Evaluating and Selecting LLMs for Coding and Long-Horizon Agentic Tasks

1. From Single-Turn Q&A to Long-Horizon Execution: Shifting Evaluation Paradigms

The focus of Large Language Model (LLM) applications is shifting from “single-turn Q&A” to “multi-step autonomous execution.” For developers, the most valuable evaluation question is no longer “Can the model write a correct function?” but rather:

• Can it independently complete a closed loop in a real terminal environment, including installing dependencies, modifying configurations, writing code, running tests, and iteratively fixing issues?
• Can it maintain goal alignment across an execution chain of dozens or even hundreds of steps without deviating from the endpoint due to intermediate errors?
• Can it drive professional software like game engines, 3D modeling tools, and office or financial analysis software to produce directly usable results?
• In knowledge-intensive tasks, can it suppress factual errors to a business-acceptable level?

These “long-horizon tasks” impose systematic requirements on a model’s planning, memory, error correction, and tool-calling capabilities, making them the primary battleground for current model upgrades and enterprise selection. This article analyzes five dimensions: real terminal tasks, long-horizon sustainability, professional toolchains, factual accuracy with security alignment, and finally, the equally critical aspect of billing structures and cost optimization.

2. Dimension 1: End-to-End Completion Rate in Real Terminal Environments

Function-level static code benchmarks can no longer effectively differentiate leading models. A more effective evaluation method is to connect the model to a restricted terminal environment and let it autonomously complete the entire pipeline “from an empty environment to a runnable result”:

1. Read the task description and project structure, then formulate an execution plan.
2. Autonomously install dependencies, modify configurations, and write/modify code.
3. Run tests, locate issues based on error messages, and fix them.
4. Output a runnable artifact that passes acceptance criteria.

The value of this evaluation lies in measuring the “engineering completion rate” rather than “code snippet accuracy”—covering the most time-consuming aspects of real development such as environment comprehension, dependency management, and failure recovery. Industry practices show that leading models currently achieve completion rates of roughly 60% to 70% on these restricted real-terminal tasks. These numbers convey two insights: First, long-horizon autonomous execution has transitioned from “demo-level” to the “usable range.” Second, about one-third of real engineering tasks still cannot be completed end-to-end, making human-machine collaboration a necessity.

Selection Advice: Do not settle for marketing claims of being “close to the best.” Extract 5 to 10 typical engineering tasks from your team and require the model to complete them in an offline environment without human intervention. Track the completion rate and average iteration steps. A small-scale internal blind test is typically more credible than any public leaderboard.

3. Dimension 2: “Sustainability” in Long-Horizon Tasks

The core challenge of long-horizon Agentic tasks is error accumulation. Assuming a single-step success rate of p, a 50-step task with a p as high as 97% yields a one-pass success rate of only about 22%. Therefore, evaluating long-horizon capabilities requires monitoring three key signals:

• Gap between step success rate and final success rate: A smaller gap indicates stronger model self-correction capabilities.
• Irreversible error rate: Whether the model falls into an infinite loop after deleting critical files or erroneously overwriting data.
• Context management strategy: Superior models actively summarize and archive completed steps to prevent attention dilution and “forgetting” caused by long conversations.

When building an internal long-horizon task set, refer to the following task template:

Task Example (Long-Horizon Regression Fix):
1. Given a repository with several legacy defects and a list of failing tests.
2. Require the model to fix all failing tests without introducing new defects.
3. Pass criteria: All regression tests green + code diff review approved.
Metrics: One-pass success rate / Average fix steps / Human intervention count.

Running multiple times with a fixed random seed and analyzing the statistical distribution yields much more reliable conclusions than a single demonstration.

4. Dimension 3: Professional Toolchain Invocation and Delivery

The differentiation of next-generation models increasingly lies in “whether they can drive professional software and deliver end-to-end.” Currently, the three most representative directions are:

1. Game Engine Scripts: Understanding the project structure, component lifecycle, and event models of mainstream game engines to directly produce compilable and runnable behavior scripts, covering high-frequency needs like character control and UI logic.
2. Parametric Modeling: Driving 3D modeling and CAD software via scripts to complete parametric 3D modeling—a critical step for efficiency gains in architectural and industrial design automation. During evaluation, separate “tool-assisted scores” from “pure generation scores”: the former tests toolchain integration, while the latter tests spatial comprehension.
3. Financial and Office Automation: End-to-end completion of the “data extraction -> modeling -> analysis -> drafting” research report pipeline, covering financial modeling, investment analysis, and industry reports. Evaluation must not only assess writing quality but also spot-check the formulas and logic of underlying models (e.g., discounted cash flow, comparable valuations).

The common evaluation standard for this dimension is the “end-to-end delivery rate”: the artifact must be immediately usable (scripts run, model files open, reports are ready for review) rather than just “snippet correct.” In practice, incorporate “can be opened directly in the target software” into the acceptance criteria, which is far stricter than text similarity metrics.

5. Dimension 4: Factual Accuracy and Hallucination Control

In knowledge-intensive tasks, hallucination is the primary risk. A rigorous evaluation should construct a “hallucination stress test set” that deliberately includes three types of error-prone samples:

• Similar but distinct entities (e.g., same-name institutions, easily confused product lines, pre/post-update versions).
• Chronologically error-prone events (e.g., reversed sequences, misattributed events).
• High-density numerical scenarios (e.g., frequent statements of years, ratios, amounts).

Simultaneously, track two metrics: Single-fact statement error rate and Proportion of responses containing at least one error. The former measures error density, while the latter better reflects user perception—users judge whether the “entire article is credible.”

Two crucial points when interpreting this data:

1. The stress set deliberately focuses on error-prone scenarios, meaning its error rate is significantly higher than everyday distributions. It cannot be directly extrapolated to “one error every N statements in daily use.” If a vendor explicitly notes this, it actually demonstrates a rigorous evaluation attitude.
2. A decrease in single-fact error rate does not equate to a synchronous improvement in the whole-article error-free rate. Selection decisions should prioritize the latter.

Engineering countermeasures: For high-factuality scenarios, elevate hallucination control from an “intrinsic model capability” to a “systems engineering” task. Use Retrieval-Augmented Generation (RAG) for traceable evidence, require the model to output structured citations, and add a post-verification layer for numerical statements. Multi-layered stacking can typically suppress perceived errors to levels far below the model’s raw test performance.

6. Dimension 5: Security Alignment vs. Capability Ceilings

An often-overlooked dimension is the impact of security policies on capability ceilings. Mainstream industry practice involves proactively identifying and removing high-risk data (such as network offense/defense and exploit-related content) during mid-training and Reinforcement Learning (RL) phases to mitigate abuse risks.

Data removal comes at a cost. Taking an internal security evaluation for developers as an example, there might be a gap where an “unfiltered version solves about 90% of problems, while the final released version only solves about 80%.” These numbers indicate that security filtering has a visible, quantifiable impact on defensive development efficiency.

For enterprise users, this is actually a procurement evaluation point: focus on whether the vendor discloses such security-capability trade-off data and provides a methodology for their security evaluation. A model with a completely undisclosed evaluation criteria makes its security claims difficult to independently verify. Conversely, a provider willing to present a “before-and-after filtering comparison” usually implies a well-documented and auditable security process.

7. Cost Model: Understanding the Economics of Context Caching

For Agentic applications, the most significant cost leverage is often not “switching to a cheaper model” but effectively utilizing context caching. The prevailing industry billing structure can be abstracted into the table below (using standard input price as 1 unit):

Billing Item	Description	Relative Price Magnitude
Standard Input	Input tokens not hitting the cache	1x
Cache Hit	Portion hitting the shared prefix cache	~0.1x
Cache Write (Short TTL)	Written to cache, valid for minutes	~1x
Cache Write (Long TTL)	Written to cache, valid for ~1 hour	~2x
Output	Generated tokens	Multiple times the input
High-Speed Tier	Inference tier offering lower latency	Usually ~2x standard tier

A typical characteristic of Agent workloads is “fixed prefix + rolling additions”: system prompts, tool definitions, and historical summaries constitute the vast majority of input tokens per request and remain unchanged across turns—making them ideal candidates for cache hits.

# Estimating cache benefits for multi-turn Agent sessions (example values for magnitude demonstration)
input_price   = 13.0    # USD / million tokens
hit_price     = 1.3     # Cache hit price ≈ 10% of input price
fixed_prefix  = 50_000  # Unchanged system prompt + tool definitions per turn, tokens
new_per_turn  = 2_000   # New conversation per turn, tokens
turns         = 20      # Session turns
cost_no_cache = turns * (fixed_prefix + new_per_turn) * input_price / 1e6
cost_cached = (
    fixed_prefix * input_price / 1e6               # First turn write
    + (turns - 1) * fixed_prefix * hit_price / 1e6 # Subsequent hits
    + turns * new_per_turn * input_price / 1e6
)
# Conclusion: In this example, enabling caching reduces input-side costs by about an order of magnitude.

Three practical recommendations:

1. Prefix the “unchanging content”: Place system prompts, tool definitions, and reference documents at the very beginning of the request and maintain byte-level stability. Any minor modification (like injecting a timestamp) will break the prefix hit.
2. Select TTL tiers based on task cadence: If task intervals exceed the short TTL window (e.g., hourly batch processing), opting for a long TTL write (~2x) actually results in a lower total cost.
3. Reserve high-speed tiers for latency-sensitive pipelines: Real-time code completion and interactive chat can utilize the high-speed tier, while batch processing and offline evaluations should strictly use the standard tier.

8. Implementation: Building Your Enterprise Evaluation Matrix

Synthesizing the dimensions above, teams should establish a maintainable evaluation matrix and rerun it periodically:

Dimension	Key Metric	Data Source	Suggested Weight
Real Terminal Tasks	End-to-end completion rate	Internal engineering task set	35%
Long-Horizon Sustainability	Multi-step one-pass success rate	Internal long-horizon task set	20%
Toolchain Delivery	First-time usable artifact rate	Typical target domain tasks	20%
Factual Accuracy	Whole-article error-free rate	Hallucination stress set	15%
Security & Compliance	Security evaluation disclosure completeness	Procurement evaluation checklist	10%

Accompany this with two disciplines: First, sanitize and version-lock the evaluation tasks to prevent “tailoring tasks to fit results.” Second, perform a full rerun after every model update to quantify both upgrade benefits and regression risks.

Conclusion

The essence of model selection is not “which is the most powerful,” but “which is the most reliable on your own task distribution under cost constraints.” Moving evaluation from public leaderboards back to business scenarios, from isolated demos back to statistical distributions, and expanding from capability metrics to a comprehensive ledger of security and costs is the only way to achieve predictable, reproducible engineering ROI.