Grok 4.7vsGLM-5
Grok 4.7 | GLM-5 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 744B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 500k | — |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $2.00 | $1.00 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $6.00 | $3.20 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.50 | $0.20 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.60xAI | $0.60GMICloud |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $4.80xAI | $1.92GMICloud |
| BenchmarksPublished by one model only | ||
BullshitBench v2Nonsense detection — Given 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. | — | 28% |
SWE-Bench VerifiedCoding — Real 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. | — | 77.8% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 73.3% |
DeepSWE 1.1Agentic coding — Artificial 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. | 71% | — |
FrontierSWE V2Ultra-long-horizon coding — Engineering problems that would occupy a person for days: systems implementation, performance work, scientific computing and AI research, with up to twenty hours per task. Partial credit is awarded, because finishing one outright is still rare. Higher is better. | 29% | — |
SWE-Marathon v1.1Multi-hour coding — Twenty multi-hour programming jobs — rebuilding a library, cloning a product, training a model, making something faster. A run counts only when every check passes, hidden tests and interface review included. Higher is better. | 46% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 94% | — |
CADGenBenchCAD generation — Can the AI turn a written design brief into a real CAD model? What it produces has to run as a program and come out the right shape. Higher is better. | 44.4% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 38% | — |
Terminal-Bench 2.0Agentic terminal coding — Can 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. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | 80.3% | — |
CVE-BenchCybersecurity — Can the AI exploit real, publicly known security holes in web applications, in a sandbox built to resemble a live service? A measure of raw capability: it is run without the safeguards a deployed model carries. Higher is better. | 37.7% | — |
CathedralBenchCybersecurity — An outside red team's test: chaining several exploits together to get through a hardened system, scored on the hardest tasks only and run without safeguards, to show what the underlying model can do. Higher is better. | 29% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity'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. | — | 50.4% |
LatchBio Capabilities v1.0Biology — Can the AI do a working biologist's analysis — opening real experimental files and reaching a defensible conclusion across genomics, drug discovery and pathogen surveillance? Averaged over eleven separate tests. Higher is better. | 44.5% | — |
EEBenchElectrical engineering — Can the AI design electronics? It is asked for circuits and chip layouts, and the designs are checked for whether they would physically work — not whether the answer reads well. Higher is better. | 66% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86% |
Harvey's Legal Agent BenchmarkAgentic legal work — Harvey's test of whether an AI agent can complete real legal work — drafting and reviewing documents, working with spreadsheets and presentations, and navigating files the way a lawyer's assistant would. Higher is better. | 19.6% | — |
HealthBench ProfessionalHealth — Realistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | 56.7% | — |
GDPval-AA v2.1Knowledge work — economically valuable knowledge work (v2.1, Crowd-BT Elo fit) | 1695 | — |
AA-Briefcase v1.1Knowledge work — Artificial Analysis agentic office-work eval (Elo, v1.1 rating fit) | 1657 | — |
| Overview | ||
| Company | SpaceXAI | Z.ai |
| Release date | Sep 21 2026 | Feb 12 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Grok 4.7 and GLM-5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-5 is cheaper on both input and output: $1.00 vs $2.00 per million input tokens, and $3.20 vs $6.00 per million output tokens. Figures are base-tier rates. GLM-5 shipped 221 days before Grok 4.7, so benchmark comparisons should account for the intervening progress.
Grok 4.7 is proprietary, while GLM-5 is open weight.
Direct benchmark comparisons are unavailable — Grok 4.7 and GLM-5 don't publish scores on any of the same benchmarks.