The Path to Closing the Gap: How Domestic LLMs Can Reach Frontier-Level Performance

Large language models have entered a phase of exponential evolution. Frontier models continue to raise the bar in multimodal understanding, long-context reasoning, and complex agentic task orchestration. Against this backdrop, one question looms large for the domestic AI ecosystem: how long will it take to match or surpass the capabilities of today’s most advanced models?

This is not merely a question of time. It is a systemic challenge spanning compute scheduling, data engineering, algorithmic architecture, and engineering delivery. Recent public benchmarks and real-world deployment results point to an uncomfortable reality: the era of scaling for its own sake has hit a ceiling. Domestic models need to prepare for a long-term effort built on targeted optimization rather than brute-force expansion.

1. Core Challenges: Compute Cost and Complex-Task Effectiveness

Recent evaluations on automated agent benchmarks and complex software engineering tests reveal a telling pattern: some leading domestic models incur higher average inference costs per task than certain frontier models. On the effectiveness axis for complex engineering tasks, they often land in a less favorable region — higher cost with slightly lower overall capability.

This points to two core pain points:

1. Inverted inference cost: When handling long-horizon tasks, models often suffer from poor context window management and excessive redundant computation, leading to massive token consumption per task invocation.
2. Weak complex-task reasoning: In agentic scenarios requiring multi-step planning, state maintenance, and tool invocation, models are prone to logical loops or hallucinations, resulting in lower task completion rates.

This “high cost, low output” dynamic means domestic models cannot simply chase faster iteration cycles. The underlying technical approach itself must be re-examined.

2. Infrastructure Optimization: Deep Coordination Between Compute and Cloud-Native Foundations

Closing the gap starts with compute scheduling and utilization efficiency. Access to high-end heterogeneous accelerators matters, but the real engineering barrier lies in converting raw compute into stable model throughput through cloud-native technologies.

2.1 Heterogeneous Compute Pooling and Dynamic Routing

In large-scale training and inference scenarios, a single compute cluster cannot gracefully absorb tidal fluctuations in request volume. Organizations should leverage mature cloud foundations — such as container platforms and heterogeneous compute instances — to build compute pools that span availability zones. A unified scheduler can dynamically route and partition GPU resources of different specifications, ensuring high-priority inference tasks receive stable underlying I/O and memory bandwidth.

2.2 Mixture-of-Experts and Operator-Level Optimization

Mixture-of-Experts (MoE) architectures have become a mainstream approach for scaling parameter counts under constrained compute. However, MoE introduces substantial memory communication overhead. Domestic vendors need deep customization at the operator level — for example, overlapping communication with computation to reduce All-to-All latency.

# Pseudocode: Asynchronous communication and computation overlap scheduling for MoE
class AsyncMoEDispatcher:
    def __init__(self, num_experts, capacity_factor):
        self.num_experts = num_experts
        self.capacity = capacity_factor
        self.comm_stream = torch.cuda.Stream()
        self.compute_stream = torch.cuda.Stream()
    def forward(self, hidden_states):
        # 1. Asynchronously dispatch tokens in the communication stream
        with torch.cuda.stream(self.comm_stream):
            dispatch_mask, dispatched_tokens = self.token_dispatcher(hidden_states)
        # 2. Run expert forward pass in parallel in the compute stream
        with torch.cuda.stream(self.compute_stream):
            expert_output = self.experts(dispatched_tokens)
        # 3. Synchronize and merge results
        torch.cuda.synchronize()
        combined_output = self.token_combiner(expert_output, dispatch_mask)
        return combined_output

Such low-level optimizations can significantly increase training and inference throughput without adding physical compute, directly reducing the average cost per agentic task.

3. Algorithmic Evolution: Combining Multi-Step Reasoning with Reinforcement Learning

Frontier models excel at complex tasks such as automated software engineering, code generation, and bug fixing because they possess strong “slow thinking” and multi-step planning capabilities. For domestic models to reach this level, post-training must incorporate more sophisticated reinforcement learning and chain-of-thought guidance mechanisms.

3.1 Combining Rule-Based Signals with Reward Models

Traditional supervised fine-tuning struggles to teach models how to dynamically adjust strategies in complex environments. What is needed is environment-based reinforcement learning, where models execute code in virtual sandboxes, observe runtime results, and receive rewards based on objective metrics such as execution success rate and test case coverage.

3.2 Self-Play and Synthetic Data Generation

High-quality human preference data is becoming increasingly scarce. Domestic LLMs need a self-reinforcing data flywheel: leverage existing powerful base models to generate large volumes of complex instructions and corresponding multi-step reasoning trajectories. After rigorous multi-stage filtering and sandbox validation, these become high-quality synthetic data that feed back into iterative model training.

4. Engineering Practice: Building an Efficiency-Centric AI Application Loop

Model capability should not be judged solely on benchmark scores. It must translate into the ability to solve real business problems. For enterprise applications, the LLM functions less as a language generator and more as the central processor of an intelligent system.

4.1 Building an End-to-End Agent Foundation

When making technology choices, organizations should align with their business characteristics and build an agent foundation using standardized cloud-native components. By integrating object storage for mounting large knowledge bases, message queues for asynchronous task decomposition, and logging services for end-to-end inference tracing, model capabilities can be embedded into existing business workflows.

4.2 Introducing Long-Term Memory and State Management

In complex automated tasks — such as repairing a codebase containing multiple latent bugs — models require long-term memory. Developers need to maintain an efficient external state machine or vector database that stores execution context and intermediate results for fast retrieval. This reduces ineffective context window filling and minimizes token waste.

5. Strategic Patience and Technical Accumulation for Long-Term Catch-Up

Faced with the current cost inversion and capability gap, the domestic LLM industry must abandon the short-sighted mindset of expecting a single decisive breakthrough. The moat around frontier-level capability cannot be crossed through a few months of parameter stacking. It requires a complete closed loop spanning data cleaning, operator optimization, architectural innovation, and real-world application feedback.

Long-term investment should focus on three dimensions:

1. Strengthening infrastructure: Work closely with cloud providers to leverage higher network bandwidth and storage I/O, reducing communication overhead in large-scale clusters.
2. Mastering core algorithms: Achieve original breakthroughs in long-context extrapolation, sparse attention mechanisms, and lossless quantized inference.
3. Building an open and rigorous evaluation ecosystem: Move beyond leaderboard chasing. Establish demanding evaluation systems based on real production environments — such as repository maintenance and complex data analysis — to guide models toward practical utility.

Conclusion

Reaching or surpassing frontier-level model performance is a marathon that spans algorithms, compute, data, and engineering. Domestic AI can only prevail in this long-term pursuit by building on cloud-native foundations, investing equally in low-level operator optimization and upper-layer agent architecture innovation, and continuously refining inference efficiency and task planning capabilities in real business scenarios.