Qwen3.7-MaxvsGLM-4.6
Qwen3.7-Max | GLM-4.6 | |
|---|---|---|
| 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. | — | 355B |
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. | — | 200k |
| 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. | $2.50 | $0.60 |
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. | $7.50 | $2.20 |
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. | — | $0.11 |
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. | — | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.75Venice |
| Benchmarks | ||
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. | 80.4% | 68% |
| 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. | 71% | — |
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. | 60.6% | — |
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. | 91.6% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 69.7% | — |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 76.4% | — |
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. | 41.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 92.4% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | May 20 2026 | Sep 30 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Qwen3.7-Max leads GLM-4.6 on 1 of the 1 benchmark they both report (SWE-Bench Verified). GLM-4.6 is cheaper on both input and output: $0.60 vs $2.50 per million input tokens, and $2.20 vs $7.50 per million output tokens. GLM-4.6 shipped 232 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Qwen3.7-Max is proprietary, while GLM-4.6 is open weight.
On SWE-Bench Verified, Qwen3.7-Max leads at 80.4% vs GLM-4.6 at 68%.
Qwen3.7-Max was released by Qwen on May 20 2026.
GLM-4.6 was released by Z.ai on Sep 30 2025.
Qwen3.7-Max leads on SWE-Bench Verified — Qwen3.7-Max 80.4% vs GLM-4.6 68%.
GLM-4.6 is cheaper on both input and output: $0.60 vs $2.50 per million input tokens, and $2.20 vs $7.50 per million output tokens. Rates are pay-as-you-go API prices verified on August 18, 2026.
Qwen3.7-Max is a proprietary model released by Qwen. GLM-4.6 is an open weight model released by Z.ai.