarchy, the new version clearly outperforms comparable general-purpose models. The value of this capability lies in the fact that a huge gap exists between frontend code that “runs” and code that is “deliverable”—and aesthetic ability is precisely what bridges that gap.

A typical structured prompt for frontend generation looks like this (illustrative):

Role: Senior frontend engineer
Constraints:
  - Output a single-file HTML with inline styles and no external dependencies
  - Use an 8pt spacing system and semantic tags
  - Responsive breakpoints: 768px / 1200px
  - Color scheme must meet WCAG AA contrast requirements
Deliverable: complete runnable code + explanation of key design decisions

Whether the model can consistently produce code that meets design specifications under such structured constraints depends on its implicit modeling of “visual plausibility.” This is also a direct payoff of introducing multimodal feedback (screenshot comparison, rendering diffs) during post-training.

2.3 Terminal Task Execution: From Weak Spot to Above the Passing Line

On the terminal benchmark that best reflects “real command-line fundamentals,” the initial base model achieved only about a 13% pass rate—a clear generational gap with top closed-source models. The new version largely closed this gap, and the reason is not mysterious:

• The coding toolchain provides executable sandbox feedback, allowing RL to sample success and failure signals in a real environment;
• Desktop visual annotation and browser tools let the model observe execution results and self-correct;
• The Plan/Goal dual modes break long tasks into verifiable subgoals, limiting the propagation of single-step failures.

In other words, a mature toolchain is itself part of the post-training data. Without an interactive execution environment, effective reinforcement learning of Agent capabilities is impossible.

3. Cost Structure: Why a “One-Fifth Effective Price” Deserves Serious Attention

API prices dropped by about one-third; combined with the capability gains, the decline in unit task cost far exceeds the nominal price cut. When evaluating model costs, enterprise users should consider three layers:

1. Per-token price: the most intuitive metric, but also the most misleading;
2. Task completion rate: with a low first-pass rate, retry costs will eat up the price advantage;
3. Context and tool-call overhead: in Agent scenarios, the number of tool round-trips often affects total cost more than generation length.

A rough cost estimation model:

Cost per task ≈ (input tokens × price_in + output tokens × price_out) × expected attempts
Expected attempts ≈ 1 / task first-pass rate

When the first-pass rate rises from low to mid-high, total cost drops significantly even if unit prices stay flat. This is the hidden economic value that post-training delivers.

4. Productivity Boundaries: What It Can and Cannot Do

A rational view of such flagship models’ capability boundaries matters more than chasing leaderboard scores.

Scenarios suitable for delivery:

• First-draft generation of frontend pages and components, where human review can significantly shorten delivery cycles;
• Batch generation of scripts and boilerplate code;
• Automation workflow orchestration involving multi-step tool calls (with a controllable sandbox);
• Assistive work such as code explanation, refactoring suggestions, and test case completion.

Scenarios that still require caution:

• Core system changes involving strong consistency and transactional semantics—human review must remain in the loop;
• Deep refactoring across large codebases, which is constrained by context window and retrieval quality;
• Outputs with hard regulatory, security, or compliance requirements cannot rely on the model’s self-assessment of correctness;
• The reliability of long-horizon autonomous Agent tasks in open environments still depends on robust permission and rollback mechanisms.

5. Practical Recommendations for Developers

5.1 Selection Methodology

Do not rely solely on composite leaderboards; run layered evaluations by task type: build an internal eval set of 50–100 cases for your core scenarios, covering both normal paths and edge cases, then compare candidate models on pass rate, retry counts, and total time. Leaderboard scores are only useful for initial screening.

5.2 Engineering Preparation

• Build an observable execution environment: sandboxes, logs, and rendered screenshots serve both as a security boundary and as input for self-correction;
• Treat prompts as code: version them, make them regression-testable, and define explicit constraint clauses;
• Design fallback paths: when model output is abnormal, the system should automatically fall back to templates or a human review queue.

5.3 Cost Governance

Track token consumption and retry rates at the task level and build a unit-task-cost dashboard; route high-frequency, low-value tasks to lightweight models and reserve flagship models for high-value complex tasks, forming a model routing strategy.

6. Conclusion

This version iteration offers a noteworthy paradigm: as the race to scale base models hits a plateau, post-training quality + tooling completeness + cost structure are becoming the new competitive triangle. For developers, the real dividend lies not in leaderboard rankings but in whether model capabilities can be reliably embedded into verifiable, rollback-capable, and measurable engineering workflows. Chinese flagship models now have the strength to compete head-to-head with top overseas models—but converting that strength into productivity still requires solid engineering work.