Grok 4.20 BetavsGLM-5.3
Grok 4.20 Beta | GLM-5.3 | |
|---|---|---|
| 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. | — | 743B |
| 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. | $1.25 | — |
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. | $2.50 | — |
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. | $0.20 | — |
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. | — | $1.40Z.AI |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $4.40Z.AI |
| Benchmarks | ||
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. | 56% | 71% |
| BenchmarksPublished by one model only | ||
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. | — | 66.9% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 28.3% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | — | 84.5% |
ExploitBenchCybersecurity — A 'capability ladder' for security research, built by CMU researchers: the AI is given known bugs in Chrome's V8 engine and scored on how far it gets toward a working exploit inside a research sandbox — from understanding the patch to triggering a crash. Higher is better. | — | 54.4% |
ExploitGym · 6-hour budgetCybersecurity — Can an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 6-hour compute budget per case. Higher is better. | — | 130 |
ExploitGym · 2-hour budgetCybersecurity — Can an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 2-hour compute budget per case. Higher is better. | — | 105 |
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. | — | 62.5% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 53.3% | — |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 28.5% |
AutomationBenchBusiness workflows — Tests 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. | — | 48.2% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1769 |
| Overview | ||
| Company | SpaceXAI | Z.ai |
| Release date | Feb 17 2026 | Aug 14 2026 |
| Access | Proprietary | Proprietary |
Other comparisons
Grok 4.20 BetavsClaude Opus 5GLM-5.3vsClaude Opus 5Grok 4.20 BetavsGPT-5.6 SolGLM-5.3vsGPT-5.6 SolGrok 4.20 BetavsGemini 3.7 FlashGLM-5.3vsGemini 3.7 FlashGrok 4.20 BetavsMuse GlimmerGLM-5.3vsMuse GlimmerGrok 4.20 BetavsDeepSeek-V4-Pro-0813GLM-5.3vsDeepSeek-V4-Pro-0813Grok 4.20 BetavsMistral Medium 3.5GLM-5.3vsMistral Medium 3.5
Frequently asked questions
GLM-5.3 leads Grok 4.20 Beta on 1 of the 1 benchmark they both report (BullshitBench v2). Only Grok 4.20 Beta has a verified first-party API price: $1.25 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3. Grok 4.20 Beta shipped 178 days before GLM-5.3, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On BullshitBench v2, GLM-5.3 leads at 71% vs Grok 4.20 Beta at 56%.
Grok 4.20 Beta was released by SpaceXAI on Feb 17 2026.
GLM-5.3 was released by Z.ai on Aug 14 2026.
Only Grok 4.20 Beta has a verified first-party API price: $1.25 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3. Rates are pay-as-you-go API prices verified on August 18, 2026.