Bridging the Inference Efficiency Gap in Domestic LLMs: From “Thinking Longer” to “Thinking Cheaper”

1. Redefining the Problem: The Gap Isn’t “Can It Be Done” but “How Expensive Is It”

When discussing the capability gap between domestic models and frontier models, the common approach is to compare benchmark scores. But the marginal information value of scores is declining rapidly: as leading models converge in absolute accuracy on most general tasks, what truly separates user experience and commercial viability is the inference cost required to complete a task of equivalent quality.

This gap can be abstracted onto a two-dimensional plane: the horizontal axis represents task completion quality, and the vertical axis represents per-task cost. Frontier models tend to sit near the bottom-right corner—“high quality, low cost”—while some domestic models, despite respectable quality metrics, land in the upper-left region: to approach equivalent performance, they consume significantly more inference tokens. This means the gap is fundamentally a gap in inference token efficiency, not merely in parameter count or data scale.

This judgment carries an important corollary: inference efficiency is not an “optimization item”—it is a “must-answer item.” It directly ties to cost, and cost determines whether a model can be called at high frequency, whether it can support long-tail scenarios, and whether it can be invoked repeatedly in agent-type applications without spiraling out of control.

2. The Divergence of Two Reasoning Paradigms

The industry currently pursues two distinctly different technical routes for improving model reasoning capability, which can be roughly summarized as “efficiency-first” and “length-first.”

Route A: Efficiency-first. The core assumption is that a model’s reasoning capability should be elicited within a relatively short chain of thought. The methods include higher-quality reasoning data, more refined reinforcement learning reward design, implicit constraints on reasoning steps, and engineering optimizations at the decoding stage such as speculative sampling and prefix caching. The result: fewer output tokens on equivalent tasks, without sacrificing accuracy.

Route B: Length-first. The core assumption is that letting the model “think longer and write more” — extending chain-of-thought length in exchange for accuracy gains — is the way forward. This route showed significant early results and was relatively straightforward to implement (essentially spending more inference budget during the generation phase), so many teams adopted it.

The problem is that Route B’s benefit curve diminishes while its cost curve is linear or even super-linear. When chain-of-thought length expands from a few thousand tokens to tens of thousands, accuracy improvement is often only a few percentage points, but inference cost may double. More troublesome is that excessively long chains of thought introduce “overthinking”: the model continues to entangle itself after already reaching a correct conclusion, sometimes even changing a correct answer to an incorrect one through redundant reasoning.

Therefore, a realistic assessment is: simply stacking reasoning length is a path that will hit its ceiling quickly. To continuously improve results under cost constraints, one must return to efficiency itself.

3. A Quantitative Perspective on the Efficiency Gap: Per-Task Cost

The most intuitive metric for measuring the efficiency gap is average per-task inference cost, which can be decomposed as:

Per-task cost ≈ input tokens × input unit price
              + output tokens × output unit price
              + additional overhead from retries/multiple samples

When compared at equivalent quality levels, if a model’s average task cost is higher than a reference model’s, its token efficiency is lower. Two easily overlooked details stand out:

1. Output tokens carry far more weight than input tokens. In most pricing schemes, output unit price is several times that of input, so the cost amplification effect of “thinking longer” is significantly underestimated.
2. Retries and multiple sampling multiply the gap. In agent or tool-calling scenarios, a single task often involves multiple rounds of interaction. A tiny per-round efficiency gap compounds into an order-of-magnitude difference over many rounds.

This also explains why the efficiency gap is particularly pronounced in complex engineering tasks (e.g., scenarios requiring multi-step code modifications and verification): such tasks involve many interaction rounds, long contexts, and dense output, so any per-round token waste gets repeatedly compounded.

4. Four Actionable Paths for Efficiency Optimization

4.1 Adaptive Allocation of Reasoning Budget

Don’t treat “reasoning length” as a fixed hyperparameter—treat it as a resource that can be dynamically adjusted based on task difficulty. The basic idea is to introduce a lightweight difficulty estimation step: route simple tasks through short-chain reasoning, and only open up the reasoning budget for complex tasks.

A conceptual scheduling logic is as follows:

def allocate_reasoning_budget(task, history):
    difficulty = estimate_difficulty(task, history)  # lightweight classifier or heuristic rules
    if difficulty < 0.3:
        return {"max_reasoning_tokens": 512, "sample_n": 1}
    elif difficulty < 0.7:
        return {"max_reasoning_tokens": 2048, "sample_n": 1}
    else:
        return {"max_reasoning_tokens": 8192, "sample_n": 2}

The key is that difficulty estimation itself must be cheap—it can be rules, a small model, or historical statistics, but must never introduce another expensive inference process. At the same time, an “early termination” mechanism should be established: when the model’s intermediate conclusion already has high confidence, subsequent reasoning is allowed to be truncated.

4.2 Decoding-Layer Optimization: Speculative Decoding and Cache Reuse

The bulk of inference cost lies in the decoding stage. Three categories of engineering techniques offer clear returns:

• Speculative Decoding: A small model drafts multiple tokens, which the main model then verifies in parallel, reducing the number of forward passes through the main model without changing the output distribution.
• Prefix Caching: KV cache reuse for stable prefixes such as system prompts, tool definitions, and long documents, avoiding redundant computation across multi-round interactions.
• Structured Output Constraints: For tasks requiring fixed-format output, constrained decoding reduces invalid tokens.

None of these change the model’s capability ceiling, but they directly lower per-task cost—they are “deterministic returns.”

4.3 Aligning Training Objectives with Evaluation Metrics

Efficiency problems are often seeded during the training phase. If reasoning traces in training data are generally verbose, the model learns a preference for “verbose equals correct.” Improvement directions include:

• Explicitly introducing a length penalty term in preference optimization, so that shorter equivalent correct solutions receive higher rewards;
• Constructing contrastive samples of “short chain correct, long chain incorrect” to suppress overthinking;
• Making per-task cost a first-class metric alongside accuracy in the evaluation system, rather than looking only at accuracy.

The guiding role of evaluation metrics is often underestimated. If internal leaderboards only track accuracy, teams will naturally choose the path of least resistance—“lengthen reasoning.” Only when cost enters the leaderboard does efficiency optimization become an engineering priority.

4.4 Collaborative Design on the Application Side

Efficiency isn’t just the model’s concern. The application layer can significantly reduce token consumption through task decomposition, context pruning, and replacing pure reasoning with tool calls. For example, changing “let the model perform large-number arithmetic in context” to “call a calculation tool,” or changing “let the model read the entire document for retrieval” to “retrieve first, then read closely,” can cut costs with almost no loss in effectiveness.

5. How Long Will the Catch-Up Take: A Judgment Based on Capability Building

Giving a precise number for the catch-up timeline is not rigorous, but a structured judgment framework can be offered. Catch-up speed depends on the product of three variables:

1. Speed of paradigm shift — Whether teams are willing to move from “length-first” to “efficiency-first.” This is a cognitive issue, usually the fastest to change, but also the most easily dragged down by existing engineering inertia.
2. Maturity of engineering infrastructure — Whether capabilities like speculative decoding, prefix caching, and dynamic budget scheduling have been productized and made reusable. This requires sustained investment and belongs to the medium-term cycle.
3. Closed loop of efficiency signals on the training side — Whether cost metrics can be reliably fed back into the training and evaluation pipeline. This involves the interplay of data, reward design, and evaluation systems, and has the longest cycle.

Based on this, a more realistic expectation is: point-level efficiency optimizations can take effect within a relatively short cycle, while systemic efficiency advantages require continuous accumulation on a generational scale. Therefore, “preparing for a long-term catch-up” is not a pessimistic judgment but a respect for engineering principles—efficiency advantages are never the product of a one-time breakthrough, but the compound result of a large number of detailed optimizations.

6. Practical Recommendations for Developers

• Include cost as a first-tier metric during model selection: Don’t just look at leaderboard scores. Measure the ratio of “per-task cost × success rate” on your own real task distribution.
• Establish your own efficiency baseline: Fix a set of representative tasks, continuously record token consumption and accuracy, and compare any model or prompt change against the same baseline.
• Prioritize deterministic optimizations: The returns from cache reuse, structured output, and context pruning are clear and come with no capability loss—these should precede model replacement.
• Beware of overthinking: Adding early stopping and length constraints in long-chain reasoning scenarios often significantly reduces cost with almost no loss in effectiveness.
• Write cost into acceptance criteria: Make efficiency part of engineering delivery, not a lingering issue discovered only after launch.

Efficiency catch-up is a prolonged battle over “cost per unit of effectiveness.” Its outcome does not depend on any single model release, but on whether cost awareness can be systematically embedded into every link of training, evaluation, and application.