A 3-Trillion-Parameter Domestic LLM: Engineering Breakthroughs in Long-Horizon Agents and Coding

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This article analyzes a major version upgrade of a domestic 3-trillion-parameter LLM, covering its million-token context window, coding and terminal-agent capabilities, managed agent services for long-running tasks, inference cost structure driven by cache hit rates, and practical guidance for developers choosing between standard and high-speed variants.

Parameter Scale and Context Capacity: From “Bigger” to “More Usable”

A domestic large model at the 3-trillion-parameter level has recently completed a major version iteration, positioned by its developers as the most capable version to date. The core of this generational upgrade is not parameter stacking for its own sake, but three engineering goals: million-token context, strengthened coding and 3D creation capabilities, and targeted optimization for financial research and long-horizon agents.

At the architecture level, crossing into the 3-trillion-parameter range means the model absorbed a broader knowledge distribution during pre-training. What actually determines real-world experience, however, is how well the context window is utilized. A million-token context is not simply “supporting longer inputs” — it requires systematic optimization of attention mechanisms, KV cache management, and positional encoding; otherwise, information recall quality degrades noticeably over long contexts. Common industry approaches include tiered KV cache eviction strategies, hybrid designs combining sliding-window and global attention, and chunked scheduling of long-context requests on the inference serving side. For developers, this means re-evaluating how prompts are organized for long-document QA, cross-file code understanding, and multi-turn tool calling — placing the most critical information at the beginning and end of the context remains a reliable practice with current long-context models.

Notably, this release also introduces a high-speed inference variant that delivers roughly 2–3x higher output speed while maintaining essentially the same level of model intelligence. This dual-version strategy — “same capability, different speed” — is typically implemented through inference acceleration techniques such as quantization, speculative decoding, batch scheduling optimization, and more aggressive parallel partitioning. For developers, the value lies in decoupling capability from latency: use the standard version for offline batch tasks to control cost, and the high-speed version for interactive scenarios to ensure responsiveness. Both share the same prompts and evaluation baselines, reducing switching costs.

Coding Capability: Real Tasks in Terminal Environments Are the Real Test

Coding is one of the most noteworthy directions in this upgrade. Official figures show the model scores 65.7% on Terminal-Bench 4.0. This benchmark deserves elaboration: it is not a traditional function-level code completion test, but rather evaluates a coding agent’s ability to complete complex tasks in realistic, resource-constrained terminal environments, with dedicated test cases for each task. In other words, it measures whether an agent can actually get things done on a real machine — involving command execution, environment exploration, error recovery, dependency management, and a whole set of engineering behaviors.

For reference, another mainstream international closed-source model scores 66.4% on the same benchmark, while another scores approximately 58.2%. This places the model in the top tier on this test, with the gap to the leading model narrowed to within 1 percentage point. It is worth noting rationally that a single benchmark ranking does not equate to comprehensive capability parity, but a gap narrowed to this magnitude indicates that domestic models have achieved practical usability in the composite capability of “agent + terminal operations.”

On long-horizon coding tasks, internal Long-horizon test scores improved from 55.95% in the previous generation to 61.2%. The difficulty of long-horizon tasks lies in long task chains, many intermediate states, and errors that compound and amplify. A model that can write a single file may not remain stable in a task involving dozens of tool calls and multiple rounds of compilation and testing. A nearly 5-percentage-point improvement reflects progress in planning, state tracking, and self-correction.

For developers, when evaluating such models, it is advisable to build a small-scale task set covering the following dimensions:

# Evaluation dimensions (pseudocode)
tasks = {
    "environment exploration": "locate the build script in an unknown container and explain its purpose",
    "error recovery": "after a failed execution, read logs, identify the cause, and fix it",
    "multi-step refactoring": "rename an interface across multiple files and ensure tests pass",
    "dependency management": "resolve version conflicts and verify runnability",
}
# Record for each task: success rate, average tool calls, average time, manual interventions

Key metrics are not just success rate but also tool call counts and manual intervention counts — the latter directly determines whether an agent can truly replace human effort.

Managed Agent Service: The Runtime Foundation for Long-Horizon Tasks

Another highlight of this upgrade is the managed agent service. The idea is to offer the execution harness that supports long-running agents as cloud infrastructure, enabling enterprises and developers to run 24/7 long-horizon tasks with multi-agent collaboration support.

This service model addresses the most practical pain points in deploying long-horizon tasks. When agents run locally, they face a series of issues: process keep-alive, state persistence, tool permission isolation, failure retry, and log auditing. Once a task spans hours or even days, local environments can hardly support it reliably. Hosting the execution harness in the cloud essentially abstracts the “agent runtime” into a foundational service — analogous to what container platforms are to applications and what message queues are to asynchronous communication.

Architecturally, such services typically require several capability layers: a task scheduling layer for queuing, timeout handling, and retry of long tasks; a state storage layer for persisting agents’ intermediate memory and tool call records; a sandbox execution layer for isolating code execution and network access; and an observability layer for trace tracking and cost attribution. Multi-agent collaboration further requires message-passing mechanisms and role orchestration to prevent agents from waiting on each other or duplicating work.

A practical recommendation for enterprises: prioritize decomposing long-horizon tasks into “checkpointable” staged goals, persisting state after each stage so that an interrupted run can resume from the latest checkpoint rather than restarting the entire task. At the same time, set explicit tool whitelists and resource quotas for agents to prevent cost overruns caused by runaway long tasks.

Inference Cost Structure: Cache Hit Rate Is the Key Variable

On API pricing, the standard version costs 1.3 yuan per million tokens for cache-hit input, 13 yuan for cache-miss input, and 68 yuan for output; the high-speed version offers the same capability at roughly 2x the standard price.

The most important signal from these numbers: the input price difference between cache hits and misses is about tenfold. This means that in scenarios involving long contexts, multi-turn conversations, and agents repeatedly reading the same codebase or documents, the cache hit rate almost determines overall cost. Engineering strategies include: stabilizing the prompt prefix, placing unchanging system instructions and tool definitions at the front of the context, and avoiding unnecessary reordering of context structure in each request round. For agent applications, tool definitions and system prompts typically consume a large number of input tokens; effective prefix caching can reduce this cost to one-tenth.

Output pricing exceeding input pricing is an industry-wide phenomenon, so tasks sensitive to output length (such as long-form generation and large code generation) should have budget controls in place, with structured output constraints used where necessary to reduce redundant content.

Capability Boundaries and Selection Guidance

Overall, this generation of 3-trillion-parameter models exhibits several clear characteristics: context windows reaching the million-token level, coding and terminal-agent capabilities entering the top tier, a dual-version strategy decoupling capability from latency, and managed services filling the infrastructure gap for long-running execution.

Selection guidance for developers can be summarized in three points:

First, choose the version based on task latency sensitivity. Use the high-speed version for interactive coding assistants and real-time Q&A; use the standard version for offline code review and batch document processing, with cache hit rate as the main line of cost optimization.

Second, choose the execution mode based on task length. Short tasks can simply call the API locally; for tasks spanning hours and requiring multiple rounds of tool calls, prioritize the managed agent service and leave process keep-alive and state persistence to the infrastructure.

Third, build your own evaluation baseline. Public benchmarks can only serve as references; task distributions in real business scenarios vary widely. It is advisable to build a regression set from dozens of real tasks and continuously track three metrics: success rate, tool call counts, and cost per task.

It should be noted that model weights are planned to be released before November 4 — an important signal for teams requiring private deployment or deep customization. However, private deployment also requires assessing whether inference hardware memory and throughput match the realistic constraints of a 3-trillion-parameter scale.