AI tool comparison
Claude Opus 4.7 vs GLM-5.1
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Foundation Models
Claude Opus 4.7
Anthropic's new flagship — 87.6% SWE-bench, 1M context
75%
Panel ship
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Community
Paid
Entry
Claude Opus 4.7 is Anthropic's latest flagship model, released April 16. It scores 87.6% on SWE-bench Verified — a 13-point improvement over Claude Opus 4.6 — and 94.2% on GPQA, making it competitive with the top frontier models on coding and scientific reasoning benchmarks. The context window extends to 1 million tokens with substantially improved retrieval accuracy at the far end of the window. The release introduces "Routines" — a first-party feature for defining persistent agentic workflows that Claude can execute autonomously across multiple sessions. Routines are defined in structured YAML and can include tool calls, conditional logic, and human-in-the-loop checkpoints. Anthropic positions this as a more reliable alternative to custom agent frameworks for common use cases. Pricing remains unchanged from Opus 4.6: $5/M input tokens, $25/M output tokens. The vision input resolution has been increased by 3.3x, which meaningfully improves performance on documents, diagrams, and UI screenshots. Available via API immediately and rolling out to Claude.ai Pro and Team plans over the next week.
AI Models
GLM-5.1
Zhipu AI's 744B MIT-licensed model that beats Claude and GPT on SWE-Bench
50%
Panel ship
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Community
Paid
Entry
GLM-5.1 is Zhipu AI's latest open-weights language model — a 744B parameter mixture-of-experts (MoE) architecture that activates 40B parameters per forward pass. Released under the MIT license with a 200,000-token context window, it has quietly topped the SWE-Bench Pro leaderboard, surpassing both Claude Opus 4.6 and GPT-5.4 on expert-level software engineering tasks. The MoE architecture means GLM-5.1 is significantly cheaper to run per token than a dense 744B model, with inference costs approaching dense 40B models for most workloads. Zhipu AI (a Tsinghua University spin-out) has steadily iterated on the GLM family to produce a text-focused reasoning model that holds its own against proprietary frontier models — now, for the first time, reportedly exceeding them on coding benchmarks. The MIT license is the headline for enterprise and research users: full commercial use, no usage restrictions, no API dependency. This puts GLM-5.1 in direct competition with Qwen3.5 for the "best open-weights model you can actually use for anything" crown, with a differentiating edge in software engineering tasks specifically.
Reviewer scorecard
“87.6% on SWE-bench isn't a small improvement — that's a meaningful jump for real-world coding tasks. The Routines feature addresses the biggest pain point with Claude in production: reliable multi-step agent behavior without building a custom framework.”
“SWE-Bench Pro beating Claude and GPT-5.4 is the real signal here. For coding automation workflows, having an MIT-licensed 200K context model at that quality tier changes the build-vs-buy calculus significantly. Deploying this on dedicated hardware is now a serious option for engineering teams.”
“Benchmarks look great but the 1M context window performance hasn't been independently validated at the limits. Routines sound powerful but the YAML spec is still in beta with known edge cases. If you're running stable Opus 4.6 workflows, wait a week for the community to stress-test this before migrating.”
“744B total parameters still requires serious infrastructure — you're looking at 8x H100s at minimum for comfortable inference. The 40B active parameters help with cost but not with deployment complexity. This is 'open source' for well-funded teams, not indie builders.”
“Anthropic is quietly winning the enterprise coding agent race. The combination of top SWE-bench scores with the Routines feature is a moat — developers don't switch orchestration frameworks easily once workflows are deployed. This release deepens that lock-in strategically.”
“The open-weights ecosystem has now fully caught up to proprietary models on the most demanding software engineering benchmarks. This is the moment the 'open vs closed' debate definitively changes — the argument that proprietary models are categorically better no longer holds.”
“The 3.3x vision resolution upgrade is underrated for design work. Document analysis, layout review, and iterating on visual mockups are all dramatically better. I can finally paste a full Figma export and get coherent feedback on the entire design rather than just the top half.”
“Unless you're a creative tech team with serious infrastructure, this isn't practical for most creative workflows. The quality is undeniably impressive but the deployment story doesn't fit solo creators or small studios.”
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