Qwen3.8-Flash-NextvsGLM-5.2
Qwen3.8-Flash-Next | 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. | 125B | 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. | 262k | 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. | — | $0.50Sail Research |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $2.00Ambient |
| Benchmarks | ||
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. | 62.5% | 62.1% |
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. | 58.7% | 44% |
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. | 35.9% | 40.5% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.7% | 91.2% |
| 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 MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 81% | — |
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. | — | 88% |
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. | 48.1% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 91.9% | — |
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 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. | 55.7% | — |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | 73.9% | — |
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. | 73.5% | — |
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% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 81.3% | — |
OSWorld 2.0Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | 19.4% | — |
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. | 24.3% | — |
Agent's Last Exam · scoreAgentic computer use — The graded score on the same desktop and operating-system tasks in Agent's Last Exam, giving partial credit for progress beyond the strict pass-or-fail result. Higher is better. | 51.2% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1514 |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 84.6% | — |
LVBenchVideo understanding — Can the AI follow a very long video — up to an hour — and answer questions that need details from far apart in it? Higher is better. | 76.6% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Aug 26 2026 | Jun 16 2026 |
| Access | Open Weight | Open Weight |
Other comparisons
Qwen3.8-Flash-NextvsClaude Opus 5GLM-5.2vsClaude Opus 5Qwen3.8-Flash-NextvsGPT-5.6 SolGLM-5.2vsGPT-5.6 SolQwen3.8-Flash-NextvsGemini 3.7 FlashGLM-5.2vsGemini 3.7 FlashQwen3.8-Flash-NextvsMuse GlimmerGLM-5.2vsMuse GlimmerQwen3.8-Flash-NextvsGrok 4.6GLM-5.2vsGrok 4.6Qwen3.8-Flash-NextvsDeepSeek-V4-Pro-0813GLM-5.2vsDeepSeek-V4-Pro-0813
Frequently asked questions
Qwen3.8-Flash-Next leads GLM-5.2 on 3 of the 4 benchmarks they both report (SWE-Bench Pro, DeepSWE 1.1, Humanity's Last Exam, GPQA Diamond). 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-Flash-Next. GLM-5.2 shipped 71 days before Qwen3.8-Flash-Next, so benchmark comparisons should account for the intervening progress.
Qwen3.8-Flash-Next has 125B parameters, while GLM-5.2 has 744B. Context windows are 262k (Qwen3.8-Flash-Next) vs 1M (GLM-5.2).
On SWE-Bench Pro, Qwen3.8-Flash-Next leads at 62.5% vs GLM-5.2 at 62.1%. On DeepSWE 1.1, Qwen3.8-Flash-Next leads at 58.7% vs GLM-5.2 at 44%. On Humanity's Last Exam · no tools, GLM-5.2 leads at 40.5% vs Qwen3.8-Flash-Next at 35.9%. On GPQA Diamond, Qwen3.8-Flash-Next leads at 91.7% vs GLM-5.2 at 91.2%.
Qwen3.8-Flash-Next was released by Qwen on Aug 26 2026.
GLM-5.2 was released by Z.ai on Jun 16 2026.
Qwen3.8-Flash-Next leads on SWE-Bench Pro — Qwen3.8-Flash-Next 62.5% vs GLM-5.2 62.1%.
GLM-5.2 leads on Humanity's Last Exam · no tools — Qwen3.8-Flash-Next 35.9% vs GLM-5.2 40.5%.
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-Flash-Next. Rates are pay-as-you-go API prices verified on August 18, 2026.
Qwen3.8-Flash-Next has a 262k context window; GLM-5.2 has 1M.