Evaluating LLM Capabilities and Compute Quotas for Long-Horizon Tasks

1. The Problem: When Model Capabilities Meet the Quota Ceiling

Over the past year, the performance of Large Language Models (LLMs) in specialized domains has shown clear stratification. While the gap between mainstream models is narrowing on routine tasks like code generation and documentation writing, the capability divide remains significant for high-difficulty tasks such as formal mathematical proofs, open-ended mathematical exploration, and complex system bug localization.

Simultaneously, another easily overlooked issue is emerging: when a model possesses the capability to handle long-horizon tasks, are subscription-based compute quotas sufficient to support the completion of such tasks?

These two questions seem independent but are tightly coupled. The former determines “can it be done,” while the latter determines “can it be finished.” This article provides a systematic analysis of these two dimensions from an engineering perspective.

2. Capability Stratification: Evaluating High-End Reasoning Levels

2.1 Selecting Evaluation Dimensions

Evaluating a model’s performance on high-end reasoning tasks requires looking beyond single-turn Q&A quality. The following dimensions offer better reference value:

• Independent Completion: Faced with questions lacking ready-made answers, can the model autonomously construct proofs or solutions rather than retrieving existing materials?
• Reasoning Chain Quality: Is the chain of thought concise and direct, or verbose and convoluted? A concise reasoning chain typically indicates stronger abstraction capabilities.
• Output Readability: Do the generated proofs or technical documents possess a structure and expression readable by human experts?
• Stability: Across multiple attempts, can the model maintain a consistent success rate and output quality?

Take formal mathematical proofs as an example. A typical testing method involves providing a theorem from an authoritative mathematical literature database that contains only the proposition statement without an accompanying proof, requiring the model to independently complete the proof and write it as an English lecture note using structured markup languages. Such tasks cannot be completed through retrieval or paraphrasing; they rely entirely on the model’s intrinsic mathematical reasoning capabilities.

2.2 Rough Division of Capability Tiers

Based on practical experience, current high-end reasoning capabilities can be roughly divided into the following tiers:

Tier 1: Open-Problem Exploration Capability
Models in this tier can not only prove existing propositions but also propose valuable partial progress or ideas for unresolved open mathematical problems. Currently, models possessing this capability are extremely rare.

Tier 2: Complex Proofs and Long-Chain Reasoning
These models can independently complete proofs for relatively complex theorems with clear reasoning chains, yielding output quality approaching human expert levels. Different models in this tier have different focuses: some feature more concise chains of thought and natural writing, while others have longer reasoning chains but broader coverage.

Tier 3: Medium-Difficulty Reasoning and Code Auditing
These models can handle technical tasks requiring multi-step reasoning, such as bug localization in complex codebases, cross-module feature modifications, and medium-difficulty algorithm design. Models in this tier already provide high practical value in daily development.

Tier 4: Routine Task Processing
This includes code completion, unit test generation, and documentation writing. The differences between models in this tier have a relatively limited impact on the daily development experience.

It is worth noting that this tier division is not absolute. A single model’s performance may overlap across different task types. For instance, a model in Tier 2 for mathematical proofs might perform even better in code auditing.

3. The Quota Dilemma: Compute Consumption Models for Long-Horizon Tasks

3.1 A Realistic Consumption Scenario

Consider the following typical long-horizon workflow:

1. A user sets a complex goal (e.g., “Audit the entire codebase and fix all potential defects”);
2. The model initiates multiple parallel sessions to process different modules;
3. Each session configures a sub-agent mechanism to delegate simple tasks to lighter models;
4. The main model handles complex reasoning and decision-making, while sub-agents handle execution.

During this process, compute consumption exhibits the following characteristics:

• Non-linear Growth: For every level increase in task complexity, the compute consumed may rise exponentially;
• Parallel Amplification: Multiple sessions running simultaneously will significantly accelerate quota depletion;
• Long-Tail Effect: The final 20% of a complex task often consumes 80% of the compute.

3.2 Structural Contradictions in Quota Mechanisms

Current mainstream subscription services typically design quotas using a “short-period + long-period” dual-limit structure:

• Short-period limits: e.g., resetting every 5 hours to prevent sudden bursts of consumption;
• Long-period limits: e.g., resetting weekly to control overall usage.

The problem is that the ratio design between long-period and short-period quotas often fails to account for long-horizon task requirements. For example, if the weekly quota is only equivalent to three short-period allowances, a long task requiring continuous operation for several hours might exhaust the majority of the weekly quota within the first short period.

This leads to an awkward situation: to complete a complex task, users must meticulously schedule their usage time, sometimes being forced to interrupt the task halfway through to wait for the next cycle.

3.3 Engineering Methods for Consumption Estimation

For developers, establishing a rough consumption estimation model is necessary. Here is a reference framework:

Task Complexity Assessment:
  - Simple tasks (code completion, single function modification): consumption coefficient 1x
  - Medium tasks (module refactoring, bug localization): consumption coefficient 3-5x
  - Complex tasks (system-level audits, mathematical proofs): consumption coefficient 10-20x
Session Quantity Impact:
  - Single session: baseline consumption
  - N parallel sessions: consumption is approximately N times (sub-agents can partially reduce this)
Time Distribution:
  - First 50% of time: consumes about 30% of quota
  - Last 50% of time: consumes about 70% of quota

This framework does not provide precise predictions, but it can help developers make more reasonable judgments before launching long-horizon tasks.

4. Engineering Practices: Optimizing Long-Horizon Task Execution Strategies

4.1 Task Decomposition and Priority Sorting

Break down complex tasks into multiple independent sub-tasks and sort them by priority. Complete core functions first and postpone non-critical tasks. This way, even if the quota is exhausted, the core objectives have already been achieved.

4.2 Rational Configuration of Sub-Agent Mechanisms

Delegate simple, repetitive work (such as code formatting checks and simple bug fixes) to lightweight models or sub-agents, ensuring the main model only handles parts requiring deep reasoning. This can significantly reduce the consumption of high-quality compute.

Configuration example:

# Agent configuration example
main_agent:
  model: high-capability
  tasks:
    - Architecture design
    - Complex algorithm implementation
    - Critical bug analysis
sub_agents:
  - model: lightweight
    tasks:
      - Code formatting checks
      - Simple bug fixes
      - Unit test generation
  - model: medium
    tasks:
      - Module-level refactoring
      - Documentation generation

4.3 Time Window Planning

Understand the reset cycles of your quotas and schedule the start times of long tasks accordingly. For example, if the weekly quota resets on Monday, scheduling a long task to start on Monday will maximize the available quota.

4.4 Multi-Model Alternative Strategy

Do not bind your entire workflow to a single model. Select the appropriate model based on the task type: use the strongest reasoning model for mathematical proofs, the strongest code comprehension model for code auditing, and the highest output quality model for documentation writing. This allows you to distribute quota pressure across different models.

5. Trends and Outlook

5.1 Capabilities

The competition in high-end reasoning capabilities continues. The ability to explore open mathematical problems is currently a key metric for distinguishing top-tier models, and more models may enter this tier in the future. Meanwhile, the conciseness of reasoning chains and output quality are also gradually improving.

5.2 Quotas

As model capabilities increase, so does task complexity, which may further exacerbate quota pressure. Ideal solutions include:

• Differentiated billing based on task type: Simple tasks consume less, while complex tasks are allowed a higher single-run consumption limit;
• Elastic quota mechanisms: Allowing users to transfer unused quotas between different cycles;
• Finer-grained consumption transparency: Enabling users to understand real-time task consumption to make more informed decisions.

5.3 Advice for Developers

1. Establish your own evaluation system: Do not rely on a single benchmark; test model performance based on actual task types;
2. Plan compute budgets: Treat quotas as a finite resource and manage them like cloud costs;
3. Maintain multi-model flexibility: Different models offer significantly different cost-effectiveness on various tasks; flexible switching can maximize efficiency;
4. Focus on the marginal cost of long tasks: The closer to completion, the higher the consumption. Planning interruption points in advance is more important than remedial actions afterward.

6. Conclusion

The capability boundaries of LLMs are expanding rapidly, but compute quota constraints persist. For developers, understanding the interaction between these two dimensions and formulating rational task execution strategies accordingly is key to maximizing productivity at the current stage. Capability determines possibility; quota determines feasibility. Both are indispensable.