Claude Opus 5vsGLM-4.7
Claude Opus 5 | GLM-4.7 | |
|---|---|---|
| Specifications | ||
Context window | 1M | 128k |
| Benchmarks | ||
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. | — | 73.8% |
Agentic coding DeepSWE 1.1Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 68.8% | — |
Agentic coding FrontierCode v1.1 (Main)A set of very hard, frontier-difficulty coding tasks an AI agent has to complete end to end. The score is the share of tasks in the main split it solves. Higher is better. | 53.4% | — |
Agentic computer work Frontier-Bench v0.1A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better. | 43.3% | — |
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. | — | 41% |
Web browsing BrowseCompCan the AI browse the web and track down hard-to-find answers? Higher is better. | 90.8%Best | 52% |
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. | 56.3%Best | 24.8% |
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. | 64.7%Best | 42.8% |
Novel problem-solving ARC-AGI-3The third generation of the ARC-AGI series: instead of static puzzles, the AI is dropped into small interactive game-like environments it has never seen and has to figure out the rules and solve them on its own. Higher is better. | 30.2% | — |
Biology BioMysteryBench · hardReal unsolved-style biology puzzles — the AI has to reason its way to an answer the way a research biologist would. The “hard” split contains the toughest cases. Higher is better. | 49.4% | — |
Biology BioMysteryBench · human solvedReal biology puzzles that human experts have managed to crack — can the AI reach the same answers? Higher is better. | 90.1% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 85.7% |
Agentic computer use OSWorld 2.0Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | 70.6% | — |
Business workflows AutomationBenchTests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 26% | — |
Agentic legal work Harvey's Legal Agent Benchmark (Held-out)Harvey's test of whether an AI agent can complete real legal work, scored on a held-out set of tasks the model makers never see — making the numbers harder to game. Higher is better. | 11.7% | — |
Health HealthBench ProfessionalRealistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | 59.8% | — |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | 1861 | — |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | — | 1440 |
| Overview | ||
| Company | Anthropic | Z.ai |
| Release date | Jul 24 2026 | Dec 22 2025 |
| Access | Proprietary | Open Weight |
Which is better: Claude Opus 5 or GLM-4.7?
Claude Opus 5 leads GLM-4.7 on 3 of the 3 benchmarks they both report (BrowseComp, Humanity's Last Exam). GLM-4.7 shipped 214 days before Claude Opus 5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Opus 5) vs 128k (GLM-4.7). Claude Opus 5 is proprietary, while GLM-4.7 is open weight.
On BrowseComp, Claude Opus 5 leads at 90.8% vs GLM-4.7 at 52%. On Humanity's Last Exam · no tools, Claude Opus 5 leads at 56.3% vs GLM-4.7 at 24.8%. On Humanity's Last Exam · with tools, Claude Opus 5 leads at 64.7% vs GLM-4.7 at 42.8%.
Frequently asked questions
Claude Opus 5 was released by Anthropic on Jul 24 2026.
GLM-4.7 was released by Z.ai on Dec 22 2025.
Claude Opus 5 leads on Humanity's Last Exam · no tools — Claude Opus 5 56.3% vs GLM-4.7 24.8%.
Claude Opus 5 has a 1M context window; GLM-4.7 has 128k.
Claude Opus 5 is a proprietary model released by Anthropic. GLM-4.7 is an open weight model released by Z.ai.
Other comparisons
Claude Opus 5vsGPT-5.6 SolGLM-4.7vsGPT-5.6 SolClaude Opus 5vsGemini 3.6 FlashGLM-4.7vsGemini 3.6 FlashClaude Opus 5vsMuse Spark 1.1GLM-4.7vsMuse Spark 1.1Claude Opus 5vsGrok 4.5GLM-4.7vsGrok 4.5Claude Opus 5vsDeepSeek-V4-ProGLM-4.7vsDeepSeek-V4-ProClaude Opus 5vsMistral Medium 3.5GLM-4.7vsMistral Medium 3.5