Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 744B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 200k |
| 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 | $1.00 |
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 | $3.20 |
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. | — | $0.20 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.325Alibaba | $0.60GMICloud |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.95Alibaba | $1.92GMICloud |
| $163Alibaba | $216GMICloud · fp8 | |
| Benchmarks | ||
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% | 28% |
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% | 77.8% |
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% | 50.4% |
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. | — | 73.3% |
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. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86% |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Apr 2 2026 | Feb 11 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3.6-PlusvsClaude Haiku 5.5GLM-5vsClaude Haiku 5.5Qwen3.6-PlusvsGPT-6.1 TerraGLM-5vsGPT-6.1 TerraQwen3.6-PlusvsGemini 4 CarbonGLM-5vsGemini 4 CarbonQwen3.6-PlusvsMuse Spark OpenGLM-5vsMuse Spark OpenQwen3.6-PlusvsGrok 4.7GLM-5vsGrok 4.7Qwen3.6-PlusvsDeepSeek-V4.1-Flash-1017GLM-5vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.6-Plus leads GLM-5 on 2 of the 3 benchmarks they both report (BullshitBench v2, SWE-Bench Verified, Humanity's Last Exam). Qwen3.6-Plus is cheaper on both input and output: $0.50 vs $1.00 per million input tokens, and $3.00 vs $3.20 per million output tokens. Figures are base-tier rates. GLM-5 shipped 50 days before Qwen3.6-Plus, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Qwen3.6-Plus) vs 200k (GLM-5). Qwen3.6-Plus is closed, while GLM-5 is open weight.
On BullshitBench v2, Qwen3.6-Plus leads at 72% vs GLM-5 at 28%. On SWE-Bench Verified, Qwen3.6-Plus leads at 78.8% vs GLM-5 at 77.8%. On Humanity's Last Exam · with tools, GLM-5 leads at 50.4% vs Qwen3.6-Plus at 28.8%.