Claude Opus 4.7 vs GLM-5.1
Anthropic Claude Opus 4.7 | Z.ai GLM-5.1 | |
|---|---|---|
| Overview | ||
| Company | Anthropic | Z.ai |
| Release date | Apr 16 2026 | Apr 7 2026 |
| Access | Proprietary | Open Weight |
| Specifications | ||
Parameters | — | 744B |
Context window | 1M | 200k |
| Benchmarks | ||
Nonsense detection BullshitBench v2Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 83% | — |
Agentic coding SWE-Bench ProCan the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 64.3% | — |
Coding SWE-Bench VerifiedReal coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 87.6% | — |
Multilingual coding SWE-Bench MultilingualLike SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 80.5% | — |
Agentic coding CursorBench v3.1Cursor's own test of harder, real-world coding tasks inside a code editor. Higher is better. | 61.6% | — |
Agentic terminal coding Terminal-Bench 2.1Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 66.1% | — |
Agentic terminal coding Terminal-Bench 2.0Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 69.4%Best | 63.5% |
Multi-step tool use MCP AtlasCan the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 79.1% | — |
Web browsing BrowseCompCan the AI browse the web and track down hard-to-find answers? Higher is better. | 79.3%Best | 68% |
Cybersecurity CyberGymTests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 73.1% | — |
Multidisciplinary reasoning Humanity's Last Exam · no toolsHumanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 46.9% | — |
Multidisciplinary reasoning Humanity's Last Exam · with toolsHumanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 54.7% | — |
Abstract reasoning ARC-AGI-2Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 75.8% | — |
Advanced math FrontierMath · Tier 1–3Very hard, research-level math problems. Tiers 1–3 are the (still extremely difficult) lower tiers. Higher is better. | 43.8% | — |
Advanced math FrontierMath · Tier 4Very hard, research-level math problems. Tier 4 is the hardest — close to what professional research mathematicians tackle. Higher is better. | 22.9% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 94.2%Best | 86.2% |
Agentic computer use OSWorld-VerifiedCan the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 78% | — |
Agentic financial analysis Finance Agent v2Tests the AI on real financial-analysis work, like digging through reports and making sound decisions. Higher is better. | 51.5% | — |
Knowledge work GDPval-AAMeasures how well the AI does economically valuable knowledge work, judged against human experts. Shown as a rating (like a chess Elo) — higher is better. | 1753 | — |
Knowledge work GDPval (win/tie rate)How often the AI's work matches or beats a human expert's on real knowledge-work tasks. Higher is better. | 80.3% | — |
Chart reasoning CharXiv ReasoningCan the AI read and reason about complex charts and figures, not just text? Higher is better. | 82.1% | — |
Multimodal reasoning MMMU-ProA tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 75.2% | — |
Spatial reasoning Blueprint-Bench 2Can the AI reason about space and layout — for example, understanding a floor plan or blueprint? Higher is better. | 24.5% | — |
Long context MRCR v2 (8-needle) · 128k averageTests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 59.3% | — |
| Timeline | ||
| Release gap | GLM-5.1 shipped 9 days before Claude Opus 4.7 | |
Which is better: Claude Opus 4.7 or GLM-5.1?
Claude Opus 4.7 leads GLM-5.1 on 3 of the 3 benchmarks they both report (Terminal-Bench 2.0, BrowseComp, GPQA Diamond). GLM-5.1 shipped 9 days before Claude Opus 4.7, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Opus 4.7) vs 200k (GLM-5.1). Claude Opus 4.7 is proprietary, while GLM-5.1 is open weight.
On Terminal-Bench 2.0, Claude Opus 4.7 leads at 69.4% vs GLM-5.1 at 63.5%. On BrowseComp, Claude Opus 4.7 leads at 79.3% vs GLM-5.1 at 68%. On GPQA Diamond, Claude Opus 4.7 leads at 94.2% vs GLM-5.1 at 86.2%.
Frequently asked questions
Claude Opus 4.7 was released by Anthropic on Apr 16 2026.
GLM-5.1 was released by Z.ai on Apr 7 2026.
Claude Opus 4.7 leads on Terminal-Bench 2.0 — Claude Opus 4.7 69.4% vs GLM-5.1 63.5%.
Claude Opus 4.7 leads on GPQA Diamond — Claude Opus 4.7 94.2% vs GLM-5.1 86.2%.
Claude Opus 4.7 has a 1M context window; GLM-5.1 has 200k.
Claude Opus 4.7 is a proprietary model released by Anthropic. GLM-5.1 is an open weight model released by Z.ai.
Other comparisons
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