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. | — | 9B |
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 | 128k |
| 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 | — |
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 | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.95Alibaba | — |
| $163Alibaba | — | |
| These models have no shared benchmark scores. | ||
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% | — |
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% | — |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Apr 2 2026 | Jun 5 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3.6-PlusvsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Qwen3.6-PlusvsGPT-6.1 SolGLM-4-9BvsGPT-6.1 SolQwen3.6-PlusvsGemini 4 ArgonGLM-4-9BvsGemini 4 ArgonQwen3.6-PlusvsMuse Spark OpenGLM-4-9BvsMuse Spark OpenQwen3.6-PlusvsGrok 4.7GLM-4-9BvsGrok 4.7Qwen3.6-PlusvsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.6-Plus and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. 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-4-9B. GLM-4-9B shipped 666 days before Qwen3.6-Plus, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Qwen3.6-Plus) vs 128k (GLM-4-9B). Qwen3.6-Plus is closed, while GLM-4-9B is open weight.
Direct benchmark comparisons are unavailable — Qwen3.6-Plus and GLM-4-9B don't publish scores on any of the same benchmarks.