Qwen3.8-Max-0902vsGLM-5.2
Qwen3.8-Max-0902 | GLM-5.2 | |
|---|---|---|
| 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 | 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. | 1M | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.40 |
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. | — | $4.40 |
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. | — | $0.26 |
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. | — | $0.4875DeepInfra |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.56DeepInfra |
| Benchmarks | ||
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. | 69.3% | 44% |
| 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. | — | 31% |
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. | — | 62.1% |
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. | — | 77% |
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. | 64.9% | — |
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. | 70% | — |
Frontier-Bench v0.1Agentic computer work — A hard, ever-evolving set of real computer tasks — coding, system administration, data work, and more — that an AI agent has to complete on its own. Run by the Harbor / Laude Institute team as the successor to Terminal-Bench (v0.1 is the first release of the task set). The score is the share of tasks solved. Higher is better. | — | 5.1% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | 29% | — |
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. | — | 81% |
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. | 64% | — |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | 76.1% | — |
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. | 73.3% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 40.5% |
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. | — | 54.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 91.2% |
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. | 50.8% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1514 |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 82.7% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Sep 2 2026 | Jun 16 2026 |
| Access | Proprietary | Open Weight |
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Frequently asked questions
Qwen3.8-Max-0902 leads GLM-5.2 on 1 of the 1 benchmark they both report (DeepSWE 1.1). Only GLM-5.2 has a verified first-party API price: $1.40 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for Qwen3.8-Max-0902. GLM-5.2 shipped 78 days before Qwen3.8-Max-0902, so benchmark comparisons should account for the intervening progress.
Qwen3.8-Max-0902 has 2.4T parameters, while GLM-5.2 has 744B. Context windows are 1M (Qwen3.8-Max-0902) vs 1M (GLM-5.2). Qwen3.8-Max-0902 is proprietary, while GLM-5.2 is open weight.
On DeepSWE 1.1, Qwen3.8-Max-0902 leads at 69.3% vs GLM-5.2 at 44%.
Qwen3.8-Max-0902 was released by Qwen on Sep 2 2026.
GLM-5.2 was released by Z.ai on Jun 16 2026.
Qwen3.8-Max-0902 leads on DeepSWE 1.1 — Qwen3.8-Max-0902 69.3% vs GLM-5.2 44%.
Only GLM-5.2 has a verified first-party API price: $1.40 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for Qwen3.8-Max-0902. Rates are pay-as-you-go API prices verified on August 18, 2026.
Qwen3.8-Max-0902 has a 1M context window; GLM-5.2 has 1M.
Qwen3.8-Max-0902 is a proprietary model released by Qwen. GLM-5.2 is an open weight model released by Z.ai.