Gemma 4 26B-A4B vs Gemma 4 31B
This comparison asks whether a sparse or dense Gemma configuration better fits a specific serving workload. Shared family branding does not remove the need to test memory, latency, and quality independently.
By BenchGrid editorial · Reviewed · Performance not yet measured
| SIDE BY SIDESame questions. Different models. | GoogleGemma 4 26B-A4B | GoogleGemma 4 31B |
|---|---|---|
| Weight memory estimate16-bit · weights only · decimal GB | Pending review | Pending review |
| Parameters | ~26B | ~31B |
| Active parameters | 3.8B | ~31B |
| Context window | 256K | 256K |
| Architecture | MoE · vision | Dense · vision |
| License | Apache 2.0 | Apache 2.0 |
| BenchGrid performance test | Not yet measured | Not yet measured |
| Deployment perspective | Compare this MoE with Gemma 4 31B at the same precision and workload to separate weight memory from active compute. | Use it as a dense counterpart to Gemma 4 26B-A4B. Quantization, context length, and image inputs each affect the memory budget. |
| Primary source | 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.
A4B does not mean a 4B checkpoint
The 26B-A4B publisher card lists a 25.2B backbone, about 3.8B active parameters, and a vision encoder. The 31B entry is a dense counterpart. Active parameters describe sparse computation rather than all the weights a fully resident deployment holds. Do not use the active count as the GPU weight-memory budget.
Keep the comparison within the same workload
Use the same application prompts, input modalities, precision policy, and answer limits. A shared advertised context range does not guarantee equal memory use at that range. Our proposed test starts at low concurrency, then increases load while checking a predefined latency target and task-quality threshold. Report each configuration's actual runtime and checkpoint revision.
No deployment winner has been measured yet
This page provides a sourced specification comparison. Neither the model names nor the sparse architecture establish which endpoint will be faster or cheaper for your traffic. Minimum GPU configurations, measured throughput, and tail latency will be added only after reproducible runs. Until then, use the pair to scope an experiment rather than select a production capacity number.