Qwen3.8-MaxvsGLM-4-9B
Qwen3.8-Max | GLM-4-9B | |
|---|---|---|
| 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 | 9B |
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 | — |
| 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 | — |
| 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. | 95% | — |
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. | 56.6% | — |
PaperBenchResearch reproduction — reproducing the results of an ML research paper end to end | 93% | — |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | 55.9% | — |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | 55.1% | — |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | 11.3% | — |
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% | — |
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% | — |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | 74.8% | — |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | 72.5% | — |
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. | 43.6% | — |
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% | — |
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% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 82.3% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Aug 3 2026 | Jun 5 2024 |
| Access | Proprietary | Open Weight |
Other comparisons
Qwen3.8-MaxvsClaude Fable 5.1GLM-4-9BvsClaude Fable 5.1Qwen3.8-MaxvsGPT-6 AstraGLM-4-9BvsGPT-6 AstraQwen3.8-MaxvsGemini 3.8 FlashGLM-4-9BvsGemini 3.8 FlashQwen3.8-MaxvsMuse Spark 1.3GLM-4-9BvsMuse Spark 1.3Qwen3.8-MaxvsGrok 4.6GLM-4-9BvsGrok 4.6Qwen3.8-MaxvsDeepSeek-V4.1-FlashGLM-4-9BvsDeepSeek-V4.1-Flash
Frequently asked questions
Qwen3.8-Max and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4-9B shipped 789 days before Qwen3.8-Max, so benchmark comparisons should account for the intervening progress.
Qwen3.8-Max has 2.4T parameters, while GLM-4-9B has 9B. Qwen3.8-Max is proprietary, while GLM-4-9B is open weight.
Direct benchmark comparisons are unavailable — Qwen3.8-Max and GLM-4-9B don't publish scores on any of the same benchmarks.