When LLMs Start Operating Professional Tools Directly: A Capability Leap from “Can Chat” to “Can Do”

Over the past year, industry expectations for large models have been undergoing a subtle but critical shift. Early on, we cared about whether a model could write poetry, hold a conversation, or translate text. Later, we focused on whether it could write code or pass exams. Now, a growing number of frontline developers and enterprise users are asking a sharper question: Can it behave like a junior engineer—open a terminal, operate professional software, and deliver a concrete work product?

The essence of this question is a migration from “language generator” to “tool executor.” In a recent version update worth noting, a domestic model scored 65.7 on a terminal-level benchmark, surpassing GPT-6 Astra, which had long held the top spot, and trailing only the flagship version from another leading overseas lab. That score means little in isolation, but placed on the timeline since 2025, it signals a phase shift: open-source models are closing the gap with the closed-source first tier on the hardest dimension—real-environment task completion.

This article does not center on any single brand. Instead, it breaks down the technical implications, evaluation methods, and practical boundaries of these capabilities from the perspective of developers actually using them.

1. Why Terminal-Level Benchmarks Deserve Serious Attention

When many developers first encounter terms like “Terminal Bench,” their initial reaction is: isn’t this just another leaderboard? But anyone who has actually run these evaluations realizes that terminal-level benchmarks are far more difficult than traditional code generation tests.

Traditional code evaluations typically assess whether a model can generate a syntactically correct, logically usable function or algorithm given a clear specification. These tasks have well-defined input/output constraints and low evaluation costs, but they are also easy to optimize against. By contrast, terminal-level benchmarks are closer to real-world scenarios like:

• Giving the model a real code repository and asking it to locate a specific bug;
• Requiring the model to install missing dependencies, modify configuration, run tests, and confirm they pass;
• Giving the model an incomplete open-source project and asking it to restore the build process based on the README;
• Finding the actual location that needs modification in an environment filled with irrelevant files and historical commits.

These tasks are hard because the model must not only “write code” but also understand feedback from the execution environment, adjust strategy based on error messages, and even make reasonable trade-offs when encountering version conflicts. If we translate 65.7 into engineering intuition, it means: the model can already independently handle a substantial portion of a junior engineer’s scripted daily work, but still has a significant failure rate when facing highly ambiguous or multi-step backtracking tasks.

From a practical standpoint, when evaluating such models, development teams should not fixate solely on the final total score. They should pay more attention to three sub-metrics: task completion rate, single-attempt success rate, and the explainability of failure modes. Many models perform well on simple tasks, but once a task requires an operation chain exceeding 15 steps, the success rate drops off a cliff. This matters more than the overall score in determining whether the model can truly enter a production pipeline.

2. The Other Meaning of the Cost Curve: Affordable Only If It Can Actually Compute

Beyond performance gains, the cost of terminal-level tasks deserves separate discussion. A frequently overlooked fact is: in scenarios requiring multi-turn tool calls and long context maintenance, inference cost growth is often five to ten times higher than a single response.

Suppose a terminal task requires the model to go through the following loop:

1. Read the current directory structure;
2. Inspect a build script;
3. Execute the script and capture the error;
4. Modify configuration based on the error;
5. Re-execute and confirm it passes.

Each step consumes tokens, and each round must carry the full context from previous rounds. If the model’s per-call price looks cheap, the total cost in such multi-turn scenarios can far exceed expectations after amplification. More critically, if the model frequently fails at step three or four, multiple retries are triggered, further compounding the cost.

Therefore, when observing a “lowest cost” claim, developers should ask: at what success rate was this cost measured? The per-task cost at a 50% success rate versus a 90% success rate differs by orders of magnitude. If a model is cheap but requires repeated retries, the effective cost may be far higher than a more expensive model that succeeds on the first attempt.

From an engineering perspective, a more reasonable evaluation method is to calculate “average total token consumption for successfully completing a task,” rather than simply comparing per-token prices. This is also why some frontier model technical reports publish cost data alongside task success rates—either one alone is insufficient to support a decision.

3. 3D Modeling Script Generation: From “Generating Text” to “Manipulating Geometry”

If terminal-level code tasks test a model’s system operation capabilities, the 3D modeling direction reveals another dimension: can the model translate abstract spatial intent into precise, executable geometry operation sequences?

In the past, the common approach for large models in 3D was to generate some intermediate representation, such as mesh data or point cloud descriptions, which an external renderer would then convert. This pipeline has inherent precision loss and is difficult to integrate into editing workflows. Now, a clear change is emerging: models increasingly generate scripts for professional modeling software directly.

Taking a common open-source 3D creation platform as an example, the model can output code structures like the following:

import bpy
# Clear scene
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete(use_global=False)
# Create a torus as base geometry
bpy.ops.mesh.primitive_torus_add(
    major_radius=1.8,
    minor_radius=0.4,
    location=(0, 0, 2.2)
)
current_obj = bpy.context.active_object
# Adjust material properties
mat = bpy.data.materials.new(name="mat_metal")
mat.use_nodes = True
mat.node_tree.nodes["Principled BSDF"].inputs["Metallic"].default_value = 0.85
mat.node_tree.nodes["Principled BSDF"].inputs["Roughness"].default_value = 0.2
current_obj.data.materials.append(mat)

The significance of such code is not in the generation itself, but in the fact that it enters an “editable, parameterizable, reusable” engineering workflow. What the model generates is no longer a one-off image, but a source file that can be further modified. After receiving the script, developers can adjust parameters, swap materials, modify geometry constraints, and then import the result into a rendering pipeline or 3D printing slicer.

In tasks with higher structural complexity—such as interior landscape modeling or mechanical part assembly—the model needs to demonstrate understanding of spatial reference frames and hierarchical relationships. Incorrect orientation judgments, coordinate offsets, or parent-child relationship errors can all cause structural problems when the final model is loaded. This is why such capabilities are still best deployed in a “human-machine collaboration” mode: the model generates the first draft, and the engineer reviews key parameters and constraints.

4. Financial Document Reasoning: From Retrieval-Based Q&A to Cross-Document Analysis

The changes in the financial domain are the most easily underestimated among the directions discussed in this article, but their practical value for enterprise users may be the highest. Traditional document Q&A systems are mostly based on retrieval-augmented generation (RAG) architectures: first recall relevant passages from a knowledge base based on the question, then have the model generate an answer. This approach performs well when “finding a specific statement,” but falls short in three types of scenarios:

• Cross-document verification: a metric’s definition is in Document A, its calculation methodology in Document B, and its actual value in Document C;
• Multi-step numerical reasoning: first calculate the growth rate, then analyze year-over-year change, then compare against industry benchmarks;
• Contradiction identification: the same entity discloses information with inconsistent standards across annual reports and temporary announcements.

A typical financial analysis task might include the following steps:

1. Filter 36 announcements for documents related to a specific secondary offering;
2. Extract the issue price, number of shares issued, and net proceeds;
3. Verify the correspondence between the use of proceeds and board resolutions;
4. Calculate the impact on earnings per share after the offering;
5. Compare the average discount rate of similar-scale offerings by peer companies over the past 12 months.

In this process, the model needs to simultaneously manage context across multiple documents, maintain intermediate calculation states, and backtrack when necessary. Any numerical error at any step can cause the final conclusion to deviate from reality. According to public evaluation results, the new model version scores 64.4 on such tasks, placing it at the leading position among all currently known models. What is more noteworthy behind this number is: error rates have declined, but error types are also changing. Early model errors were often “can’t find the information” or “completely miscalculated,” while current errors are more concentrated in “found incomplete information” or “reasoning direction is correct but precision is insufficient.” The latter, while frustrating, is easier to backstop in engineering through cross-validation and manual review.

5. The Engineering Significance of Hallucination Control

The hallucination problem was widely discussed before 2024, but often remained at the level of “will the model confidently make things up?” In the era of tool execution and multi-step reasoning, the impact of hallucinations has become more insidious and more destructive.

In conversational scenarios, a false statement might merely mislead the user. But in code execution scenarios, a fabricated function name can crash a program; in data analysis scenarios, a made-up field name can cause an entire data pipeline to produce incorrect results; in financial scenarios, a fabricated regulatory provision can trigger substantive decision bias.

According to internal stress test data, the new generation model’s error rate on difficult-level hallucination tests dropped from 23.4% to 8.3%. The magnitude of this change is more meaningful than the surface numbers. If it dropped from 23.4% to 18%, that would just be “a bit better.” But dropping to 8.3% means that across a large number of routine tasks, hallucination has become a relatively low-probability event—this is a psychological threshold for whether a model can enter production environments.

However, it is important to remain clear-headed: current hallucination evaluations primarily cover relatively clear “factual errors,” while “logical errors,” “methodological biases,” and “omission-based misleading” in real scenarios remain difficult to systematically capture. When using such models for critical tasks, developers should still establish independent verification steps rather than relying on the model’s own “confidence level.”

6. Capability Transfer Brought by Post-Training Scale Expansion

The changes described above do not come from revolutionary innovations at the model architecture level, but rather from sustained investment in the post-training phase of the same generation of models. This trend has become increasingly evident since 2025: marginal improvements in base models are slowing, but task-level capability gains from post-training are still being rapidly released.

Specifically for the directions discussed in this article, the role of post-training manifests at three levels:

First, optimization of tool-calling trajectories. By constructing large numbers of samples with complete call chains, models gradually learn when to observe the environment before acting, when to execute directly, and when to stop and confirm with the user.

Second, improved planning capability for long-horizon tasks. Early tool-calling models could barely complete 3-to-5-step task chains, but now after specialized training, some models can reliably execute operation sequences of 10 or more steps, with a degree of self-repair capability when anomalies occur mid-sequence.

Third, fusion of domain knowledge with general capabilities. Improvements in vertical tasks such as finance, 3D modeling, and cybersecurity cannot be achieved by simply piling on general-purpose corpora. They require injecting large amounts of high-quality samples with domain constraints during the post-training phase. This is also why the same generation of base models can perform very differently across vertical domains.

The practical implication of this rhythm—“slowing base breakthroughs, intensive post-training refinement”—for developers is: rather than chasing the latest model version number, pay attention to whether your domain has received sufficient attention during post-training. A model that has undergone specialized post-training in your domain is often more worth the time to validate than a model with stronger general capabilities but no domain adaptation.

7. Practical Recommendations: How to Integrate These Models into Real Workflows

For developers considering bringing such models into their team’s workflow, a few lessons may be more valuable than any benchmark score:

Start with “assist,” not one-step replacement. Let the model generate first drafts, draft scripts, organize document structures, and complete the first round of data verification, then have engineers confirm. The output quality and efficiency of human-machine collaboration typically exceeds what either side can achieve alone.

Build lightweight verification loops for core tasks. Whether the model generates code or calculation results, it should pass through independent automated validation. Code can run test cases, analysis results can undergo cross-sampling, and 3D models can be checked for geometric validity. The verification loop doesn’t need to be complex, but it must exist.

Record the complete trajectory of every failure. The intermediate process when a model fails is the most valuable debugging material. It is recommended to save complete records of tool calls and periodically analyze where failures occur, what the causes are, and whether there are patterns. This data is closer to reality than generic benchmarks.

Control context length; avoid greed. In terminal tasks and document analysis scenarios, longer context is not always better. The growth of irrelevant information dilutes the model’s attention and reduces accuracy at critical steps. A reasonable practice is to pre-filter inputs, retaining only content truly relevant to the current task.

Evaluate cost on a per-task basis. Don’t just look at per-token prices. Calculate the average total cost of completing a standard task. It is recommended to select 3 to 5 representative tasks within the team, run each task more than 20 times, record success rate, average time, and average token consumption, then compare horizontally with other approaches.

Model capabilities are moving from “looks impressive” to “actually works.” The most testing aspect of this transition period is not any single metric of the model, but the team’s depth of understanding of its capability boundaries and failure modes. Technology updates won’t wait for anyone, but those who truly use tools well are always the ones who understand their limitations.