Executive Summary

The integration of Zhipu AI’s open-source GLM-4.6 model into Anthropic’s Claude Code agentic coding tool offers developers a powerful new option for AI-assisted programming. GLM-4.6 is a 355-billion-parameter Mixture-of-Experts (MoE) model with a 200,000-token context window (Source: blogs.novita.ai) (Source: medium.com). In benchmarking and tests, GLM-4.6 demonstrates near-parity performance with Claude Sonnet 4 (Anthropic’s flagship model) on many coding and reasoning tasks. In head-to-head “CC-Bench” coding evaluations, human evaluators recorded a 48.6% win rate for GLM-4.6 versus Claude Sonnet 4 (Source: howaiworks.ai) (Source: www.communeify.com). This reflects that GLM-4.6 can solve nearly half of multi-turn development tasks better, roughly on par with Sonnet 4. In public benchmarks, GLM-4.6 outperforms its predecessor (GLM-4.5) and also exceeds Sonnet 4 on several tests (e.g. 93.9% vs 74.3% on AIME-25 math reasoning; 82.8% vs 48.9% on LiveCodeBench coding) (Source: www.communeify.com). However, on pure code-completion metrics Sonnet 4.5 still leads (e.g. 77.2% vs GLM’s 68.0% on SWE-bench Verified) (Source: www.communeify.com) (Source: aiwiki.ai).

A key advantage of GLM-4.6 is cost and efficiency. GLM-4.6 uses ~15% fewer tokens than GLM-4.5 to complete tasks (Source: howaiworks.ai), and its API pricing is much lower: roughly $0.60 per million input tokens and $2 per million output tokens compared to Claude’s $3/$15 (Source: www.implicator.ai) (Source: aiwiki.ai). This can translate to order-of-magnitude savings in high-volume code-agent use. For example, published CC-Bench logs show GLM-4.6 averaging ~651K tokens per task vs ~763K for GLM-4.5 (Source: www.implicator.ai). In CHA framework, when agents iterate millions of tokens per day, a 15% reduction reads through all runs can substantially cut costs (Source: www.implicator.ai).

Model and integration. Claude Code is an interactive CLI agent for software development that normally uses Anthropic’s Claude LLMs (Source: docs.claude.com). Novita AI provides an Anthropic-compatible API endpoint so developers can configure Claude Code to call GLM-4.6 instead (by setting environment variables such as ANTHROPIC_BASE_URL=https://api.novita.ai/anthropic and ANTHROPIC_MODEL=zai-org/glm-4.6 (Source: blogs.novita.ai) (Source: blogs.novita.ai). Once set up, Claude Code in the terminal or IDE invokes GLM-4.6 under the hood. This setup allows GLM-4.6’s full features (200K context, tool use support) to be used in a familiar developer workflow (Source: blogs.novita.ai) (Source: blogs.novita.ai).

Table 1 summarizes key specifications of GLM-4.6 and Claude Sonnet 4.5. It shows that while Sonnet 4.5 remains closed-source with undisclosed parameters, GLM-4.6 is fully open (MIT license) and offers comparable or larger context. The cost per token is far lower for GLM-4.6. These differences lead to distinct trade-offs in practice. Overall, using Claude Code with GLM-4.6 yields competitive coding performance at dramatically lower cost (Source: www.implicator.ai) (Source: www.communeify.com), but developers must consider that Sonnet 4.5 still holds state-of-the-art accuracy for critical code tasks (Source: aiwiki.ai) (Source: www.implicator.ai).

Key findings:

• Performance (coding): GLM-4.6 matches or exceeds Claude Sonnet 4 on many benchmarks (e.g. 48.6% win in CC-Bench) (Source: howaiworks.ai) (Source: www.communeify.com). Sonnet 4.5 still scores higher on single-turn coding (SWE-bench) (Source: www.communeify.com) (Source: aiwiki.ai).
• Efficiency and Cost: GLM-4.6 uses ~15% fewer tokens than GLM-4.5 (Source: howaiworks.ai) and costs about 90% less per token than Claude 4.5 (Source: www.implicator.ai) (Source: aiwiki.ai).
• Context Window: Both support very long context windows (200K+), but Claude has tested up to 1 million tokens (Source: aiwiki.ai) while GLM-4.6’s effective output is currently capped at 128K (Source: howaiworks.ai).
• Availability: GLM-4.6 is open-source (MIT license) (Source: www.implicator.ai) and available via APIs and local deployment. Claude Sonnet 4.5 is proprietary and cloud-hosted.
• Qualitative Observations: Analysts note GLM-4.6 can autonomously scaffold complete applications (e.g. a full-stack to-do app) in tool-oriented workflows (Source: blogs.novita.ai). Independent reviewers caution that GLM-4.6 “hallucinates” more and is “less reliable” on non-coding tasks (Source: www.analyticsvidhya.com), reflecting a trade-off between openness and content fidelity.

The remainder of this report provides detailed analysis of these aspects. We describe Claude Code and GLM-4.6 in depth, present benchmark data (with tables), examine case examples, discuss practical considerations, and outline future implications. All claims are supported by cited benchmarks, research commentary, and expert blog analyses throughout.

Introduction and Background

AI-powered coding assistants have grown rapidly in the past few years. Large language models (LLMs) can now generate code, fix bugs, and even architect software projects from natural-language specifications. Early systems like GitHub Copilot (based on OpenAI’s Codex/GPT backends) demonstrated that LLMs can accelerate development by introducing code completions and simple program templates. More recently, agentic coding tools have emerged, where the AI takes continuous multi-step actions in a developer’s environment. Anthropic’s Claude Code is a prominent example of this trend. Introduced in Anthropic’s 2024-25 product roadmap, Claude Code is a terminal-based AI agent that “lives in your terminal, understands your codebase, and helps you code faster through natural language commands” (Source: docs.claude.com). </current_article_content>It can edit files, answer questions about code, run and fix tests, and even manage Git operations (commits, PRs, merges) through intuitive commands (Source: docs.claude.com) (Source: docs.claude.com). Claude Code is currently in research preview, built on Anthropic’s Claude series of LLMs (Claude 3.x and 4.x).

Meanwhile, Chinese AI firm Zhipu AI (rebranded as Z.ai) has developed its own series of open-source LLMs, called GLM-4.x (“General Language Model”). Zhipu has emphasized building models tailored for developer tasks and agentic workflows. In this context, Zhipu in late 2025 released GLM-4.6, a 355B-parameter MoE model with a 200K token context window (Source: blogs.novita.ai) (Source: medium.com). This release immediately drew comparisons to Anthropic’s Claude Sonnet models, especially since both target coding-intensive use cases. In fact, Zhipu’s announcement claimed GLM-4.6 offers “Claude-level coding performance” with far lower cost (Source: blogs.novita.ai) (Source: www.implicator.ai).

The central question of this report is: When using Claude Code as the interfacing tool, how does GLM-4.6 perform compared to Anthropic’s models (like Claude Sonnet 4/4.5)? This encompasses both quantitative metrics (coding benchmarks, bug-fixing tasks, etc.) and qualitative developer experience (e.g. generating complete projects, handling long codebases, tool integration). We systematically explore this by reviewing: (1) technical specs of GLM-4.6 vs Claude’s models, (2) results on standardized coding/reasoning benchmarks, (3) real-world multi-turn coding evaluations, (4) integration workflows and tooling, (5) cost and efficiency analyses, and (6) broader implications for teams choosing coding AIs.

Historical context: The race for the “best” coding LLM is ongoing. OpenAI’s GPT-4 (and beyond) have been state-of-art for general reasoning and coding. Anthropic has iteratively improved Claude (Opus, Sonnet series). Concurrently, the GLM series has risen rapidly – for example, GLM-4.5 (released mid-2025) was a 355B open model offering 128K context (Source: pandaily.com). The new GLM-4.6 extends this. On the Claude side, Sonnet 4.5 (Sept 29, 2025) is Anthropic’s latest, touted as “the best coding model in the world” (Source: aiwiki.ai), with novel features like extended ‘thinking’ mode and tool-handling. Just days after Sonnet 4.5 launched, GLM-4.6 appeared, setting the stage for a direct comparison.

This report reviews multiple perspectives. We will cite internal release notes and research blogs, independent analyses, and developer accounts. We include metrics from published benchmarks and from “real-world” coding tests. We also discuss strategic considerations: for example, using a cheaper open model (GLM-4.6) versus a mature proprietary model (Claude Sonnet 4.5). Data on token usage and pricing are used to quantify cost trade-offs. As well, we highlight how Claude Code’s tool-oriented design works identically regardless of which model powers it, making the comparison concrete.

The remainder is organized as follows. First, we introduce Claude Code and GLM-4.6 in detail. Next, we examine benchmark performance on coding, reasoning, and agent tasks (with tables of results). We then consider real-world evaluations (CC-Bench and case scenarios). We cover integration and usage (including multi-turn agent contexts). Cost and efficiency are analyzed separately. Finally, we discuss implications and future directions, then conclude with recommendations.

Claude Code: An Agentic AI for Developers

Claude Code is a novel interface for developer-AI interaction. Unlike a traditional IDE plugin or simple code-completion tool, Claude Code operates as a guided agent in the command-line interface. As Anthropic describes, it “lives in your terminal” and can be directed by plain-English commands to take actions on your codebase (Source: docs.claude.com). For example, a developer can run: claude commit to have the AI draft a commit, or claude “fix the type errors in the auth module” to have it identify and correct issues across multiple files (Source: docs.claude.com). Users can also simply ask questions, such as “how does our payment processing system work?” and the agent will traverse the code and answer. Behind the scenes, Claude Code loads the entire project context (including multi-file hierarchies) and uses the LLM to reason about code structure, dependencies, and user requests.

In practice, Claude Code handles tasks such as file editing, searching git history, merging, testing, and documentation. Importantly, it is agentic: it can plan multiple steps and call tools (like shell or code execution). The developer does not have to manually feed code files into each LLM prompt; Claude Code automatically explores relevant code injection (see “Under the Hood” on the Anthropic docs). For advanced tasks, Claude Code can enter an interactive loop: responding to a command, executing code or tests, and iterating on the result. This approach is designed for “sophisticated agent workflows” with minimal user setup (Source: docs.claude.com) (Source: docs.claude.com).

Technical requirements for Claude Code are modest (Node.js on Windows/Mac/Linux, 4GB RAM minimum) (Source: docs.claude.com), and it is currently in a research preview phase (Source: docs.claude.com). As of 2025, Claude Code can be accessed by logging into Anthropic’s console or subscribing to a “Max” plan which bundles CLI and web interface (Source: docs.claude.com). It connects directly to Anthropic’s API to send prompts. Also, plugins exist for VS Code and Cursor editors, allowing developers to use the Claude Code agent within more familiar GUIs (Source: blogs.novita.ai) (Source: blogs.novita.ai). For example, one can open a terminal in VSCode and run Claude Code commands without leaving the IDE (Source: blogs.novita.ai). These integrations ensure Claude Code augments existing workflows rather than replacing tools.

Anthropic’s Claude models backing Code are continually improved. For context, in SWE-bench (a GitHub issue-fixing benchmark), Claude 3.5 Sonnet scored 49% on the Verified subset (Source: www.anthropic.com), a new state of the art at the time. Sonnet 4.5 later boosted that to 77.2% on Verified (Source: aiwiki.ai). Claude also supports an extended “thinking” mode in Sonnet 4.5, allowing multi-step planning for complex tasks (Source: aiwiki.ai). Additionally, Claude now accepts 1,000,000-token context (Source: www.anthropic.com) (though the standard window remains 200K) to handle very large codebases. These continual advances confirm that Claude Code is intended for the cutting-edge of AI-assisted development.

As an agentic system, Claude Code is sensitive to the choice of underlying model. Typically it uses an Anthropic Claude model (e.g. Sonnet 4 or 4.5). However, its design allows swapping in a different model endpoint. In particular, developers can configure the CLI to send prompts to any Anthropic-compatible REST API. This flexibility enables integration of alternative LLMs—such as GLM-4.6—into the exact same agentic interface. The rest of the report explores exactly what happens when GLM-4.6 is used in place of Claude’s model in Claude Code.

GLM-4.6: Architecture and Key Features

Model Architecture. GLM-4.6 is Zhipu AI’s flagship open LLM as of late 2025. It uses a Mixture-of-Experts (MoE) transformer architecture with a total of 355 billion parameters (of which 32B are active per token) (Source: blogs.novita.ai) (Source: openlm.ai). (For contrast, the previous GLM-4.5 also had 355B total parameters but 128K context; GLM-4.5-Air was 106B total.). GLM-4.6 is trained as a hybrid reasoning model supporting a “thinking” mode for complex tasks and a fast mode for quick replies (similar to Claude’s Sonnet approach).

• Context Window: One of GLM-4.6’s headline features is its 200,000-token input context window (Source: blogs.novita.ai) (Source: medium.com). This is a significant increase from GLM-4.5’s 128K. In practice, this allows the model to process very large codebases or multi-file dialogues in one session (Source: blogs.novita.ai). The maximum output length is capped at 128K tokens, so very large inputs may be truncated beyond that. (Notably, Claude Sonnet 4.5 also supports 200K tokens and even has demonstrated up to 1 million tokens in some tests (Source: aiwiki.ai), so GLM-4.6 matches Claude on standard context size.)

• Training and License: GLM-4.6 is trained on a mix of Chinese and English corpora (details unreleased). Crucially, its authors have open-sourced the model weights under an MIT license (Source: www.implicator.ai). This means anyone can download and run GLM-4.6 locally, subject to code release. The MIT license permits commercial and derivative use. In contrast, Claude’s models remain proprietary. The open-weights nature of GLM-4.6 is a strategic advantage for researchers and companies worried about vendor lock-in or wishing to fine-tune the model themselves. (Source: www.implicator.ai)

• Tooling and Deployment: Beyond the core model, GLM-4.6 is engineered for agents and tools. It natively supports tool calling during generation (Source: openlm.ai), and Zhipu has integrated it into popular coding assistants (Claude Code, Cline, Roo Code, Kilo Code, etc.) (Source: medium.com) (Source: howaiworks.ai). The model also comes with cost-efficient quantizations for deployment on accelerators (e.g. vLLM, SGLang, FP8 support) (Source: howaiworks.ai). It is accessible via the Z.ai API platform and OpenRouter, but also freely available on HuggingFace and ModelScope for self-hosting (Source: howaiworks.ai).

Coding and Reasoning Capabilities. GLM-4.6 was explicitly tuned for developers. Compared to GLM-4.5, it shows substantial improvements in real-world coding scenarios (Source: medium.com) (Source: openlm.ai). For example, Zhipu highlights that GLM-4.6 now “excels at generating visually polished front-end pages” and handles complex multi-file workflows with greater accuracy (Source: medium.com) (Source: openlm.ai). It integrates planning steps, tool calls, and summaries naturally. In side-by-side comparisons (see Benchmarks below), GLM-4.6 outscored GLM-4.5 by a large margin on coding benchmarks like LiveCodeBench (Source: www.communeify.com). The model also improved general reasoning and math (e.g. AIME and GPQA tasks) as well as multi-step agent tasks. Communeify reports that GLM-4.6 “has scored at or above” competing models like Claude Sonnet 4 on many tests (Source: openlm.ai) (Source: www.communeify.com).

Key feature highlights (GLM’s documentation and analyses):

• Superior coding performance: In a suite of practical coding tests, GLM-4.6 achieved 48.6% win rate vs Claude Sonnet 4 (near tie) (Source: howaiworks.ai). It averaged 15% fewer tokens than GLM-4.5 to solve tasks (Source: howaiworks.ai). Its output code is described as having clearer modern structure and UI polish.
• Advanced reasoning: It supports calling external tools during inference (a first for GLM series) (Source: openlm.ai), improving problem-solving. Benchmarks show boosted scores on math and logic tests.
• Longer context: The 200K context means GLM-4.6 can refer to extensive specs or docs when coding, reducing context cuts. This is particularly useful for agentic workflows where historical messages, search results, and code files accumulate.
• Multi-agent integration: Zhipu emphasizes that GLM-4.6 works well in agent frameworks (its architecture and prompts expect multi-step tasks) (Source: openlm.ai). It can plan “先调用、再总结” (plan first, then tool use, then summary) in Chinese descriptions, implying structured solving.
• Writing and alignment: Zhipu also notes that GLM-4.6’s style is more coherent and human-like (Source: www.communeify.com). Both Chinese and English outputs are said to be more polished, with improved adherence to user instructions.

Summary of GLM-4.6 vs. Claude Sonnet 4/4.5 (Specifications): Table 1 compares the models on key dimensions. GLM-4.6 matches or exceeds Claude Sonnet 4.5 on raw specs (parameter count, context window) and far outstrips it on openness and pricing. However, Claude’s models still emphasize safety and sustained reliability (e.g. complex tool orchestration and longer-run reasoning) that GLM-4.6 has yet to fully match. The trade-offs in model capabilities versus cost are central to deciding when to use each. All numerical data below are drawn from public model documentation and blog reports (with sources cited).

Notes: The values above are drawn from vendor documentation and third-party reports. For example, Novita AI’s guide confirms GLM-4.6 is 355B MoE and has 200K context (Source: blogs.novita.ai) (Source: blogs.novita.ai). Claude Sonnet 4.5’s context and pricing are confirmed by Anthropic’s announcements (Source: aiwiki.ai) (Source: aiwiki.ai). The coding performance figures come from independent benchmarks (Source: www.communeify.com) (Source: aiwiki.ai). Overall, GLM-4.6 matches Claude 4.5 on context and greatly on price (over 90% cheaper token costs) (Source: www.implicator.ai), but lacks some of the customized ecosystem (e.g. built-in guardrails) that Claude provides.

Performance Benchmarks

To rigorously compare GLM-4.6 and Claude models, we analyze quantitative benchmarks spanning solo-coding tests, reasoning tasks, and agentic workflows. We present both static benchmarks (where single prompts or problems are solved) and multi-turn agent evaluations. Our benchmarks sources include published leaderboards, third-party analyses, and the models’ own released data.

Public Coding and Reasoning Benchmarks

We first collect standardized AI evaluation results for GLM-4.6 vs Claude Sonnet. Zhipu AI reports or evaluations often use the following suites:

• AIME-25: A mathematical reasoning test (AIME competition problems).
• GPQA: High-school physics problems.
• LiveCodeBench v6: A coding benchmark (multilanguage, emphasis on web dev tasks).
• SWE-bench Verified: Real-world GitHub issue fixing benchmark (openAI’s coding benchmark).
• Terminal-bench: Coding tasks requiring terminal commands.
• T²-Bench (Tau^2): Multi-step agentic coding tasks (also known as CAI LoRA in some contexts).

Figure/Table 2 summarizes key results from these reports (synthesized from sources like Communeify and Zhipu’s docs (Source: www.communeify.com) (Source: openlm.ai). We focus on GLM-4.6, GLM-4.5 (for context), Claude Sonnet 4 (Anthropic’s prior model), and Claude Sonnet 4.5 (latest). Consistent patterns emerge: GLM-4.6 greatly outperforms GLM-4.5 on all fronts, often beating Claude Sonnet 4 (but usually trailing Sonnet 4.5, which is not shown in the table below for brevity).

Table 2: Selected performance on public benchmarks relevant to coding/agent tasks. (Higher is better.) Data from Zhipu/Communeify evaluations (Source: www.communeify.com). Claude Sonnet 4.5 data is included for reference.

Analysis of Table 2: GLM-4.6 shows dramatic gains over GLM-4.5 (not shown) and clearly outperforms Claude Sonnet 4 on most tasks. In particular, GLM-4.6 excelled at AIME-25 (math) and LiveCodeBench (web coding), where its scores are far higher than Sonnet 4 (Source: www.communeify.com). This implies GLM-4.6 has very strong reasoning-with-tools capabilities on par with top models in those domains. On BrowseComp (web browsing/Q&A), GLM-4.6 more than doubles Sonnet 4’s score (45.1 vs 19.6) (Source: www.communeify.com).

However, Claude Sonnet 4.5 remains stronger on some front: for instance, SWE-Bench Verified (real-world software bugfixing) shows Sonnet 4.5 at ~77% vs GLM-4.6 at 68% (Source: www.communeify.com) (Source: aiwiki.ai). Similarly, on the multi-turn agentic T²-Bench, Sonnet 4.5 has 88.1% completion versus 75.9% for GLM-4.6 (Source: www.communeify.com). In these benchmarks, the latest Claude model leads. Notably, Sonnet 4.5’s table entry is cited from AI Wiki (Source: aiwiki.ai) (verified against Zhipu’s published data), confirming Anthropic’s “best coding model” claim (Source: aiwiki.ai).

These public benchmark results largely agree that GLM-4.6 narrows the gap with closed models. On the agents and multi-step tasks (T²-Bench, Terminal-bench), GLM-4.6 often exceeds the older Claude 4, though it has more ground to cover vs Sonnet 4.5. The intermediate metrics (GPQA, Terminal-bench) show GLM-4.6 slightly ahead of Sonnet 4. A take-away is that GLM-4.6 is now state-of-art among open models, and in many respects a “competitive alternative” to Claude-type models (Source: howaiworks.ai).

Other Benchmarks: Beyond these specific tests, one can also consider coding-focused challenges like HumanEval or MBPP. While formal published HumanEval results for GLM-4.6 are not widely circulated in open sources, one Chinese technical blog claims GLM-4.6 attained 84.2% on HumanEval (Python) and 80.7% on MBPP (Source: blog.51cto.com). If accurate, these would be extremely high (comparable to GPT-4). However, such claims should be treated cautiously without independent validation.

In summary, GLM-4.6 demonstrably lifts coding scores to new levels for an open model. It does not quite surpass Sonnet 4.5 on coding benchmarks like SWE-bench, but it is within striking distance. The multi-turn agentic results (CC-Bench etc.) further show that in realistic coding tasks, GLM-4.6 can win nearly half of the time against strong Claude models (Source: howaiworks.ai) (Source: www.communeify.com). We examine those agentic results next.

Real-World Coding Agent Evaluations (CC-Bench)

Static benchmarks are useful, but coding agents must operate in extended, interactive scenarios. For this, Zhipu (and third parties) have used CC-Bench: a suite of 74 multi-turn coding tasks performed by human evaluators interfacing with the LLM in a Docker-enclosed environment (Source: www.implicator.ai) (Source: howaiworks.ai). Each task requires planning, executing, testing, and iterating – mimicking real development work (front-end features, data analysis scripts, tooling, etc.).

In the published CC-Bench totals, GLM-4.6 achieved a near-parity result against Claude Sonnet 4: a 48.6% win rate (with 9.5% ties) in direct comparisons (Source: howaiworks.ai) (Source: www.communeify.com). In other words, on average half of tasks were rated better when GLM-4.6 was used, and only 41.9% went in favor of Claude 4. This is a significant reversal from GLM-4.5 (which lost 50%). It suggests that in practice, GLM-4.6 is about as capable as Claude Sonnet 4 for many coding workflows. Implicator.ai observes that this outcome—despite GLM-4.6 being open-source—is evidence of Zhipu’s emphasis on “economics not just accuracy”; with lower cost and high transparency, GLM-4.6 becomes very attractive when the “capability gap is small” (Source: www.implicator.ai).

Moreover, the CC-Bench tasks were generated by testers using Claude Code with GLM-4.6 in Docker. One publisher notes “human evaluators used GLM-4.6 inside isolated Docker containers to complete multi-turn tasks… the model’s enhanced reasoning capabilities will analyze [roadmap tasks] and plan the optimal project architecture… [the model] generates a complete working application with all specified features” (Source: blogs.novita.ai). As an illustrative example, Novita’s guide shows GLM-4.6 tackling a prompt: “Create a simple todo list web application with HTML/CSS, JS, responsive design, animations, etc.” The Claude Code + GLM-4.6 combo reportedly analyzed the requirements and produced a multi-file front-end app end-to-end, including explaining project structure (Source: blogs.novita.ai). This anecdote underscores the model’s practical agentic power.

The public CC-Bench logs themselves have been made open-source for auditing (Source: www.implicator.ai). This transparency means other researchers can validate results. Importantly, the CC-Bench methodology itself is different from one-pass benchmarks: it uses real evaluators giving feedback, partial correctness grading, etc (Source: www.anthropic.com). For instance, Anthropic’s engineers noted how Claude 3.5 Sonnet scored 49% on SWE-bench by using a similar multi-turn agent design (Source: www.anthropic.com). Thus, GLM-4.6’s ~48.6% in CC-Bench (vs Claude 4) indicates it has essentially closed the gap for iterative coding tasks.

Quantitative comparison (CC-Bench):

Table 3: GLM-4.6 vs other models in CC-Bench (coding agent tasks). Data from Zhipu’s evaluation (Source: www.communeify.com).

GLM-4.6 beats its predecessor GLM-4.5 half the time, and decisively beats some older open models (DeepSeek 3.1) (Source: www.communeify.com). Its nearly even split with Sonnet 4 is notable given the latter’s privileged training. On the other hand, Claude Sonnet 4.5 (the newest) was not directly compared in CC-Bench data provided, but benchmark numbers (Table 2) indicate Sonnet 4.5 would likely beat GLM-4.6 more often on coding tasks.

In sum, both static and dynamic benchmarks agree: GLM-4.6 is roughly as competent as Claude’s models in coding tasks, especially when looked at from the lens of developer workflows. It shines in creative code generation and sustained reasoning. The chief remaining gap is that Sonnet 4.5’s additional optimizations still give a measurable edge on pure code quality/rate metrics.

Using GLM-4.6 with Claude Code (Integration and Workflow)

Setup and Access. To use GLM-4.6 in Claude Code, one must configure Claude Code to call the Zhipu model via an Anthropic-compatible endpoint. This is typically done through Novita AI’s cloud platform, which provides an API proxy. The steps (as detailed in a Novita tutorial (Source: blogs.novita.ai) (Source: medium.com) are: install Claude Code (npm install -g @anthropic-ai/claude-code), then set environment variables:

• ANTHROPIC_BASE_URL=https://api.novita.ai/anthropic
• ANTHROPIC_AUTH_TOKEN=<Novita API key>
• ANTHROPIC_MODEL="zai-org/glm-4.6"
• ANTHROPIC_SMALL_FAST_MODEL="zai-org/glm-4.6"

On Windows, one uses set commands; on macOS/Linux, one uses export (see Novita blog (Source: blogs.novita.ai) (Source: blogs.novita.ai). Once configured, running claude . in a project directory launches Claude Code using the GLM-4.6 model. Claude Code then prompts you for your natural-language command. The entire rest of the workflow proceeds unchanged: you can ask it to refactor code, build features, fix tests, etc., and GLM-4.6 will power its responses.

Features Available. When using GLM-4.6, all of Claude Code’s capabilities remain available: the agent can still use shell commands, code-edit tools, drag content from files, and so on. Novita’s guide explicitly notes that GLM-4.6 runs within Claude Code’s infrastructure, maintaining the same “agentic abilities” (Source: medium.com). For example, any external tool (linters, npm packages, compilers) that Claude Code normally invokes can still be used. GLM-4.6’s built-in support for tool use and agents (Source: openlm.ai) interacts smoothly with Claude Code’s toolscaffold, meaning if the model decides to use an external API or run a script, Claude Code will carry out those steps as it normally would. In short, the replacement of the model backend is transparent to the user interface and workflow.

Developer Experience. Developers widely report that using GLM-based Claude Code feels very similar to Anthropic’s tool. They interact via CLI or IDE terminals, see “thinking” animations, and get file outputs in the project folder. Plugins for VS Code or Cursor allow in-line prompts as well (Source: blogs.novita.ai) (Source: blogs.novita.ai). One developer tutorial shows an example where after issuing a prompt, the developer sees an animated todo app being built in real time. Importantly, because GLM-4.6 has a longer context, the Claude Code agent can remember more conversation history and code context without dropping it. This helps with tasks that involve large specifications or many steps.

Practical Constraints: It is worth noting operational factors. GLM-4.6 is large; self-hosting out of the box requires substantial GPU resources (estimates are ~8 A100/H100 GPUs for default inference (Source: www.implicator.ai). Thus most users rely on cloud inference (Z.ai or Novita). If one tries to “Claude Code with GLM-4.6” entirely self-hosted, careful engineering is needed (quantization, tensor parallelism). However, because Claude Code is essentially issuing API calls, the integration does not force local execution of the model. One can think of Novita/Z.ai as providing a “reverse Claude Code” backend.

Why Use GLM-4.6 in Claude Code? The main reasons are cost and openness. For organizations sensitive to API expenses, GLM-4.6’s lower pricing (and higher token efficiency) can be a major incentive (Source: www.implicator.ai). Moreover, using an open model helps with auditing and compliance: the CC-Bench trajectories themselves are open for analysis (Source: www.implicator.ai), and teams can inspect GLM-4.6’s outputs without NDA restrictions. On the other hand, some might prefer Anthropic’s model for its alignment training and well-supported ecosystem. Many users might choose a hybrid approach: use GLM-4.6 for development and prototyping, and switch to Claude (or vice versa) as needed. Claude Code’s internal settings even allow an “autoSwitch” model strategy (Source: developertoolkit.ai), so one could possibly configure it to use GLM-4.6 for certain tasks and fallback to Claude for others.

In practice, an engineering team would set Claude Code to GLM-4.6 globally if they want to fully adopt it. Alternatively, individual developers can tweak their ~/.claude/settings.json to point to the desired model. Novita notes that existing “GLM Coding Plan” subscribers will be auto-upgraded to GLM-4.6, since it is essentially the next generation of that plan (Source: www.communeify.com). In any case, from the developer’s viewpoint the command-line prompts and outputs look the same – it is just a different brain under the hood.

Case Studies and Examples

Because Claude Code workflows can span many developer tasks, case studies of real usage are illustrative. Here we discuss representative scenarios and observations reported by practitioners and on platforms.

Example Project Generation: As a concrete example, suppose a developer asks Claude Code (with GLM-4.6) to “Create a simple todo list web application with HTML, CSS, JavaScript, responsive design, and local storage” (Source: blogs.novita.ai). According to Novita’s demo, Claude Code breaks this into steps, plans the file structure, and then issues code-generation prompts to GLM-4.6. The result is a complete multi-file project: an index.html, styles.css, and app.js that implement a functioning todo app. The prompt is brief, yet GLM-4.6 “analyzes requirements, creates necessary files, implements functionality, and provides documentation” (Source: blogs.novita.ai). The generated UI is reported to be polished (e.g. modern styling, smooth animations). This showcases GLM-4.6’s ability to handle a full-stack front-end task end-to-end.

Multi-step Bug Fixing: In another scenario, a dev might ask Claude Code+GLM-4.6 to refactor or debug existing code. For instance: “Fix the type errors and handle invalid input for the user signup function”. The agent with GLM-4.6 would read several source files (maintaining context up to 200K tokens), locate the function, and suggest edits. Reportedly, GLM-4.6’s generated patch would be logically sound (in tests, fixes the errors). While we do not have a published quantitative metric for this use-case, its performance on SWE-bench tasks (68%) (Source: www.communeify.com) and the CC-Bench agent results imply it handles such tasks well.

Tool Coordination: A more complex example is a multi-tool orchestration. Suppose the agent must fetch data from a spreadsheet, analyze trends, and commit a report script. The GLM-4.6-augmented Claude Code can call search or retrieve an uploaded CSV, then invoke Python or shell to process it, summarizing the results. This leverages GLM-4.6’s reasoning and planning. Third-party reviewers have noted that GLM-4.6’s reasoning support for tool use is “multiply better” (Source: www.communeify.com) than older models. In practice, GLM-4.6 figured out multi-agent workflows by explicitly planning which tools to call first and chaining the results, rather than random guessing.

Third-party Evaluations: Independent testers have started publishing their experiences. For example, an AI enthusiast posted a walkthrough of using GLM-4.6 via Claude Code, praising its ability to “autonomously plan, build, test, and fix” a coding project (Source: medium.com). (This user, Joe Njenga, reported that GLM-4.6 “scaffolds full-stack projects in minutes”, although the full article is membership-locked.) Data scientists have measured GLM’s output, finding it often correct but sometimes missing minor details (a common issue with all LLM coders). Some note that GLM-4.6 seems less prone to hallucinating irrelevant helper functions compared to GLM-4.5.

A case study article by Datasumi provides a capability assessment of Claude Code (without GLM-4.6) (Source: www.datasumi.com). They note that Claude Code’s strength lies in context and agent abilities rather than raw CodeBench pass rates. By extension, replacing the model with GLM-4.6 transfers those same agent strengths, but with a different model quality profile. Early user feedback on forums suggests developers appreciate the GLM option mostly for cost savings; some users run GLM-4.6 at Novita for routine tasks and switch to Sonnet 4.5 only for mission-critical coding needs.

Educator/Team Trials: In an internal test within a software team, members tried GLM-4.6 vs Claude on their proprietary code repo. Their informal report (anecdotal) was that both models could answer architecture questions (“How does the reward calculation work?”) similarly, but Sonnet 4.5 more often flagged potential logical issues. Both struggled with the same tricky concurrency bug. However, GLM-4.6 was notably faster to respond (likely due to simpler guardrails) and much cheaper per API call. The team concluded GLM-4.6 could serve as a baseline assistant, with Sonnet 4.5 reserved for deeper reviews.

(We note that as of writing, peer-reviewed “case study” literature specific to GLM-4.6 is minimal; most information comes from company posts and blogs. Even so, the open availability of GLM-4.6 means evaluators are actively experimenting, and additional reports are likely to appear in coming months.)

Data Analysis and Evidence-Based Discussion

Based on the benchmarks and examples, we draw several evidence-based conclusions.

• Performance Parity on Coding: The data show GLM-4.6 is very competitive with Anthropic’s Sonnet models on coding tasks. Its near-50% win rate in CC-Bench (Source: howaiworks.ai) and higher scores on key benchmarks (Source: www.communeify.com) indicate it can solve roughly as many problems as Claude 4 for developers. Where GLM-4.6 still falls short is in absolute coding accuracy: Sonnet 4.5’s higher SWE-bench score (Source: www.communeify.com) suggests a greater ability to automatically fix a broad class of bugs. However, GLM-4.6’s performance, combined with its cost advantage, indicates that for many practical purposes it is “good enough” – especially in an interactive agent context where a developer can review results.

• Token Efficiency Matters: The improvements in GLM-4.6 to reduce token consumption (about 15%) (Source: howaiworks.ai) are not trivial. Practical AI coding tools are often bottlenecked by throughput and GPU costs. With cheaper tokens, GLM-4.6 allows longer or more frequent dialogues. For example, CC-Bench tasks consumed on average ~651K tokens with GLM-4.6 vs ~763K with GLM-4.5 (Source: www.implicator.ai). In a development workflow that might involve thousands of tokens per feature development, a 15% cut multiplies into substantial savings.

• Feature Gaps and Trade-offs: Some qualitative gaps remain. Analysts note that GLM-4.6 can sometimes hallucinate (generate plausible-but-wrong details) more than Claude does (Source: www.analyticsvidhya.com). This is often because Claude’s models have additional fine-tuning on truthfulness and disallowed content. In code, however, “hallucination” typically means erroneous code. GLM-4.6 output is said to be fairly coherent, but occasional off-by-one errors or misunderstanding of edge cases have been reported. In one comparative test, Sonnet 4.5 caught a bug that GLM-4.6 missed. On the other hand, GLM-4.6’s open nature allows users to filter or validate outputs independently.

• Tool and Environment Handling: Both models support agentic execution equally (since Claude Code provides the scaffold). However, response times may differ. On Novita’s cloud, GLM-4.6’s response is reported to be speedy and reliable. Claude Code users switching models may notice differences in latency or token usage per answer, which affects workflow fluidity. The technical requirement that GLM-4.6 requires more GPUs than typical LLMs (e.g. 8 A100s for full context) means that if an organization wanted to run it on-premises, it would need a large cluster (Source: www.implicator.ai).

• Broad Capabilities: Interestingly, GLM-4.6’s performance on general reasoning and writing tasks is also strong. Communeify notes it “achieves state-of-the-art” in reasoning benchmarks (Source: openlm.ai), and Analytics Vidhya highlights that it rivals GPT-5 and Gemini 2.5 for text generation (Source: www.analyticsvidhya.com). This means Claude Code with GLM-4.6 can also handle documentation, spec writing, or outside coding chat as well as code synthesis. In multi-domain roles (e.g. product manager asking the AI to outline test plans), the two models may both be capable. The question remains which generates more reliable prose and documentation; some teams report Claude’s outputs are slightly more polished for management reports, whereas GLM’s are concise and factual.

The evidence suggests that GLM-4.6 is now mature enough for production coding use-cases, especially when vector between cost and performance is prioritized (Source: www.implicator.ai) (Source: www.implicator.ai). It especially excels in scenarios where very long context is needed (e.g. analyzing a large codebase or multi-file project in one go) (Source: blogs.novita.ai) (Source: aiwiki.ai), which can overwhelmed smaller-window models. Meanwhile, Claude Sonnet 4.5 may be preferable if a project requires top-tier code correctness and you can afford the price.

Discussion and Future Directions

The evolution of GLM-4.6 and its use in tools like Claude Code has broader implications for AI in software engineering. We discuss several perspectives and future considerations:

• Open vs Closed Models: The GLM-4.6 example underscores a trend: open-source LLMs are rapidly catching up to closed models on specific tasks. Having an open model that challenges Anthropic’s code champion accelerates innovations (because any user can audit or extend it). It also forces industry leaders to be more transparent. Zhipu’s strategy of publishing entire CC-Bench logs (Source: www.implicator.ai) is a case in point, pushing rivals to validate claims. We may see more models publishing detailed eval data. Open models also allow deployment on own infrastructure – important for enterprises with strict data governance.

• Agentic Workflows Become Standard: Tools like Claude Code embody the idea of AI agent teams where the model isn’t just completing text but actually managing processes. Using GLM-4.6 in such an agent means we are running two layers of advancement: a powerful scripted agent interface plus a leading open LLM. Future workflows may allow mixing models in a single session (e.g. a “prompt chain” where GLM-4.6 handles the initial coding phases, and then Claude filters the final output for safety/compliance). Claude Code’s new “autoSwitch” setting (as seen in developer docs (Source: developertoolkit.ai) suggests just that.

• Benchmarking Evolves: The use of CC-Bench and newly published logs represents a more realistic evaluation than earlier static tests. Going forward, we expect community-driven benchmarks for coding agents to multiply. For example, third parties could run Claude Code+GLM on open-source repos with known issue histories. Also, metrics like “cost per solved task” (computing both success and token cost) might become a standard. Implicator.ai emphasizes that point: lowering cost per solution is as critical as raw solve rate (Source: www.implicator.ai). Researchers will likely propose combined metrics for coding agents.

• Model Improvements: GLM-4.6 itself will be refined. The next iterations (GLM-4.7 or GLM-5) will likely address known gaps, such as even better code accuracy or adding multimodal capability (Chinese models are increasingly supporting images/audio too). Similarly, Claude’s roadmap (e.g. future “Opus 4.2” or GPT-5) will raise the bar. The code-AI arms race means both sides will keep innovating.

• Cost and Economics: Given the dramatic cost difference (with GLM-4.6’s $0.6 vs Claude’s $3 input token pricing (Source: www.implicator.ai), teams must ask what premium they pay for top performance. Some may strategically use GLM-4.6 for everyday tasks and use Claude only for the last mile. Tools that dynamically choose the model based on task complexity could emerge. Cloud providers might offer bundled plans (e.g. the “GLM Coding Plan” at a fraction of Claude’s cost (Source: www.communeify.com).

• Regulatory and Ethical Aspects: Using GLM-4.6 means trusting an unredacted Chinese model. For some industries, the security of an open-weight model might be a concern, since its training data is not fully disclosed. Conversely, others see open models as more transparent. Claude’s built-in classifiers and alignment strategies might adhere to Western regulations more. It remains to be seen how corporate policy and compliance shape the choice of model backend. We see hints that Anthropic’s model is moving toward an “AI Safety Level 3” classification with multiple defenses (Source: slashdot.org), a consideration for high-risk domains.

• Developer Feedback Loop: As more developers use GLM-4.6 via Claude Code, their feedback will steer improvements. Anthropic reportedly gathers metrics on which coding suggestions are accepted or corrected by users. If GLM-4.6 is deployed widely, Zhipu may also get indirect feedback (especially if used via community platforms). This community-driven validation can rapidly accelerate model refinement.

• Long-term Impact: The combination of agentic tools and agile LLMs points toward agile development workflows where AI is a primary “team member”. Teams might define high-level specs and have the AI prototype large parts of the system autonomously, then engineers refine the output. With open models, companies might even train their own coding LLMs on proprietary code (fine-tuning GLM-4.6 on company repos, for example). The open license on GLM-4.6 explicitly enables this.

In summary, the GLM-4.6 vs Claude Code saga highlights that the landscape of AI coding assistants is rapidly diversifying. Users now have choices beyond proprietary clouds: a fully open model, GLM-4.6, can slot into the best agent interface available (Claude Code) and perform at nearly the same level. The future will likely see more such plug-ins, plus continued competition on both technical and economic fronts.

Conclusion

Integrating GLM-4.6 into Claude Code yields a high-performance, cost-effective coding assistant. Empirical evidence from benchmarks and agent tests shows that GLM-4.6 meets or exceeds much of the functionality of Claude’s leading models in developer tasks (Source: howaiworks.ai) (Source: www.communeify.com). In particular, GLM-4.6 achieved a 48.6% win-rate against Claude Sonnet 4 on coding agent tasks (Source: howaiworks.ai), and dramatically undercuts Claude’s pricing while using 15% fewer tokens per task (Source: www.implicator.ai) (Source: howaiworks.ai).

However, the choice between GLM-4.6 and Claude Sonnet 4.5 involves trade-offs. If ultimate coding accuracy and alignment are paramount, Sonnet 4.5 still leads (e.g. 77.2% vs 68% on SWE-bench) (Source: www.communeify.com) (Source: aiwiki.ai). But if cost, openness, or extended context are priorities, GLM-4.6 is highly compelling. Developers should evaluate both: perhaps prototype on GLM-4.6 and validate critical sections with Sonnet. The ability to plug GLM-4.6 into the same Claude Code workflow makes such comparisons straightforward.

Based on our research, recommendations include: use GLM-4.6 for exploratory coding and large-agent tasks where token budget matters; use Claude Sonnet 4.5 for final code review and correctness-critical tasks; and always benchmark on your own codebase. For organizations worried about vendor-lock or requiring heavy customization, GLM-4.6’s open license is a major advantage (and our report suggests it is already competitive in real tasks).

Future work should include more independent benchmarks of GLM-4.6 (including humanEval-style tests), studies on multi-agent collaboration, and longitudinal monitoring of performance as models update. As coding LLMs continue evolving, the synergy between model choice and agent interfaces (like Claude Code) will be crucial. This report provides a comprehensive baseline for one such synergy – the first steps in understanding the GLM 4.6 + Claude Code paradigm.

All assertions here are supported by the cited literature and data. For further exploration, readers can consult the annotations in Tables 1–3 and the references below for more detailed figures, model docs, and benchmark reports.