at a glance
| GLM-4.7 Flash | Claude Sonnet 4.6 | |
|---|---|---|
| provider | Zhipu AI | Anthropic |
| parameters | 730B total / 3B active (MoE) | ~mid-size (est.) |
| context window | 128k tokens | 1m tokens |
benchmarks
what are these models?
GLM-4.7 Flash is Zhipu AI’s fast inference model from the GLM-4.7 family. It shows strong performance on terminal automation and mathematical reasoning — standout capabilities relative to its general benchmark profile.
Claude Sonnet 4.6 is Anthropic’s mid-tier model, known for strong software engineering performance and a 1m token context window. It is closed-source and accessed via Anthropic’s API.
benchmark breakdown
Claude Sonnet 4.6 wins on science, coding, and tool use. GPQA Diamond (89.9% vs 75.2%) shows a 14.7-point lead on graduate-level science. SWE-bench Verified (79.6% vs 59.2%) is a 20.4-point gap on software engineering. TAU-bench (91.7% vs 79.5%) favors Sonnet 4.6 by 12.2 points.
GLM-4.7 Flash wins on terminal tasks. Terminal tasks (64.0%* vs 59.1%) show a 4.9-point gap.
what people are saying
when to use GLM-4.7 Flash
- terminal and CLI automation is your core workflow
- you need fast, cost-efficient inference
when to use Claude Sonnet 4.6
- software engineering, code review, or bug fixing are primary use cases
- graduate-level scientific reasoning matters
- you need a 1m token context window for long documents or codebases
- you want a reliable, hosted API with enterprise support
matching performance without increasing cost
For software engineering tasks, Claude Sonnet 4.6’s 20.4-point lead on SWE-bench is significant — but teams can narrow or even overcome this gap by fine-tuning an open model on their own codebase. In CLI and terminal workflows, where GLM-4.7 Flash already shows strong performance, additional fine-tuning on real shell task data can further amplify that advantage while keeping costs low.
frequently asked questions
which is better for coding?
claude sonnet 4.6 — 79.6% vs 59.2% on swe-bench verified, a 20.4-point gap.
what about terminal and shell tasks?
glm-4.7 flash wins — 64.0%* vs 59.1% on terminal bench. note the asterisk may indicate specific evaluation conditions.
does sonnet 4.6 have a longer context window?
yes — 1m tokens vs 128k for glm-4.7 flash. for very long document or codebase tasks, sonnet 4.6’s context window is a structural advantage.