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/27B | 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. | 8k | 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. | $0.65NextBit | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.65NextBit | — |
| $163NextBit · int4 | — | |
| Overview | ||
| Company | Z.ai | |
| Release date | Jun 27 2024 | Jun 5 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemma 2vsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Gemma 2vsGPT-6.1 SolGLM-4-9BvsGPT-6.1 SolGemma 2vsMuse Spark OpenGLM-4-9BvsMuse Spark OpenGemma 2vsGrok 4.7GLM-4-9BvsGrok 4.7Gemma 2vsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Gemma 2vsMistral Large 4GLM-4-9BvsMistral Large 4Frequently asked questions
Gemma 2 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 22 days before Gemma 2, so benchmark comparisons should account for the intervening progress.
Gemma 2 has 9B/27B parameters, while GLM-4-9B has 9B. Context windows are 8k (Gemma 2) vs 128k (GLM-4-9B).
Direct benchmark comparisons are unavailable — Gemma 2 and GLM-4-9B don't publish scores on any of the same benchmarks.