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. | 0.5B/1.5B/3B/7B/14B/32B/72B | 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. | — | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $0.36DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40DeepInfra | — |
| $92.00DeepInfra · fp8 | — | |
| Overview | ||
| Company | Qwen | Z.ai |
| Release date | Sep 19 2024 | Jun 5 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen2.5vsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Qwen2.5vsGPT-6.1 TerraGLM-4-9BvsGPT-6.1 TerraQwen2.5vsGemini 4 CarbonGLM-4-9BvsGemini 4 CarbonQwen2.5vsMuse Spark OpenGLM-4-9BvsMuse Spark OpenQwen2.5vsGrok 4.7GLM-4-9BvsGrok 4.7Qwen2.5vsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen2.5 and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4-9B shipped 106 days before Qwen2.5, so benchmark comparisons should account for the intervening progress.
Qwen2.5 has 0.5B/1.5B/3B/7B/14B/32B/72B parameters, while GLM-4-9B has 9B.
Direct benchmark comparisons are unavailable — Qwen2.5 and GLM-4-9B don't publish scores on any of the same benchmarks.