Chinese LLMs Shift from Conversation to Task Delivery: Agent Capabilities and Inference Efficiency Advance Together

1. From “Can Answer” to “Can Deliver”: LLM Competition Enters a New Phase

For some time, industry attention on large models focused mainly on parameter scale, benchmark scores, and conversational experience. As application scenarios expand from chat assistants and content generation to code development, financial research, data analysis, and automated workflows, the criteria for evaluating model capabilities are changing noticeably. More teams are asking a more fundamental question: can a model sustain execution across a long task chain and ultimately produce an acceptable result?

This shift can be summarized as follows: LLM competition is moving from “conversational ability” to “task delivery capability.” Task delivery requires not only that a model understand instructions, but also that it plan, invoke tools, read long documents, validate intermediate results, correct errors, and complete execution within a reasonable time. Especially in code generation, research reports, data processing, and multi-step automation, whether a model is “usable” often depends on whether it can reliably complete the entire workflow, rather than merely producing a seemingly correct opening.

For developers, this means new dimensions are needed when evaluating models:

• Consistency and memory across long tasks;
• Stability of tool invocation and execution chains;
• The impact of inference latency and output speed on user experience;
• Cost per task, not just price per call;
• Whether weights can be released or private deployment supported in controlled environments.

2. Post-Training Scaling: From Parameter Race to Capability Density

A notable recent trend is that some models no longer pursue continued expansion of pre-training parameter scale alone, but instead improve performance on specific tasks by expanding compute investment in the post-training stage. Post-training typically includes supervised fine-tuning, preference alignment, reasoning process optimization, and reinforcement learning for specific capabilities.

The core logic is that pre-training mainly endows a model with language modeling and knowledge compression capabilities, while what truly determines whether a model can complete complex tasks is often the instruction following, reasoning planning, and tool use capabilities formed during post-training. By introducing richer task distributions, finer-grained feedback signals, and larger-scale joint optimization in post-training, models can improve task completion quality without significantly increasing inference cost.

Research and practice suggest that joint reinforcement learning across multiple downstream domains helps models achieve more stable policy generalization. For example, on tasks such as code generation, mathematical derivation, structured information extraction, and long-chain planning, models that undergo sufficient post-training often show better stability and reduce the probability of deviating from the goal midway or repeating ineffective steps.

For engineering teams, the implications of this trend are:

• When fine-tuning privately, do not focus only on sample quantity, but design post-training data that covers real failure modes;
• When selecting models, focus on stability metrics on target tasks rather than only general benchmark scores;
• In enterprise applications, improvements in post-training quality are often more aligned with return on investment than simply pursuing larger parameters.

3. Long Context and Multi-Step Reasoning: Foundational Capabilities for Task Delivery

Complex tasks typically require a model to simultaneously process long background materials, historical records, or code repository content. For example, a financial research report may involve hundreds of pages of announcements, earnings reports, and market data; a code maintenance task may require understanding cross-file call relationships; an automated workflow may require the model to remember the inputs, outputs, and state changes of previous steps.

Therefore, long-context support is increasingly becoming a foundational requirement for task-delivery models. It is important to note that long context is not just about “allowing more tokens as input”; more critically, it concerns the model’s ability to extract and associate information at different positions in long text. A qualified long-context model should be able to:

• Accurately locate task-relevant segments within documents;
• Maintain logical consistency before and after, avoiding forgetting early key information;
• Continuously track state across long conversations or long execution chains;
• Reduce attention decay and hallucination under long inputs.

In engineering implementation, long-context capability also needs to work with retrieval, chunking, summarization, and memory mechanisms. Even if a model itself supports ultra-long context, it does not mean all scenarios should input all materials at once. A more reasonable approach is usually to treat long context as a container for core context while combining retrieval and structured indexing to improve information density. For example, in financial research tasks, announcements and earnings reports can first be indexed by section, and relevant content can then be dynamically loaded according to the current analysis goal, preserving more critical information within the model’s context window.

4. High-Speed Inference Variants: Making Task Delivery Go from “Usable” to “Good to Use”

In many scenarios with high real-time requirements, even if a model has strong task completion capability, it may be difficult to deploy because inference speed is too slow. For example, online code completion, interactive data analysis, real-time customer service assistance, and task-oriented assistants for end users all require models to produce effective output within seconds or even less.

As a result, the industry has begun to see parallel release strategies for standard and high-speed versions. High-speed versions typically use model distillation, inference path optimization, speculative decoding, more efficient attention implementations, or hardware-level operator acceleration to increase output speed several times while basically maintaining major capabilities. This strategy can effectively reduce user waiting time and make long tasks acceptable in interactive scenarios.

Traditional high-speed versions often face the trade-off of “faster speed but degraded capability.” The direction receiving more attention now is: on the basis of expanded post-training and inference optimization, to make high-speed versions approach the task delivery capability of standard versions as closely as possible. That is, high speed is no longer just a “faster lightweight version,” but a higher-throughput version for production environments.

For developers, choosing between standard and high-speed versions should not depend only on price, but should also consider task characteristics:

• If the task is offline batch processing and allows longer waiting, prioritize the higher-capability version;
• If the task is in the user interaction path and is sensitive to first-token latency and total completion time, the high-speed version may be more suitable;
• For tasks requiring multiple rounds of tool invocation and iterative validation, focus on evaluating the stability of the high-speed version in long-chain execution;
• In enterprise cost accounting, use the cost of “completing a single effective task” as the core metric, rather than merely comparing price per million tokens.

5. Coordinated Optimization of Agent Capabilities and Inference Efficiency

One of the core capabilities of task-delivery models is interaction with the external environment. A model needs to be able to call functions or tools, read returned results, determine whether the goal is met, and retry or adjust the plan when necessary. This capability is commonly called Agent capability.

Agent capability and inference efficiency are closely related. A typical multi-step task may include the following stages:

Understand the task goal
→ Formulate an execution plan
→ Call external tools to obtain information
→ Parse tool return values
→ Validate intermediate results
→ Correct errors or adjust the plan
→ Output the final result

Each step in this process may incur token consumption and time cost. If the model’s inference efficiency is low, or if insufficient planning capability leads to frequent ineffective calls, the completion time and cost of the entire task will rise significantly. Conversely, if the model can plan adequately upfront, reduce unnecessary tool calls, and perform validation at key nodes, it can improve task quality while lowering overall overhead.

A clear current trend is: model capability optimization no longer separately pursues “stronger reasoning” or “more tool calls,” but optimizes reasoning quality, tool use strategy, and generation speed as a whole. Some technical directions include:

• Training better judgment of invocation timing within the model;
• Introducing structured mechanisms that separate planning and execution;
• Improving the correctness of long-chain tasks through process supervision;
• Using dynamic token budget techniques to allocate more reasoning resources to highly uncertain steps;
• Binding high-speed inference to the Agent execution chain to shorten waiting intervals between external calls.

For example, in long-chain Agent tasks, even if a single tool call is fast, if the model generates a large amount of intermediate text during each round of planning and reflection, overall latency may still be high. If a high-speed inference version can significantly compress the generation time of these intermediate steps, the user-perceived completion time can be greatly shortened. This is also an important reason why the industry has begun to discuss “improved Agent capabilities” and “leapfrog gains in inference efficiency” together.

6. Model Evaluation Recommendations for Developers

Against the backdrop of rapidly changing model capabilities, developers should not rely only on public benchmarks or demo effects, but should establish an evaluation system highly relevant to their own business. For task-delivery scenarios, evaluation can be conducted at the following five levels:

1. Task Completion Rate

Define a clear task success criterion, such as runnable code, a report containing necessary sections, or data extraction accuracy reaching a threshold. Record the model’s end-to-end success rate across multiple runs, not just single-run performance.

2. Execution Stability

Run the same task multiple times and observe whether the model exhibits path drift, missing steps, or inconsistent results. Stability is especially important for production environments.

3. Efficiency Metrics

Measure the average time, average token consumption, and average number of tool calls required to complete a single effective task. Efficiency metrics should map directly to the business cost model.

4. Long-Context Quality

Construct long inputs containing distracting information to test the model’s ability to extract, associate, and reason over key information. Focus on recall of information in the later portions of documents.

5. Cost-Benefit Ratio

Relate model cost to task delivery quality. For example, calculate “total cost per hundred effective tasks” or “minimum configuration cost to reach the target completion rate.” This is more business-relevant than simply comparing unit prices.

In addition, developers can connect standard and high-speed versions to the same test set separately, observe differences in task completion rate, average latency, and cost structure, and formulate routing strategies accordingly. For example, use the high-speed version for simple questions and the standard version for complex tasks, or prioritize the high-speed version in high-concurrency, low-latency scenarios.

7. Common Practices in Technical Deployment

In practical engineering, when integrating task-delivery models into business systems, it is usually necessary to build a set of foundational capability layers around the model, including:

• Context management module: responsible for long-document chunking, history compression, and structured indexing;
• Tool registration and invocation gateway: centrally manages external interfaces and provides parameter validation, retries, and timeout control;
• Process monitoring and logging system: records the inputs, outputs, call latency, and exception information of each step;
• Evaluation and replay mechanism: saves failure cases to form a continuously improving dataset;
• Cost and quota control system: applies differentiated quota management to high-frequency calls, long tasks, and high-speed channels.

Take the code development scenario as an example. A model may need to read multiple source files, call code search tools, generate modification suggestions, and execute tests. If the system lacks good context management, the model can easily lose key file structure in long context; if there is no unified tool invocation gateway, the execution chain may be interrupted by abnormal returns. Therefore, task delivery capability is not just a problem of the model itself, but also depends heavily on platform-side engineering support.

Consider the financial research scenario as another example. A model typically needs to extract information from large volumes of announcements, earnings reports, and market data, and generate structured research reports or investment analysis frameworks. At this point, the model’s long context, information extraction, logical validation, and citation accuracy are all critical. The value of a high-speed inference version in this scenario is mainly reflected in shortening the response time of each round when a research task requires multiple rounds of retrieval, reading, and analysis, so that the entire research workflow can be completed within an acceptable time window.

8. Conclusion

The development of large models is currently moving from the “expression and generation” stage into the “execution and delivery” stage. Whether a model has stable long-context processing capability, a high-quality post-training task strategy, and sufficiently high inference efficiency is becoming a key factor determining whether it can be deployed in real business settings.

For developers, taking a rational view of model parameters, benchmark scores, and marketing narratives, and returning to core metrics such as their own task completion rate, stability, latency, and cost, is a more robust engineering strategy. It can be foreseen that task-delivery-oriented model capabilities will continue to evolve, including stronger planning and error-correction capabilities, more efficient tool invocation mechanisms, and more flexible inference resource scheduling. Teams that can deeply integrate model capabilities with business workflows will be more likely to gain substantive advantages in the competition for intelligent applications.