From Terminal Tasks to 3D Modeling: The Expanding Frontier of LLM Capabilities and Model Selection in Practice

1. Why Terminal-Level Coding Tasks Have Become the New Litmus Test

For the past two years, the standard way to evaluate a code LLM was to provide a function signature or code snippet and check whether the model could complete a correct implementation. The biggest problem with this static evaluation approach is that it diverges sharply from real development work: real-world developers are not “continuing code”—they are installing environments in the terminal, running tests, reading logs, fixing errors, and committing changes.

As a result, the industry’s evaluation focus is shifting from “can it write” to “can it do”—letting models operate directly in a terminal environment, receive a natural-language engineering task such as “fix the defect in the repository that causes test failures,” and then autonomously complete the entire process from environment setup and dependency installation to code modification and test verification.

A notable recent industry signal: open-source models have now reached the global top tier in this kind of terminal engineering task evaluation, closely trailing closed-source frontier models and even surpassing them on some benchmarks. This means the boundary of “open-source usability” is expanding from everyday coding assistance to highly complex autonomous engineering tasks.

2. What Terminal-Level Tasks Actually Assess

Broken down, a model needs at least five capabilities simultaneously to score well on terminal tasks:

1. Environment awareness: After entering a project, quickly understand the directory structure, build system, and dependency relationships, and determine “what to change and what can be reused.” This is the prerequisite for all subsequent actions; misjudgment cascades and amplifies.
2. Long-horizon planning: Decompose a vague task into an ordered sequence of operations—which files to inspect first, which command to run first, and what fallback options to try in order after a failure. Models with weak planning skills commonly get stuck “spinning in circles,” consuming many rounds without converging on the goal.
3. Tool orchestration: Correctly use command-line tools, package managers, testing frameworks, version control, and other tools. This requires a solid grasp of command syntax, exit codes, pipe semantics, and process states—not just memorizing common commands.
4. Error recovery: After a command fails, locate the root cause from lengthy and noisy error messages and adjust strategy, rather than mechanically retrying or giving up early. This is a key differentiator between model tiers.
5. Termination judgment: Know when “the task is truly complete”—tests pass, the build succeeds, artifacts meet acceptance criteria—rather than “it looks about right.” Stopping too early is as harmful as not daring to stop.

A simplified agent execution loop looks like this (illustrative pseudocode):

task = "fix the test case in the repository that causes CI failure"
while not done:
    observation = terminal.execute(next_action)   # perceive execution output
    next_action = model.plan(task, history, observation)
    done = verify(task, terminal.state)           # verify via test/build results

Static completion tasks are decided in a single generation, while terminal tasks allow the model to iterate through “trial—observation—correction”—which is precisely closer to how human engineers work and better reflects the model’s usability in real production environments.

3. Beyond Performance, Cost Is the Other Half

Benchmark scores are only half of model selection; the other half is cost. The key metric is not “how much does a single call cost” but “how much does it cost to successfully complete one task.”

An intuitive estimation formula is:

Expected cost per success ≈ cost per call ÷ success rate

Under the approximation of independent retries, the lower the success rate, the higher the cost allocated to each success. This formula explains two common misconceptions:

• Lowest unit price does not necessarily mean best value. Typical cases have already appeared in industry evaluations: lightweight models known for low prices, while cheap per call, have clearly insufficient success rates on complex terminal tasks; factoring in retry rounds, context expansion after failures, human intervention, and rollback cleanup, the total cost actually exceeds alternatives. For this reason, the “most cost-effective” option among frontier models is often not the one with the lowest unit price—performance and cost are not a zero-sum relationship.
• High unit price does not necessarily mean waste. On high-value tasks such as core defect fixes and large-scale automated migrations, frontier models have higher “first-time-correct rates,” saving more expensive human time.

For enterprises, a practical evaluation process is:

1. Build an internal task set: Extract 30–100 representative real tasks from your own repositories (defect fixes, feature additions, build refactoring), covering different difficulty levels and language stacks.
2. Score on two dimensions: Record each model’s success rate, average number of attempts, token consumption, and converted cost.
3. Calculate cost per successful task: Use the formula above to derive “expected cost per successful task” rather than looking only at unit price.
4. Tiered routing: Assign simple tasks (formatting, comments, lightweight refactoring) to low-cost models; assign complex terminal tasks to frontier models; use rules or small classifiers for front-end dispatch and dynamic scheduling.

Several general cost-reduction measures also deserve inclusion: context compression and prompt cache reuse, batch inference scheduling, and distilling “script templates” for high-frequency fixed tasks rather than generating from scratch each time—codifying fixed workflows is itself the best cost optimization.

4. 3D Modeling and Spatial Reasoning: The Next Expansion of Capability Boundaries

If terminal tasks assess a model’s ability to “operate an engineering environment,” then 3D modeling assesses the ability to “operate in space”—which has direct value for gaming, industrial design, architectural visualization, e-commerce 3D display, digital twins, and other scenarios. Recent industry progress is equally noteworthy: the best results of a new generation of open-source models on tool-dependent 3D modeling evaluations are now very close to closed-source frontier models, reaching a historic high for the open-source camp.

4.1 Two Technical Routes: Protocol Integration vs. Direct Scripting

There are currently two main routes for letting LLMs drive 3D software:

• Protocol/plugin integration: Through standardized model-tool integration protocols, 3D software capabilities are wrapped into a set of controlled interfaces (“create cube,” “set material”), and the model calls them as needed. The advantage is controllable behavior and easy auditing; the limitation is that tool coverage is constrained by the pre-wrapped interface set, and it often falls short on flexible operations such as complex boolean operations and procedural layout.
• Direct scripting: Most professional 3D and CAD software have built-in script runtimes that expose complete scripting APIs. The model directly writes and executes scripts, expressing the full intent of “build geometry—adjust layout—assign materials—set camera—render output” in one pass. The advantage is coverage of nearly all software functionality, and the output is a versionable, reviewable, reusable code asset; the barrier is that the model must truly “understand” these APIs and the spatial concepts behind them.

The key breakthrough of the new generation of models: without pre-wrapped protocol integration, they can directly write scripts for professional 3D and CAD software to complete asset construction and refinement, and iteratively adjust layout, materials, geometry, and other elements. This marks the model’s evolution from “calling wrapped capabilities” to “natively mastering professional tools.”

4.2 Typical Workflow of Direct Scripting

A complete scripted 3D creation typically involves five stages:

1. Requirement translation: Convert a vague requirement like “make a Nordic-style living room scene” into a parametric description—dimensions, proportions, color scheme, lighting tone.
2. Script generation: Produce a structured script that explicitly declares geometry creation, transforms, materials, and camera.
3. Sandbox execution: Run the script in an isolated environment to avoid polluting production project files.
4. Visual verification: Render a preview image and return it to the model (or a human) for comparison with expectations.
5. Iterative correction: Adjust the script based on difference feedback, looping until it meets standards.

Illustrative pseudocode (abstract expression of a generic 3D software scripting interface):

scene = api.new_scene()
# Main geometry: tabletop
tabletop = api.create_box(size=(1.8, 0.9, 0.05))
tabletop.position = (0, 0, 0.75)
# Four table legs: procedural layout along symmetry axes
for dx, dz in [(-0.8, -0.35), (0.8, -0.35), (-0.8, 0.35), (0.8, 0.35)]:
    leg = api.create_cylinder(radius=0.03, height=0.72)
    leg.position = (dx, 0.36, dz)
# Materials and lighting
wood = api.material(color=(0.72, 0.5, 0.32), roughness=0.55)
tabletop.material = wood
api.add_light(type="area", intensity=800, position=(0, -2, 3))
# Camera and output
api.set_camera(look_at=(0, 0, 0.4), fov=45)
api.render(output="preview.png")

Note that the value of this script goes beyond “drawing an image”: it is a reusable procedural asset—change one parameter and you can batch-generate an entire furniture set in the same style; connect it to a business system and you can automatically produce 3D display assets from product data, fully handing repetitive labor to programs.

4.3 Three Challenges of Spatial Reasoning

• Coordinate systems and transforms: Local/world coordinate systems, rotation order, scaling, and mirroring—a slight misunderstanding by the model produces results that “look right but are actually wrong,” so acceptance cannot rely on visual inspection alone.
• Topology and assembly constraints: Fit relationships of mechanical parts and collision detection of scene objects require numerical-level understanding of spatial constraints, not semantic-level “roughly right.”
• Visual closed loop: Single-pass generation rarely gets it right the first time; success depends on the iterative ability to “render—compare—correct”—which essentially returns to the error recovery capability in terminal tasks. The capabilities across these two tracks are interconnected.

5. Enterprise Adoption: An Engineering Checklist

To put these capabilities into real use, proceed according to the following checklist.

1) Security and isolation. Both terminal execution and script execution must run in containerized sandboxes: restrict network access, set CPU/memory quotas, mount only the working directory in the filesystem; uniformly store 3D project artifacts in object storage with version snapshots for rollback and auditing.

2) Evaluation and regression. Solidify the internal task set into nightly regression tasks, continuously track each model’s success rate curve and cost curve, and alert when any metric degrades noticeably. Model upgrades should not rely solely on official release notes—let your own task set speak.

3) Infrastructure. From evaluation to production, the pipeline requires general capabilities including GPU compute, container platforms, object storage (for script artifacts and rendered assets), message queues (for asynchronous task orchestration), and monitoring/alerting. These can be assembled through cloud platforms—for example, general services such as containers, object storage, compute, and monitoring offered by Baidu AI Cloud and similar providers can support the complete loop from model evaluation and batch production to online serving, saving the overhead of building and maintaining your own infrastructure.

4) Human-machine collaboration boundaries. High-risk changes (such as core library defect fixes) must retain human review; 3D assets need lightweight acceptance criteria (geometry dimensions, material parameters, rendered reference image comparison). Codify the task tiers of “fully automated” versus “human confirmation” into the workflow rather than relying on ad-hoc judgment.

6. Conclusion

The capability boundaries of large models are undergoing two key expansions: one from “completing code” to “operating a terminal to complete engineering tasks,” and one from “generating text” to “operating in space for 3D creation.” For enterprises, the right approach is not to chase single-point benchmark scores but to establish a quantitative evaluation system of “success rate × cost,” codifying task tiering, model routing, sandboxed execution, and regression evaluation into engineering processes. As these two high grounds—terminal tasks and 3D creation—are gradually conquered, the vision of “models as digital employees” is moving from slogan to an engineering reality that can be scheduled.