GLM 4.6 vs Claude 4.5 Sonnet – Which Coding LLM Leads the Pack
GLM 4.6 vs Claude 4.5 Sonnet – Which Coding LLM Leads the Pack
Introduction
The race for the most capable coding‑focused large language model (LLM) has taken another turn with the early‑access release of GLM‑4.6‑6 from Zhipu AI. At the same time, Claude 4.5 Sonnet from Anthropic has become generally available, promising a larger context window and stronger tool‑augmented reasoning. In this article we compare the two models on a variety of benchmarks, real‑world coding tasks, and cost considerations to determine which one currently offers the best value for developers.
GLM‑4.6‑6 Overview
Model Architecture
- Parameters: 355 billion‑parameter mixture‑of‑experts (MoE) backbone with roughly 35 billion active parameters per inference step.
- Release Position: Successor to GLM‑4.5, which was already regarded as the strongest open‑weight coding model.
- Availability: Currently offered only as the “big” MoE variant; no lightweight “air” version for local inference.
Promised Improvements
- Parity or superiority to Claude 4.5 Sonnet on coding benchmarks.
- Enhanced alignment with human preferences for readability and role‑playing scenarios.
- Better cross‑lingual performance.
- Retains the affordable pricing that made GLM‑4.5 popular among developers.
Claude 4.5 Sonnet Overview
Core Features
- Context Window: Expanded to 200 k tokens, matching the previous top‑tier models.
- Reasoning Mode: Optional tool‑augmented reasoning that claims state‑of‑the‑art performance on several evaluation suites.
- Alignment: Emphasizes human‑like style, readability, and role‑play consistency.
- Cross‑Lingual Tasks: Further improvements over earlier Claude versions.
Pricing
- Significantly higher per‑token cost compared with open‑weight alternatives, making it a premium option for enterprises.
Testing Methodology
The evaluation consisted of three main components:
- Raw Coding Benchmarks – Straightforward prompt‑response tasks without any external tooling.
- Agentic Benchmarks – Scenarios that require the model to orchestrate multiple steps, such as generating full applications or interacting with simulated agents.
- Real‑World Code Generation – End‑to‑end creation of apps (e.g., a movie‑tracker using Expo and TMDB API) and interactive scripts (e.g., a terminal‑based Go calculator).
All tests were run on the Ninja Chat platform, which provides a side‑by‑side playground for multiple LLMs. The same prompts were used across models to ensure a fair comparison.
Performance Results
Raw Coding Benchmarks
- GLM‑4.6‑6 placed 4th on the leaderboard without reasoning and 5th with reasoning – a remarkable showing for an open‑weight model.
- Claude 4.5 Sonnet and Claude Opus retained the top two spots, but at a considerably higher cost.
Agentic Benchmarks
- GLM‑4.6‑6 rose to 2nd place, outperforming Claude 4.5 Sonnet in complex multi‑step tasks.
- The model demonstrated strong planning abilities, though the dedicated “reasoning” variant offered only marginal gains for pure coding.
Real‑World Code Generation
| Task | GLM‑4.6‑6 | Claude 4.5 Sonnet |
|---|---|---|
| Movie Tracker App (Expo + TMDB) | Clean UI, smooth animations, minor font issues; overall the most cohesive generation observed. | Good design but repeatedly hard‑codes the TMDB API key, a security lapse. |
| Go Terminal Calculator | Responsive to terminal size, well‑structured code, high visual fidelity. | Functional but less adaptive to resizing. |
| FPS Game Modification (Godo engine) | Added health bar and jump‑affected mechanics in a single pass; moves are legal and the logic is sound. | Implemented core features but left integration steps incomplete, requiring manual stitching. |
| Open‑Source Repo Query | Failed – could not retrieve repository information. | Similar failure, indicating a broader limitation for both models. |
Overall, GLM‑4.6‑6 produced more reliable, end‑to‑end solutions with fewer manual adjustments.
Cost and Accessibility
- GLM‑4.6‑6 remains open‑weight, allowing the community to host the model on their own hardware. Its pricing on Zhipu AI’s cloud tier is dramatically lower than Anthropic’s, making it attractive for startups and hobbyists.
- Claude 4.5 Sonnet charges premium rates (approximately $315 per million tokens for combined input/output), which can quickly become prohibitive for heavy coding workloads.
- The lack of a lightweight local version of GLM‑4.6‑6 is a drawback for developers who need on‑device inference, but the cost advantage often outweighs this limitation.
Comparative Summary
Strengths of GLM‑4.6‑6
- Competitive coding performance despite being open‑weight.
- Superior multi‑step (agentic) capabilities.
- Affordable pricing and open‑source availability.
- Consistently better end‑to‑end app generation.
Weaknesses of GLM‑4.6‑6
- No low‑parameter “air” variant for local inference.
- Occasional minor visual issues (e.g., SVG shape inaccuracies).
Strengths of Claude 4.5 Sonnet
- Largest context window (200 k tokens).
- Strongest raw benchmark scores when cost is not a factor.
- Advanced reasoning mode for complex problem solving.
Weaknesses of Claude 4.5 Sonnet
- High per‑token cost limits scalability.
- Persistent security‑related coding habits (e.g., hard‑coding API keys).
- Marginal improvements over previous Claude versions relative to price increase.
Verdict
For developers whose primary concern is effective, affordable coding assistance, GLM‑4.6‑6 emerges as the clear winner. It delivers near‑top benchmark performance, excels in agentic tasks, and produces robust, production‑ready code—all while remaining open‑weight and cost‑effective.
Claude 4.5 Sonnet still holds a niche for organizations that can justify the expense and need the extended context window or specialized reasoning features. However, the modest performance gains do not currently justify the steep price differential for most coding workloads.
Conclusion
The early‑access release of GLM‑4.6‑6 signals a turning point in the open‑weight LLM landscape. By narrowing the gap with proprietary giants like Anthropic, it democratizes high‑quality AI‑assisted development and challenges the notion that premium pricing is the only path to top‑tier performance.
Developers looking to integrate a coding LLM into their pipelines should seriously consider GLM‑4.6‑6 as the default choice, reserving Claude 4.5 Sonnet for specialized scenarios where its unique features outweigh the cost.
Share your experiences with these models in the comments, and stay tuned for further updates as both platforms continue to evolve.