Observations on the Evolution of Long-Range Reasoning and Code Intelligence in Domestic LLMs

1. From Unmanned to Manned Lunar Orbit: Positioning the Capability Leap

The positioning of the recent model iteration can be understood through an analogy: the previous generation completed an “unmanned lunar orbit,” validating basic orbital and return capabilities. The new generation is akin to a “manned lunar orbit,” meaning the system must maintain stability under real workloads, long-range tasks, and complex constraints. The core shift in this generation of general-purpose LLMs is not a breakthrough in a single-point capability, but a noticeable improvement in their “human-like” behavior when solving practical problems and executing long-range tasks. It acts more like an experienced collaborator than a single-turn Q&A machine.

Looking at the capability matrix, this generation supports long-context input, file comprehension, content generation, and data analysis. It primarily targets general users and offers multiple integration methods, including API calls, client-side interactions, and integrated toolchains. What truly matters to developers, however, is the structural improvement in its reasoning and coding capabilities, along with the resulting changes in cost structure.

2. Long-Chain Reasoning: Sufficiency of Solution Space Exploration

2.1 The Previous Generation’s Issue: Insufficient Exploration

In various long-chain logic and multi-step reasoning tasks, the previous generation scored reasonably well but suffered from a typical flaw: insufficient exploration of the solution space when multiple valid solutions existed. This issue is common in scenarios like constraint satisfaction, multi-step planning, and combinatorial optimization. The model would often stop upon finding the first feasible solution rather than continuing to search for optimal or alternative solutions.

2.2 The New Generation’s Improvement: Comprehensive Solutions at Maximum Cost

The new generation largely resolves this issue, capable of thoroughly exploring possibilities to find a comprehensive set of solutions. The trade-off is straightforward: the model tends to “run against the thinking budget limit.” While overall token consumption remains comparable to the previous generation, the token usage for complex logic problems can be several times higher.

This exposes a critical contradiction: thinking efficiency. Leading models in the industry consume only one-half to one-sixth of the tokens to achieve the same perfect score on identical problems. In other words, while the capability meets the standard, the inference cost per unit of capability remains high. Improving thinking efficiency is a challenge the next iteration must address.

2.3 Engineering Impact of Thinking Budgets and Token Limits

During evaluations, a phenomenon worth developers’ attention was observed: because the output token limit for the non-reasoning mode is relatively conservative (around 15K), and nearly half of the complex tasks exceeded this limit, they could not be scored by the rules. Thus, the non-reasoning mode temporarily lacks complete test data. The reasoning mode is similarly affected by long chains of thought; a small number of problems exceeded the 80K output limit across multiple test runs, resulting in a zero score. This directly contributes to the lower median score in reasoning mode.

This provides two key takeaways for engineering practices:

• Limits must align with task complexity. The output distribution for long-chain reasoning tasks has a heavy long tail. A fixed token limit will systematically truncate the most complex samples, causing a discrepancy between evaluation results and production performance.
• The behavioral boundary between fast and slow thinking is blurring. The new generation’s non-reasoning mode behaves more like an “adaptive reasoning model with a lower thinking budget.” It can solve simple problems within a few thousand tokens, but output consumption can surge to 20K or 30K for complex tasks. This means callers can no longer rely on a simple “fast/slow” dichotomy to estimate latency and costs.

3. Coding and Agentic Capabilities: From One-Shot Generation to Real Projects

3.1 The Previous Generation’s Shortcomings

The previous model significantly improved one-shot code generation but underperformed in real agentic and real-project scenarios. The prominent issue was a lack of effective debugging mechanisms. While one-shot generation could produce “correct-looking” code, its capabilities clearly degraded once entering multi-round iteration, error localization, and repair loops.

3.2 The New Generation’s Improvements

In early public code preview versions, agentic programming capabilities have seen significant improvement. Looking solely at standard front-end and back-end programming tasks, its capability has entered the “usable” range—a crucial leap from “demo-usable” to “engineering-usable.” For developers, the criteria for usability typically include:

• Maintaining context consistency across multi-file projects.
• Localizing issues based on error messages and providing executable fixes.
• Adhering to existing code styles and engineering constraints rather than rewriting from scratch every time.

3.3 Practical Recommendations for Agentic Coding

When introducing model-assisted coding into real projects, consider the following engineering practices:

1. Context Layering: Inject project structure, interface contracts, and change history
   in layers to avoid diluting effective context by dumping all code at once.
2. Debugging Loop: Require the model to output a three-part conclusion:
   "Hypothesis - Verification - Fix," rather than directly providing a patch,
   to facilitate human review.
3. Change Granularity: Limit single-task changes to a reviewable diff range.
   Overly large changes significantly reduce rollback capability and testability.
4. Budget Fallback: Set output limits and timeouts for long tasks,
   and implement degraded processing for truncated results.

4. Cost Structure and Routing Strategies

The improvement in reasoning capabilities comes with a slight price adjustment, with the unit price rising from 21 to 27. This adjustment is justified by the significant enhancement in reasoning capabilities, positioning it ahead of similar models. The good news is that the chain-of-thought length hasn’t changed drastically, keeping overall costs controllable.

However, the cost structure of the non-reasoning mode requires separate evaluation. Because its behavior is closer to adaptive reasoning, output consumption for complex tasks may multiply. When selecting a model, developers are advised to measure costs based on task types:

Task Type	Recommended Mode	Cost Considerations
Simple Q&A, format conversion	Non-reasoning mode	A few thousand tokens per call, low cost
Multi-step logic, constraint solving	Reasoning mode	Heavy output long tail; requires limits and truncation fallback
Agentic coding, multi-round debugging	Reasoning mode + Tool calls	Accumulates by round; requires budget circuit breakers

5. Conclusion: With Capabilities Met, Efficiency Becomes the Main Battlefield

Overall, this generation of general-purpose LLMs has achieved three things: it has essentially filled the gap in solution space exploration for long-chain reasoning, brought agentic coding into the usable range, and covered general user scenarios with long-context and file comprehension capabilities. Its problems are equally clear: thinking efficiency still lags behind industry leaders, token consumption for complex tasks remains high, and fixed output limits systematically truncate long-tail samples.

For developers, the conclusion is pragmatic: the capabilities are sufficient to support real engineering scenarios, but cost control and invocation strategies must be upgraded in tandem. Treating thinking budgets, output limits, and fallback strategies as first-class design citizens is the only way to translate model capabilities into deliverable product features. The focus of competition for the next generation will likely shift from “can it get it right” to “at what cost can it get it right.”