# Gemma 4 26B-A4B vs 31B for text coding tasks

Compare Gemma 4 26B-A4B and 31B for text coding tasks using their documented MoE and dense designs, matched thinking and independent patch checks.

Author: Retrivium team
Updated: 2026-11-03
Canonical URL: https://retrivium.ai/journal/gemma4-26b-a4b-vs-31b-coding/
Language: en

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](https://huggingface.co/google/gemma-4-26B-A4B-it/raw/main/README.md) lists a MoE model with 25.2B total and 3.8B active parameters in its table. The [31B card](https://huggingface.co/google/gemma-4-31B-it/raw/main/README.md) lists 30.7B for the dense model. The [official overview](https://ai.google.dev/gemma/docs/core) 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.

## Related reading

- [Qwen3-Coder VRAM: Check the Exact Model Variant](/journal/qwen3-coder-vram-requirements/)
- [A GPU inference benchmark you can trust](/journal/inference-benchmark-method/)