Domestic Large Models: Long-Horizon Reasoning and Coding Capability Evolution

Capability Overview: A Long-Context Intelligent Assistant for General Users

The new generation of domestic large models presents a capability layout characterized by “general capabilities as the foundation, specialized capabilities layered on top.” Based on publicly available capability descriptions, its core positioning is to support long-text input, file understanding, content writing, and data analysis, primarily targeting general users and offering multiple access methods. This combination means the model is no longer a single conversational entry point but attempts to cover three high-frequency task categories: read, write, and compute.

• File understanding: Structured parsing, key-point extraction, and cross-chapter question answering over user-uploaded long documents. This tests information retention and localization capability under long context windows.
• Content writing: Generating reports, explanatory documents, and summary abstracts based on given materials. This tests instruction following and style consistency.
• Data analysis: Statistical description and trend interpretation over tabular and structured data. This tests numerical reasoning and robustness of format parsing.

From an engineering perspective, the key to supporting these three task categories is not the elevation of any single metric but stability under long context: when input length reaches tens of thousands of tokens or more, can the model still accurately locate key information scattered across different positions in a document and maintain consistency across multi-turn interactions? This is the dividing line that determines whether such models can be deployed in real business scenarios.

Long-Chain Reasoning: The Trade-Off Between Solution-Space Exploration and Thinking Efficiency

In long-chain logic and multi-step reasoning tasks, this generation shows significant improvement over the previous generation, primarily in two aspects.

First, solution-space exploration is more thorough. The previous generation scored reasonably well on problems with multiple feasible solutions but did not explore sufficiently, often stopping at the first feasible solution and failing to enumerate alternatives, resulting in “missed solutions.” The new generation essentially resolves this issue, being able to more completely enumerate possibilities and find as many solutions as possible. This improvement is significant for logic puzzles, constraint satisfaction, and multi-step planning tasks, where scoring is often directly tied to solution completeness rather than merely whether an answer is provided.

Second, the cost is that the thinking budget gets maxed out. Because the model tends to keep exploring until possibilities are exhausted, its output consumption on complex logic problems can reach more than three times that of the previous generation. Notably, under the premise that overall token consumption remains on par with the previous generation, this structural change indicates that consumption on simple tasks is compressed while consumption on complex tasks is amplified, making resource allocation more “demand-driven.”

However, compared with leading industry solutions, the efficiency gap remains evident. On the same problems and achieving the same full score, leading models consume only half to one-sixth of this generation’s consumption. This reveals a key engineering proposition: there are two paths to improving reasoning capability—improving per-step reasoning quality, or improving search and verification efficiency. The former depends on the model’s inherent capability; the latter depends on budget control, early-stopping strategies, and pruning mechanisms. How to reduce ineffective exploration while maintaining solution completeness is a problem the next generation must address.

Behavioral Divergence Between Reasoning and Non-Reasoning Modes

This generation exhibits notable differences between the two modes, a divergence that deserves special attention from developers:

Dimension	Reasoning Mode (slow thinking)	Non-Reasoning Mode (fast thinking)
Behavioral characteristics	Long chain-of-thought, step-by-step unfolding	More like an “adaptive reasoning model with a smaller thinking budget”
Simple problems	Controllable consumption	Solvable within a few thousand tokens
Complex tasks	May hit the output ceiling	Consumption can rise to 20K–30K
Budget constraints	Output ceiling exists; over-limit outputs are not scored per rules	Ceiling approximately 15K; nearly half of samples exceed it

This divergence indicates that the non-reasoning mode is not “no thinking” but “short-budget thinking.” When encountering complex tasks, it also unfolds longer reasoning processes but lacks sufficient budget space. In testing, because the output ceiling was set relatively low, nearly half of the samples exceeded it and could not be scored, so this mode has not yet been fully evaluated. The reasoning mode is similarly affected by ultra-long chains of thought; a small number of problems exceeded the set ceiling across multiple test runs and could only be counted as zero, which is one reason the reasoning mode’s median score is偏低 (on the lower side).

The implication for developers is that the output ceiling is not a formal parameter but a hard constraint that directly affects evaluation results and production usability. When integrating such models, maximum output length must be set dynamically based on task complexity, and degradation strategies (such as segmented solving, task decomposition, and result merging) must be designed for over-limit scenarios. Otherwise, the awkward situation of “the model is capable enough but gets blocked by parameters” will arise.

Coding Capability: Moving from One-Shot Generation Toward Engineering-Grade Usability

The evolution path of coding capability is more illustrative. The previous generation substantially improved one-shot coding capability—producing runnable code directly from a requirement description—but performed unsatisfactorily under real Agent scenarios combined with real projects. The prominent shortcoming was a lack of effective debugging means: when code failed to run, the model struggled to autonomously locate errors, propose fixes, and verify fix results.

In this generation’s preview version, Agent coding capability has improved significantly. For conventional front-end and back-end programming tasks alone, its capability has entered the “usable” range. “Usable” in engineering practice typically means:

1. Can read existing codebases: Given file structure and context, locate the functions and modules that need modification.
2. Can produce changes consistent with project style: Rather than generating code in a drastically different style that requires extensive rework.
3. Can complete a basic debugging loop: Infer causes from error messages, propose and verify fixes.
4. Can handle common contract issues in front-end/back-end integration: Such as inconsistent interface fields and serialization format differences.

From “one-shot generation” to “Agent coding,” the core leap is not marginal improvement in code quality but tool use and feedback loops. A usable coding Agent’s workflow typically looks like this:

# Pseudocode: minimal loop for a coding Agent
while not task_done:
    plan = model.plan(task, context)          # Plan: decompose subtasks
    patch = model.generate(plan, repo_ctx)     # Generate: produce code changes
    result = sandbox.run_tests(patch)          # Verify: run tests in isolated environment
    if result.failed:
        diagnosis = model.debug(result.log)    # Debug: locate cause based on logs
        task = diagnosis.refined_task          # Feedback: refine task description and retry
    else:
        task_done = True

In this loop, the quality of the sandbox execution environment and test cases often determines success more than the model itself. Without executable verification means, even the strongest generation capability cannot form reliable delivery.

Cost Structure: Pricing and Budget Changes Brought by Capability Improvement

Capability improvement has also brought changes to the cost structure. Based on public information, this generation’s unit price has seen a modest increase, from 21 to 27. The confidence behind this adjustment comes from significant improvement in reasoning capability—among domestic models, its reasoning capability has reached the front ranks.

But costs have not spiraled out of control, because the form of the chain of thought has not changed much, and overall token consumption remains essentially on par with the previous generation. The real cost pressure comes from structural shift: consumption on complex logic problems may be more than three times that of the previous generation, while consumption on simple tasks is compressed. This means:

• For businesses with task distributions skewed toward simple tasks (such as customer service Q&A and document summarization), the impact on unit cost is limited.
• For businesses with task distributions skewed toward complex tasks (such as multi-step planning, complex logic verification, and code debugging), costs may rise noticeably, requiring task tiering and budget allocation at the architecture level.

Practical Recommendations: How to Make Good Use of This Type of Model

Based on the observations above, here are several implementation recommendations for developers:

1. Route by task complexity: Assign simple tasks to fast-thinking mode and complex tasks to slow-thinking mode. Avoid using high-budget mode for low-complexity requests, and avoid forcing low-budget mode to handle complex tasks.
2. Set output ceilings dynamically: Do not apply a uniform maximum output length across all scenarios. Set tiered ceilings based on task type and design decomposition and retry mechanisms for over-limit requests.
3. Design verification steps for long-chain reasoning: Thorough solution-space exploration means multiple candidate solutions may be produced, requiring accompanying validation logic (such as constraint checking, unit tests, and rule engines) to filter and confirm.
4. Prioritize building execution environments for coding scenarios: Investment in sandboxes, test cases, and static analysis tools often yields more certain returns than switching models.
5. Focus on thinking efficiency rather than scores alone: In evaluation and model selection, record token consumption and latency alongside accuracy, and calculate “cost per correct result”—the key metric in real business scenarios.

Overall, this generation’s performance on long-horizon tasks is closer to a form that “can independently advance tasks.” The improvement in reasoning capability and the reinforcement of coding Agent capability give it stronger usability in complex task scenarios. However, thinking efficiency, budget control, and debugging loops remain clear areas for improvement and directions worth continued attention in subsequent versions.