at a glance

GLM-4.7 FlashClaude Opus 4.6
providerZhipu AIAnthropic
parameters730B total / 3B active (MoE)~large (est.)
context window128k tokens1m tokens

benchmarks

Cost (per 1M tokens) ?
GLM-4.7 Flash
$0.07 in / $0.40 out
Claude Opus 4.6
$5.00 in / $25.00 out
GPQA Diamond (graduate science) ?
GLM-4.7 Flash
75.2%
Claude Opus 4.6
91.3%
SWE-bench Verified (software engineering) ?
GLM-4.7 Flash
59.2%
Claude Opus 4.6
80.8%
TAU-bench (agentic tool use) ?
GLM-4.7 Flash
79.5%
Claude Opus 4.6
91.9%
Terminal Bench (shell tasks) ?
GLM-4.7 Flash
64.0%*
Claude Opus 4.6
65.4%
GLM-4.7 Flash Claude Opus 4.6 bold score = winner

what are these models?

GLM-4.7 Flash is Zhipu AI’s fast inference model from the GLM-4.7 family. Despite being designed for speed and efficiency, it shows exceptional strength on mathematical reasoning — exceeding expectations for a “flash” tier model.

Claude Opus 4.6 is Anthropic’s flagship model — their most capable tier, designed for complex reasoning, software engineering, and advanced agentic tasks. It is closed-source and accessed via Anthropic’s API.

benchmark breakdown

Claude Opus 4.6 wins on all four benchmarks. GPQA Diamond (91.3% vs 75.2%), SWE-bench Verified (80.8% vs 59.2%), TAU-bench (91.9% vs 79.5%), and Terminal Bench (65.4% vs 64.0%*) all favor Anthropic’s flagship. The largest gaps are on science (16 points) and software engineering (21.6 points).

what people are saying

when to use GLM-4.7 Flash

  • you need fast, cost-efficient inference — Opus 4.6 is Anthropic’s most expensive model tier
  • you’re building applications where lower latency matters more than peak benchmark performance

when to use Claude Opus 4.6

  • software engineering is your primary use case (80.8% vs 59.2% on SWE-bench)
  • agentic tool-calling with high reliability is required
  • graduate-level scientific reasoning is important
  • terminal and CLI automation is part of your workflow
  • you need a 1M token context window and Anthropic’s enterprise support

closing the performance gap at the same cost

Claude Opus 4.6 leads across all four benchmarks tested here. For software engineering and knowledge-intensive tasks, Opus 4.6 has substantial advantages. For teams needing lower inference costs, GLM-4.7 Flash is the more practical option — and fine-tuning it on your domain can close some of the gap on specific tasks.

frequently asked questions

which model should i use for coding?

claude opus 4.6 — 80.8% vs 59.2% on swe-bench verified, a 21-point advantage.

which is better for terminal tasks?

claude opus 4.6 — 65.4% vs 64.0%*. note the asterisk on glm may indicate specific evaluation conditions.

is claude opus 4.6 worth the premium over claude sonnet 4.6?

for most tasks claude sonnet 4.6 delivers strong results at lower cost. opus 4.6 is the better choice when you need maximum performance on complex reasoning, agentic workflows, or high-stakes software engineering tasks.