Haidian’s New 3-Trillion-Parameter Open-Source Model: A Technical Evolution Analysis

1. Background: The Parameter Ceiling for Open-Source Models Pushes Higher

The Haidian AI industry cluster has reached another important technical milestone. On October 24, a large-model company in the district officially released its next-generation flagship open-source model. This is the first formal release in the series and the most capable 3-trillion-parameter model to date, supporting a 1-million-token context window and remaining among the largest open-source models in the world by parameter count. According to the official plan, full model weights will be made available by November 4.

For developers and enterprise technical decision-makers, the core value of this announcement is not simply “more parameters” but three quantifiable engineering signals:

1. Open weights are obtainable: Full weights will be released within the publication window, meaning enterprises can pursue private deployment, domain fine-tuning, and self-built inference stacks without being constrained by the capability boundaries and invocation costs of closed-source APIs.
2. Million-token context is becoming a standard direction: A 1-million-token window makes it possible to complete whole-repository code retrieval, long-document analysis, and multi-turn long-horizon agent tasks within a single session.
3. The cost-performance structure is shifting: Official disclosures indicate that invocation pricing is roughly 20% to 50% of comparable products from top overseas closed-source models, directly changing the cost model for large-scale inference scenarios.

It is important to emphasize that parameter scale alone does not equal capability. A 3-trillion-parameter scale typically implies a sparse Mixture-of-Experts (MoE) architecture—the total parameter count is enormous, but the parameters activated in a single forward pass are far smaller than the total, thereby expanding model capacity while keeping inference costs controllable. Understanding this is a prerequisite for assessing the feasibility of deploying such models.

2. From Base to Post-Training: The Engineering Path Behind Capability Gains

The released model is a post-training version of the base model, with the core change being a significant increase in compute during the post-training stage. According to official statements, to support large-scale joint training across hundreds of downstream domains, every layer of the technology stack received targeted optimization, including the reinforcement learning framework, long-context inference, training efficiency, sandbox infrastructure, scheduling systems, and a shared platform for environment quality checks.

This description, though seemingly straightforward, reveals three key engineering challenges in ultra-large-scale reinforcement learning training that deserve layer-by-layer unpacking:

2.1 Reinforcement Learning Framework: From Single-Task to Joint Training Across Hundreds of Domains

Traditional RLHF/RLAIF pipelines are usually designed around a single or small set of task families, with reward models tightly coupled to training environments. When training objectives expand to hundreds of downstream domains, the framework must address:

• Task sampling and ratio scheduling: Sample difficulty and reward sparsity vary enormously across domains, requiring dynamic ratio adjustment to prevent any single domain from dominating gradient updates;
• Reward signal standardization: Code tasks produce discrete pass/fail signals, while knowledge tasks mostly involve continuous scoring; these must be unified into a comparable reward scale;
• Training stability: Large-scale joint training is prone to reward hacking, requiring environment quality-check mechanisms to continuously filter low-quality samples and exploitable evaluation environments.

2.2 Long-Context Inference: Balancing Compute and Memory Under a Million-Token Window

A 1-million-token context places stringent demands on the inference stack. Common engineering combinations include chunked attention, tiered KV-cache storage, and prefix-cache reuse. The key point: long context cannot merely “fit” the input; it must also keep time-to-first-token (TTFT) and decoding throughput within acceptable ranges. This is precisely why long-context inference must be optimized in tandem during post-training—if the inference stack cannot efficiently support long sequences, the training returns from long-horizon agent tasks will be swallowed by infrastructure bottlenecks.

2.3 Sandboxes and Scheduling: Converting Every Unit of Compute into Stable Returns

Code and agent tasks require execution, verification, and feedback in real or near-real environments. Sandbox infrastructure must satisfy three requirements: isolation, resettability, and concurrency density. The scheduling system must perform fine-grained preemption and backfilling across heterogeneous tasks to reduce GPU idle time. The official emphasis on “ensuring every additional unit of reinforcement learning compute translates into stable, efficient returns” essentially addresses the problem of effective training compute utilization (an “effective training ratio” beyond MFU).

3. Benchmark Performance: Positioning Across Code, Agent, and Knowledge Tasks

Officials disclosed that the model reaches or approaches the level of the world’s strongest closed-source models on code, agent, and knowledge benchmarks. The most informative result is Terminal-Bench 4.0: this benchmark evaluates coding agent capabilities through resource-constrained terminal tasks in real environments, with strict dedicated tests for each task to avoid vague “looks correct” scoring.

Public data is as follows:

Model Category	Terminal-Bench 4.0 Score
This released model	65.7%
Top overseas closed-source model A	58.2%
Top overseas closed-source model B	66.4%

The data shows the model has surpassed one top closed-source model and trails the other by a mere 0.7 percentage points. For an open-weight model, the significance lies in this: on agent tasks that require real execution feedback under resource constraints, open-source solutions have entered the same discussion range as the closed-source first tier.

At the same time, benchmark scores should be viewed rationally: terminal benchmark scores are heavily influenced by task distribution, sandbox environment versions, and retry policies. Cross-model comparisons should focus on whether evaluation protocols are consistent rather than merely comparing numbers. Enterprises should build their own in-domain evaluation sets when selecting models.

4. Capability Profile: Long-Horizon Coding and Complex Simulation

Beyond benchmark scores, official descriptions of capability improvements focus on four scenario categories: programming, 3D creation, financial research, and long-horizon agents. Two details deserve attention from technical readers:

First, the scale span of coding tasks. Officials state that capabilities cover “everything from fixing a single issue in a real codebase to building a complete project from scratch.” These two task types have fundamentally different engineering requirements: the former tests repository-level code understanding and precise minimal edits, while the latter tests architectural planning, dependency management, and multi-file consistency. Covering both ends typically relies on joint enhancement of long-context retrieval and long-horizon planning capabilities.

Second, the complex system simulation case. In an internal test, the model built a human walking dynamics simulator with an integrated training and evaluation pipeline, based on an open-source human anatomy model containing 296 skeletal components (including cartilage) and 402 muscle components. The information value of this case: it requires the model to understand anatomical structural constraints, physical dynamics modeling, and closed-loop training-evaluation design—a typical “cross-domain long-horizon engineering task” far beyond single-file code generation. Such cases reflect model usability in real R&D workflows more than benchmark scores alone.

5. High-Speed Version: Throughput Trade-offs at Equivalent Intelligence

A high-speed version released alongside the standard model delivers approximately 2 to 3 times the output speed of the standard version while maintaining equivalent intelligence. This design corresponds to clear engineering scenario divisions:

• Standard version: Suited for complex reasoning, long-horizon agents, repository-level refactoring, and other tasks demanding high single-output quality and tolerating higher latency;
• High-speed version: Suited for interactive coding completion, batch document processing, high-concurrency online services, and other scenarios sensitive to time-to-first-token and throughput.

It should be noted that “equivalent intelligence” is typically a vendor’s claim on its own evaluation sets. In actual deployment, acceleration is often achieved through quantization, speculative decoding, distillation, and other techniques, and capability degradation is not uniform across tasks. Enterprises are advised to evaluate both versions on their own business distributions before deciding traffic split ratios.

6. Deployment Recommendations: How Enterprises Should Evaluate Such Open-Source Models

Based on the above, here is an actionable evaluation path:

1. First verify private deployment feasibility: Confirm weight formats, license terms, and inference framework support; estimate memory usage and throughput on target hardware. A 3-trillion-parameter MoE model has high memory and interconnect bandwidth requirements, so capacity planning should be done in advance.
2. Build domain evaluation sets: Do not rely directly on public benchmarks. Sample from your own codebases, tickets, and documents to build a small evaluation set of 100 to 300 tasks, covering accuracy, first-pass rate, and average interaction turns.
3. Stress-test real long-context gains: Measure time-to-first-token and task completion rates at 100K, 500K, and 1M token levels to determine whether the million-token window is “usable” or merely “fillable.”
4. Compare dual-version costs: Use cost per million tokens of effective task completion as the unit—rather than simply comparing unit prices—to decide the traffic ratio between standard and high-speed versions.
5. Plan the post-training pipeline: The greatest value of open weights is the ability to continue training. Prepare domain data cleaning, reinforcement learning environment setup, and sandbox resources in advance to turn “fine-tunable” into actual capability gains.

7. Conclusion

The technical significance of Haidian’s 3-trillion-parameter open-source model can be summarized in three points: pushing parameter scale to a new level with a sparse architecture, converting post-training compute into stable capability through full-stack engineering optimization, and returning choice to enterprises through open weights. For developers, the action truly worth investing in is not watching parameter numbers but establishing evaluation baselines in their own scenarios as quickly as possible and integrating such models’ long-context and long-horizon agent capabilities into actual R&D workflows. The upper bound of the parameter race is set by vendors; the upper bound of capability landing depends on how well engineering teams control evaluation and integration.