Compare open AI models
Compare GPU memory estimates, parameter counts, context windows, and licenses side by side.
| SIDE BY SIDESame questions. Different models. | AlibabaQwen3 8B | GoogleGemma 3 12B | AlibabaQwen3 32B |
|---|---|---|---|
| Weight memory estimate16-bit · weights only · decimal GB | 16.4 GB | 24 GB | 65.6 GB |
| Parameters | 8.2B | ~12B | 32.8B |
| Active parameters | 8.2B | ~12B | 32.8B |
| Context window | 32K native | 128K | 32K native |
| Architecture | Dense | Dense · vision | Dense |
| License | Apache 2.0 | Gemma | Apache 2.0 |
| BenchGrid performance test | Not yet measured | Not yet measured | Not yet measured |
| Deployment perspective | A useful small-model baseline. Start with a short context and measure the additional latency of thinking mode separately. | The nominal 16-bit weights alone approach 24 GB. Leave room for runtime allocations, KV cache, and image processing. | Weight precision changes the hardware shortlist dramatically. Quality and performance still need to be tested on the chosen checkpoint. |
| Primary source | Model card | Model card | Model card |
Lower weight memory does not mean better quality or faster inference. These calculations exclude serving overhead and do not confirm a working quantized checkpoint. Read the methodology.
Deployment comparisons
Selected pairs, with sourced specifications and a clear test plan.
Qwen3.8 27B vs Gemma 4 12B
Compare Qwen3.8 27B and Gemma 4 12B deployment specifications, multimodal architecture, context limits, and what remains to be benchmarked.
Read comparison →MiMo V2.6 Distill 9B vs Qwen3.5 9B
Compare MiMo V2.6 Distill Qwen 9B with Qwen3.5 9B: checkpoint identity, deployment scope, and a fair distillation evaluation plan.
Read comparison →Gemma 4 26B-A4B vs Gemma 4 31B
Compare sparse Gemma 4 26B-A4B and dense Gemma 4 31B: total versus active parameters, deployment trade-offs, and a reproducible test plan.
Read comparison →Qwen3 8B vs Llama 3.1 8B
Compare Qwen3 8B and Llama 3.1 8B baseline specifications, 4/8/16-bit weight estimates, context configuration, and reproducibility considerations.
Read comparison →How to compare model deployment requirements
Start with the exact checkpoint and input modalities your application needs. Compare total parameters, context configuration, license, and runtime support before estimating weight memory. Then validate the configuration under a representative workload.
Is the memory estimate a minimum GPU requirement?
No. It estimates raw weight storage at the selected precision in decimal GB. KV cache, quantization metadata, encoders, and runtime allocations can add memory. Pending entries have not yet had their complete checkpoint scope reconciled.
Can I compare inference speed here?
Not yet. BenchGrid has not run GPU performance tests. The table separates publisher specifications and calculated estimates from measured performance; it does not rank models by speed or quality.
Why are total and active parameters different?
A sparse model may use only some experts for each token while retaining a much larger checkpoint. Use the complete weight scope for storage planning. Read our MoE memory field note.
Memory is the starting point.
Latency, throughput, and quality need a test that reflects your workload.
Learn to read a benchmark