Qwen3.8-MaxvsGLM-5.3-Flash
Qwen3.8-Max | GLM-5.3-Flash | |
|---|---|---|
| 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. | 2.4T | 320B |
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. | 1M | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $2.00Alibaba | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $6.00Alibaba | — |
| Benchmarks | ||
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 86.6% | 84.3% |
| 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. | 94% | — |
SWE-Bench ProAgentic coding — Can 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. | 67.7% | — |
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. | — | 63.4% |
PaperBenchResearch reproduction — reproducing the results of an ML research paper end to end | 93% | — |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | 53.4% | — |
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. | — | 55.3% |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 86.1% | — |
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. | — | 26.3% |
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. | — | 48.8% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1773 |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 88.4% | — |
BabyVisionVisual reasoning — Tests core visual reasoning — seeing and understanding images the way even young children can, which AIs often find surprisingly hard. Higher is better. | 82% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Aug 3 2026 | Aug 26 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Qwen3.8-MaxvsClaude Opus 5GLM-5.3-FlashvsClaude Opus 5Qwen3.8-MaxvsGPT-5.6 SolGLM-5.3-FlashvsGPT-5.6 SolQwen3.8-MaxvsGemini 3.7 FlashGLM-5.3-FlashvsGemini 3.7 FlashQwen3.8-MaxvsMuse GlimmerGLM-5.3-FlashvsMuse GlimmerQwen3.8-MaxvsGrok 4.6GLM-5.3-FlashvsGrok 4.6Qwen3.8-MaxvsDeepSeek-V4-Pro-0813GLM-5.3-FlashvsDeepSeek-V4-Pro-0813
Frequently asked questions
Qwen3.8-Max leads GLM-5.3-Flash on 1 of the 1 benchmark they both report (Terminal-Bench 2.1). Qwen3.8-Max shipped 23 days before GLM-5.3-Flash, so benchmark comparisons should account for the intervening progress.
Qwen3.8-Max has 2.4T parameters, while GLM-5.3-Flash has 320B. Context windows are 1M (Qwen3.8-Max) vs 1M (GLM-5.3-Flash). Qwen3.8-Max is proprietary, while GLM-5.3-Flash is open weight.
On Terminal-Bench 2.1, Qwen3.8-Max leads at 86.6% vs GLM-5.3-Flash at 84.3%.
Qwen3.8-Max was released by Qwen on Aug 3 2026.
GLM-5.3-Flash was released by Z.ai on Aug 26 2026.
Qwen3.8-Max leads on Terminal-Bench 2.1 — Qwen3.8-Max 86.6% vs GLM-5.3-Flash 84.3%.
Qwen3.8-Max has a 1M context window; GLM-5.3-Flash has 1M.
Qwen3.8-Max is a proprietary model released by Qwen. GLM-5.3-Flash is an open weight model released by Z.ai.