Qwen3.6-PlusvsGLM-5.3-Flash
Qwen3.6-Plus | 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. | — | 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 |
| 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. | $0.50 | — |
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. | $3.00 | — |
| Benchmarks | ||
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. | 28.8% | 55.3% |
| 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. | 72% | — |
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. | 78.8% | — |
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% |
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. | — | 56.3% |
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. | — | 84.3% |
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. | — | 78.4% |
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 Reasoning · with toolsChart reasoning — The same chart-and-figure reasoning test, run with the AI allowed to use tools — writing code to inspect the image, for instance — rather than reading the chart unaided. Scores run higher than the unaided version, so read the two as separate tests. Higher is better. | — | 89.4% |
Chartography · with toolsChart tasks — A chart-centred test run with tools available to the AI, reported separately from the chart-reading benchmarks above it. Higher is better. | — | 78% |
OfficeQA ProDocument Q&A — Questions about office documents, where answering depends on reading the page as a document — layout, tables and figures included — rather than as loose text. Higher is better. | — | 62.4% |
MVBenchVideo understanding — Video questions that cannot be answered from any single frame: the AI has to follow what changes over time — the order things happen in, what moved where. Higher is better. | — | 77.8% |
MMVUVideo reasoning — Expert-level video questions drawn from specific disciplines, where answering means applying subject knowledge to what is happening on screen rather than just describing it. Higher is better. | — | 80.5% |
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. | — | 53.4% |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Apr 2 2026 | Aug 26 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
Qwen3.6-PlusvsClaude Opus 5GLM-5.3-FlashvsClaude Opus 5Qwen3.6-PlusvsGPT-5.6 SolGLM-5.3-FlashvsGPT-5.6 SolQwen3.6-PlusvsGemini 3.7 FlashGLM-5.3-FlashvsGemini 3.7 FlashQwen3.6-PlusvsMuse GlimmerGLM-5.3-FlashvsMuse GlimmerQwen3.6-PlusvsGrok 4.6GLM-5.3-FlashvsGrok 4.6Qwen3.6-PlusvsDeepSeek-V4-Pro-0813GLM-5.3-FlashvsDeepSeek-V4-Pro-0813
Frequently asked questions
GLM-5.3-Flash leads Qwen3.6-Plus on 1 of the 1 benchmark they both report (Humanity's Last Exam). Only Qwen3.6-Plus has a verified first-party API price: $0.50 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3-Flash. Qwen3.6-Plus shipped 146 days before GLM-5.3-Flash, so benchmark comparisons should account for the intervening progress.
Qwen3.6-Plus is proprietary, while GLM-5.3-Flash is open weight.
On Humanity's Last Exam · with tools, GLM-5.3-Flash leads at 55.3% vs Qwen3.6-Plus at 28.8%.
Qwen3.6-Plus was released by Qwen on Apr 2 2026.
GLM-5.3-Flash was released by Z.ai on Aug 26 2026.
GLM-5.3-Flash leads on Humanity's Last Exam · with tools — Qwen3.6-Plus 28.8% vs GLM-5.3-Flash 55.3%.
Only Qwen3.6-Plus has a verified first-party API price: $0.50 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for GLM-5.3-Flash. Rates are pay-as-you-go API prices verified on August 18, 2026.
Qwen3.6-Plus is a proprietary model released by Qwen. GLM-5.3-Flash is an open weight model released by Z.ai.