What should be two of the most capable AI models launched within days of each other, and developers are already taking sides. We’re talking about the recently released GLM-5 and Claude Opus 4.6. GLM-5 from Zhipu AI arrived as an open-weight challenger with 744 billion parameters, while Claude Opus 4.6 immediately became Anthropic’s most capable model ever. The benchmark numbers are close enough that the choice genuinely matters, and one of these models costs roughly seven times less than the other. (you can guess which one that is) But there’s more to it when comparing GLM 5 vs Opus 4.6, so let’s investigate further.

How do GLM 5 and Opus 4.6 Compare?

Before chiming in on benchmarks, it’s a good idea to understand what each model is designed for. GLM-5 is Zhipu AI’s fifth-generation flagship, built specifically around what the company calls “agentic engineering,” meaning complex, multi-step programming tasks that require planning, tool use, and sustained execution over long sessions. It scaled from the 355B-parameter GLM-4.7 up to 744B total parameters (with 40B active at any time thanks to a Mixture-of-Experts architecture), and expanded its pretraining corpus from 23 trillion to 28.5 trillion tokens, with a notable emphasis on code and reasoning data.

Claude Opus 4.6, released on February 5, 2026, is Anthropic’s answer to that. It builds on Opus 4.5’s already dominant SWE-bench score and layers in a 1 million token context window (in beta), adaptive thinking via an effort parameter, and a new “Agent Teams” feature for orchestrating multiple AI subagents together. Anthropic positioned it as their best model for coding, agents, and computer use, and the benchmarks back that up in most areas.

GLM 5 vs Opus 4.6 Benchmark Comparison

This is where the conversation gets real. The table below pulls from published benchmark data across both models’ official release documentation and independent evaluations.

The pattern here is consistent: Opus 4.6 leads across every directly comparable benchmark, though GLM-5 closes the gap considerably on SWE-bench and τ²-bench. The Terminal-Bench gap (56.2% vs 65.4%) is the most telling for everyday coding workflows, since it measures exactly the kind of autonomous debugging and file management that developers care about in an agentic coding assistant.

Where GLM-5 Makes a Stronger Case

Benchmarks only tell part of the story. GLM-5 has real arguments in its favor, and they’re worth taking seriously depending on your situation.

The open-weight advantage is significant. GLM-5 is available under the MIT license with weights on HuggingFace. That means you can self-host it, fine-tune it on your codebase, wrap it in your own safety layer, or deploy it in regions where Anthropic’s API is inaccessible. Opus 4.6 offers none of that. If your team needs data residency, on-premise deployment, or the ability to deeply customize the model, GLM-5 is the only option here.

Pricing is genuinely different in scale. GLM-5 API access through Z.ai is priced at approximately $1.00 per million input tokens and $3.20 per million output tokens. Opus 4.6 sits at $5.00 per million input tokens and $25.00 per million output tokens at standard rates. For high-volume coding pipelines generating millions of tokens daily, that difference compounds fast.

GLM-5 holds its own where it counts most for coding. A 77.8% SWE-bench score is not a consolation prize. It puts GLM-5 ahead of where Opus 4.5 sat before Anthropic’s November 2025 release, and ahead of Gemini 3 Pro’s 76.2%. On CC-Bench-V2, GLM-5 hits a 98% frontend build success rate and 74.8% end-to-end correctness, suggesting it can handle full-stack project construction more reliably than its raw benchmark position implies.

Key GLM-5 strengths for coding workflows:

• Full-stack code generation covering front-end, back-end, and data pipelines from a single natural language prompt
• 98% frontend build success rate on CC-Bench-V2
• MIT license permitting commercial deployment, fine-tuning, and self-hosting
• API pricing is approximately 7x lower than Opus 4.6 at standard rates
• Support for vLLM, SGLang, and xLLM inference frameworks for local deployment
• BrowseComp score of 75.9, ranking first among all tested models at release (subsequently surpassed by Opus 4.6’s 84.0%)
• Multilingual coding support with a 73.3 SWE-bench Multilingual score

Where Opus 4.6 Pulls Ahead

The gap between the two models is smaller than many expected, but it’s consistent, and it widens in exactly the scenarios where model performance matters most: complex, long-horizon, agentic coding work.

The Terminal-Bench 2.0 improvement from 4.5 to 4.6 was one of Anthropic’s key optimization targets. Moving from 59.8% to 65.4% reflects genuine improvements in the model’s ability to navigate command-line environments, manage file systems, and recover from failures mid-session. GLM-5’s 56.2% is a solid open-source result, but that’s nearly a 10-point gap in the benchmark most predictive of autonomous debugging quality.

The ARC-AGI-2 result deserves mention, too. Opus 4.6 scores 68.8% on that benchmark, which measures novel abstract reasoning outside the model’s training distribution. This matters for coding because writing genuinely new software — as opposed to pattern-matching on familiar structures — requires the kind of fluid intelligence that ARC-AGI-2 is designed to test. Opus 4.5 scored 37.6% on the same benchmark; Opus 4.6’s near-doubling of that figure represents a qualitative shift in reasoning capability.

Key Opus 4.6 strengths for coding workflows:

• 80.8% on SWE-bench Verified, maintaining the highest score among proprietary models
• 65.4% on Terminal-Bench 2.0, the strongest recorded score in Anthropic’s lineup
• 1 million token context window (beta), enabling full large codebase ingestion in a single session
• Adaptive thinking via an effort parameter for tuning reasoning depth per task
• Agent Teams support for coordinating multiple Claude subagents on parallel coding tasks.
• 68.8% on ARC-AGI-2, nearly double Opus 4.5’s 37.6%, indicating stronger generalization
• Leads across 7 of 8 programming languages on SWE-bench Multilingual

GLM 5 vs Opus 4.6 – The Real-World Trade-Off (+Online Reception)

The benchmark gap between these two models is real but not enormous. On SWE-bench, the most widely cited coding benchmark in the industry, GLM-5 trails Opus 4.6 by about 3 percentage points. That margin matters in production, but it’s not the kind of gap that makes one model unusable.

What matters more than the raw numbers is the use case. If you’re building a coding agent at scale, running millions of tokens per day, and need full control over the deployment stack, GLM-5 is a serious option that punches well above its price. If you’re building with Claude Code, working in a team environment, or tackling the kind of complex multi-agent workflows where Opus 4.6’s reasoning depth and Terminal-Bench lead translate directly to fewer failed runs and less human intervention, the premium is harder to argue against.

GLM-5’s arrival also signals something important: the gap between open-source and frontier proprietary models in coding has narrowed to the point where it’s a genuine strategic decision, not a default one.

The Bottom Line

GLM-5 vs Opus 4.6 for coding is genuinely competitive in a way that previous open-source versus proprietary matchups rarely were. Opus 4.6 holds measurable leads on SWE-bench (80.8% vs 77.8%), Terminal-Bench 2.0 (65.4% vs 56.2%), and agentic reasoning benchmarks. For teams doing high-stakes, long-horizon coding work where every percentage point on benchmark resolution rates translates to engineering hours saved, Opus 4.6 is the stronger choice today. But GLM-5 costs roughly 7x less, runs under an MIT license, and scores high enough on the benchmarks that matter to be a legitimate daily driver for most coding workflows. The answer depends less on which model is “better” and more on what your team actually needs to build.