In this article
Gemma's 26B-A4B MoE and 31B dense members differ in expert computation without establishing a universal coding winner.
Gemma 4 26B-A4B and 31B are different architecture choices within one family. A text coding comparison should preserve their actual model identities while judging the same required behavior.
Read the model table rather than the label alone
The 26B-A4B card lists a MoE model with 25.2B total and 3.8B active parameters in its table. The 31B card lists 30.7B for the dense model. The official overview separates dense and MoE members of the family.
Those figures explain the names; they are not a complete memory or speed estimate. Active parameters describe conditional computation, not a promise that only that amount of model data must be available to the runtime.
Separate expert sparsity from attention design
The family cards describe a mixture of local sliding-window and global attention. That sequence-processing design is a different axis from whether the feed-forward computation is dense or uses experts.
Do not call one candidate a small dense model merely because its active-parameter label is smaller. The complete MoE artifact and its runtime path remain relevant. Likewise, dense does not imply that every coding answer is more accurate.
Compare a bounded code change
Use a fictional serializer that must preserve an explicit distinction between a missing field, a null field and an empty string. Supply a small implementation and expected output cases.
| Case | Required behavior |
|---|---|
| Missing field | Follow the declared omission rule |
| Explicit null | Preserve the specified null behavior |
| Empty string | Avoid converting it into missing data |
| Existing ordinary value | Preserve current serialization |
| Unrelated field | Remain unaffected by the patch |
A patch that collapses these states can appear concise and still violate the requirement. Independent tests should check each state, not merely whether the generated function runs.
Hold thinking and context conditions explicit
Both candidates need their own valid tokenizer and template. Keep the chosen thinking condition, supplied source and overall output allowance visible. A different mode or missing reference case can create a comparison unrelated to architecture.
Inspect whether the patch applies and whether the final explanation agrees with the code. A plausible explanation of null handling should not pass if the implementation actually drops null values.
Choose using complete accepted results
Retain exact revisions, model settings, source fixtures and actual checks. Compare accepted patches, state-collapsing errors and incomplete responses separately. Several valid implementations can pass without matching one reference diff.
The result can support a candidate for this text coding workload, but it does not establish universal dense-versus-MoE superiority, guaranteed local fit or a fixed throughput ratio. The documented architecture provides context; the accepted behavior supplies the actual model-selection evidence.