at a glance
| GLM-5 | Claude Sonnet 4.6 | |
|---|---|---|
| provider | Zhipu AI | Anthropic |
| parameters | 744B total / 40B active (MoE) | ~mid-size (est.) |
| context window | 200k tokens | 1m tokens |
benchmarks
what are these models?
GLM-5 is Zhipu AI’s flagship language model — a significant step up from GLM-4 in reasoning, coding, and tool-use capability. It competes at the frontier tier against both open and closed models.
Claude Sonnet 4.6 is Anthropic’s mid-tier model, known for strong software engineering and a 1m token context window. It is closed-source and accessed via Anthropic’s API.
benchmark breakdown
Claude Sonnet 4.6 leads on five of six benchmarks. The updated results flip the previous comparison:
- GPQA Diamond: 89.9% vs 86.0% — 3.9-point lead on graduate science
- SWE-bench Verified: 79.6% vs 77.8% — 1.8-point lead on software engineering
- Terminal Bench 2: 59.1% vs 56.2% — 2.9-point lead on shell tasks
- TAU2-Bench: 91.7% vs 89.7% — 2-point lead on agentic tool use
- HLE without tools: 33.2% vs 30.5% — 2.7-point lead on raw expert knowledge
GLM-5 wins only on HLE with tools. At 50.4% vs 49.0%, it’s a 1.4-point margin — the closest result in the set, and the one area where GLM-5 retains an advantage.
what people are saying
when to use GLM-5
- you need the strongest open-weight model that can be self-hosted or fine-tuned
- tool-augmented expert knowledge tasks are your primary workload (HLE with tools)
- cost efficiency relative to frontier closed models is a key consideration
when to use Claude Sonnet 4.6
- you need strong performance across science, coding, terminal tasks, and agents
- you need a 1m token context window — GLM-5 tops out at 200k
- you want Anthropic’s enterprise support, safety layer, and hosted API reliability
- your team is already integrated into the Anthropic ecosystem
compounding gains with fine-tuning
GLM-5 is already competitive, and its performance on HLE with tools highlights a key strength: the ability to effectively leverage external systems for complex, knowledge-heavy tasks. With targeted fine-tuning on your own data, workflows, and tool-use patterns, teams can close much of the remaining gap — and in some cases surpass baseline performance on real-world tasks.
Where Claude Sonnet 4.6 retains a structural edge is its 1m context window, which is especially valuable for workloads involving very long inputs like full codebases or extensive legal documents. But outside of these extreme context scenarios, a fine-tuned GLM-5 can evolve into a highly specialized, high-performance system tailored to your domain.
frequently asked questions
does claude sonnet 4.6 beat glm-5 across the board?
based on these six benchmarks: yes, on five of six. the only exception is hle with tools (50.4% vs 49.0% — a 1.4-point margin for glm-5).
why use glm-5 if sonnet 4.6 leads on most benchmarks?
open weights (self-hosting, fine-tuning, data privacy), cost at scale, and a slight edge on tool-augmented expert knowledge tasks. for teams that can’t use closed apis, glm-5 is the strongest open-weight alternative.
which is better for software engineering?
claude sonnet 4.6 — 79.6% vs 77.8% on swe-bench verified, a 1.8-point gap.
which is better for agentic workflows?
claude sonnet 4.6 — 91.7% vs 89.7% on tau2-bench, a 2-point gap on multi-step tool use.
what about the context window difference?
claude sonnet 4.6 supports 1m tokens; glm-5 supports 200k tokens. for full-codebase ingestion or very long document processing, this structural difference matters regardless of other benchmark results.