r/LocalLLaMA · past month

Gemma 4 — October 2026

Community sentiment from 10 posts · 400 comments · collected November 10, 2026 · generated November 10, 2026

Gemma 4 — October 2026 scores 47.79/100 overall in the r/LocalLLaMA community benchmark, collected November 10, 2026 from 10 posts and 400 comments. Its strongest category is Speed at 60/100, and its weakest is Agentic & tool use at 32/100.

47.79/ 100

Confidence: medium — 400 comments across 10 posts were read, but only a minority address Gemma 4 directly (the rest discuss Qwen, hardware, or other topics); 6 of 10 categories have direct data, 4 are null. Disk-only evidence, no truncated data.

Category scores

Intelligence

55

Mixed direct statements: 'Gemma 4 is still goated for stuff like translation' (1xkcyn6, score 3) and 'crazy good gemma ones' incl. artemis-31b fine-tunes (1x3gpyi, score 15) vs jokes that qwen 'running circles around it' (1xkcyn6, score 12) and 'Google... always choose Qwen3.5 4b over Gemma' for small-model finetuning (1xkcyn6, score 4).

Coding quality

38

Directly negative: 'Gemma4 is not a model trained for agentic coding work. It can easily get stuck when driving agents' (1x2mw1o, score 11); 'one just wants to finish the job fast' vs Qwen's persistence on smaller models (1x2mw1o, score 21). Only neutral use: 'Qwen 27B or Gemma 31B for coding or text depending on the job' (1x6stod, score 2).

Instruction following

48

Negative: complaints about 'Gemma-isms such as always asking Are you (x), or just (y)? after every response in an unnatural way, and still retaining some of that uncanny gemma tone' (1xjdjy0, score 2). Positive: 'good results with base Gemma, system prompting, and some examples' (1xjdjy0, score 5) and base Gemma 4 31B noted for NOT inventing tool capabilities like a fine-tune did (1xjdjy0, score 19).

Long context

no data

No data in the sample — no direct user statements about Gemma 4's long-context behavior; the only long-chat mention (one-word replies late in long chats) is an explicit guess about a fine-tune, not the base model.

Price / value

no data

No data in the sample — no comment in the 10 posts compares Gemma 4's price or value-for-money against other models.

Speed

60

Positive: 'Gemma 4 MoE would be much quicker with no quality loss' (1xjdjy0, score 6), Gemma on CPU+RAM at '~200t/s pp/s, 23tg/s' (1wwocom, score 3), MoE Gemma 'will happily run on 8GB' (1x3gpyi, score 8). Negative in a degenerate hardware context: gemma4:26b-a4b 'minimum usable speed is about 3 tokens per second' on a smart-TV array (1x4wcnn, score 12) and 'slower than a human typing' at 0.39 tok/s (1x4wcnn, score 4).

Reliability & uptime

no data

No data in the sample — no comment discusses Gemma 4's reliability, stability over time, or uptime.

Creativity & writing

55

Positive: 'crazy good gemma ones' for creative work with artemis-31b v1.1 'especially' (1x3gpyi, score 15) and good NPC-dialogue results with base Gemma (1xjdjy0, score 5). Negative: 'uncanny gemma tone' and 'the dreaded emdash' complaints carried into a fine-tune (1xjdjy0, score 2).

Multimodal & vision

no data

No data in the sample — no comment discusses Gemma 4's vision or multimodal capabilities.

Agentic & tool use

32

Directly negative: 'Gemma4 is not a model trained for agentic coding work. It can easily get stuck when driving agents' (1x2mw1o, score 11). Positive counterpoint: 'Base Gemma 4 31b doesn't do that' — i.e. does not hallucinate tool actions like inventing a calendar skill (1xjdjy0, score 19).

Summary

The sample spans the 10 highest-scoring posts of the 2026-10 r/localllama set that include 'gemma-4' in their model list, holding 400 comments in total. Gemma 4 appears in them mostly as a family member (12B, 26B-A4B MoE, 31B dense) inside VRAM-fit rankings, low-end hardware threads, a space-datacenter story, and a thread about a Gemma-4-31B Humanlike-Chat fine-tune, with a minority of comments addressing the model's quality directly.

Where users speak directly, the strengths are efficiency and fit: the MoE variant is called 'much quicker with no quality loss' against the dense base, Gemma on CPU+RAM is reported at ~200 tok/s prompt processing, MoE Gemma models 'will happily run on 8GB', and base Gemma 31B is used successfully for character dialogue with system prompting. Gemma 4 is also called 'goated for stuff like translation', and Gemma-based fine-tunes such as artemis-31b are described as 'crazy good' for creative work.

The weaknesses in these files are concentrated in agentic and coding use: one engineer states Gemma4 'is not a model trained for agentic coding work' and 'can easily get stuck when driving agents', and the same thread notes Gemma 'just wants to finish the job fast' where Qwen is 'more stubborn about getting it right'. Personality complaints include 'Gemma-isms' such as a repetitive 'Are you (x), or just (y)?' question and an 'uncanny gemma tone', and low-score jokes treat the model as trivially small ('a whole data center in orbit just to run gemma 4 for 20 minutes') or say Google itself picks Qwen3.5 4b over Gemma for small-model finetuning.

Four of the ten categories have no direct data in these files at all — long context, price/value, reliability/uptime, and multimodal/vision — so the overall picture is of a fast, low-VRAM-friendly local workhorse with a solid reputation for translation and creative fine-tuning bases, but a weak one for agentic coding, with its characteristic tone a recurring irritant for users who want realistic dialogue.

Pros and cons

Pros

  • MoE Gemma 4 is praised as 'much quicker with no quality loss' compared to the dense base for dialogue-style output (1xjdjy0).
  • Gemma 4 is called 'goated for stuff like translation' (1xkcyn6).
  • MoE Gemma models 'will happily run on 8GB' with --fit on, and Gemma on CPU+RAM is reported at ~200 tok/s prompt processing (1x3gpyi, 1wwocom).
  • Base Gemma 4 31B gives 'good results' for character/NPC dialogue with system prompting, and is noted for not hallucinating tool capabilities that a fine-tune invented (1xjdjy0).
  • Gemma-based fine-tunes (e.g. artemis-31b v1.1) are described as 'crazy good' and personally preferred over other creative-writing models (1x3gpyi).

Cons

  • 'Gemma4 is not a model trained for agentic coding work. It can easily get stuck when driving agents' (1x2mw1o).
  • Gemma perceived as less persistent than Qwen on coding: 'one just wants to finish the job fast while the other is more stubborn about getting it right' (1x2mw1o).
  • Recurring 'Gemma-isms' complaints: repetitive 'Are you (x), or just (y)?' questions, 'uncanny gemma tone', and 'the dreaded emdash' (1xjdjy0).
  • At very low throughput (3 tok/s on a smart-TV array) Gemma 4 26B A4B is deemed unusable — 'slower than a human typing' (1x4wcnn).
  • Ecosystem signal: 'when they finetune small models, they always choose Qwen3.5 4b over Gemma' (1xkcyn6), plus jokes framing Gemma 4 as trivially small (1xkcyn6).

Top quotes

  • “there's some crazy good gemma ones too, which I personally think are better (artemis-31b v1.1 especially)”
    Spectrum1523 · 15 points positive view on Reddit ↗
  • “Gemma4 is not a model trained for agentic coding work. It can easily get stuck when driving agents.”
    D-3r1stljqso3 · 11 points negative view on Reddit ↗
  • “The Ollama default `gemma4:26b-a4b` (Q4\_K\_M) is about 16 GB and the minimum usable speed is about 3 tokens per second, so chaining 15 tvs to run a model is pretty useless. **Prompt:** Hi Gemma >*\”
    Clean-Data-259 · 12 points negative view on Reddit ↗
  • “Gemma 4 MoE would be much quicker with no quality loss. Why did you train from dense as a base? ”
    semangeIof · 6 points positive view on Reddit ↗
  • “I'm having good results with base Gemma, system prompting, and some examples.”
    goldcakes · 5 points positive view on Reddit ↗
  • “Gemma 4 is still goated for stuff like translation”
    MerePotato · 3 points positive view on Reddit ↗
  • “prone to certain Gemma-isms such as always asking "Are you (x), or just (y)?" after every response in an unnatural way, and still retaining some of that uncanny gemma tone. it even used the dreaded em”
    RandumbRedditor1000 · 2 points negative view on Reddit ↗
  • “Gemma with map might do it (~200t/s pp/s, 23tg/s after 50% ram usage by other processes, with vram mostly only for the model)”
    RobustLokiX · 3 points positive view on Reddit ↗
  • “Those MoE Gemma & Qwen models will happily run on 8GB with `--fit on`!”
    FriskyFennecFox · 8 points positive view on Reddit ↗
  • “they put a whole data center in orbit just to run gemma 4 for 20 minutes at a time meanwhile some dude in his basement got qwen running circles around it on a laptop with a broken fan”
    Gullible_Implement36 · 12 points negative view on Reddit ↗
  • “It look just a few chats for it to start inventing a calendar skill and events. It even told me it was able to text a fictional person and gave me their response. Base Gemma 4 31b doesn't do that. ”
    SevereBathtub · 19 points positive view on Reddit ↗
  • “The efficiency gap with Gemma vs Qwen's persistence is something I've noticed too on smaller models, like one just wants to finish the job fast while the other is more stubborn about getting it right ”
    linear_parson · 21 points negative view on Reddit ↗

Top sample posts

Top 5 of 10 posts by score

FAQ

What is Gemma 4 — October 2026's overall community score?
47.79 out of 100, with medium confidence: 400 comments across 10 posts were read, but only a minority address Gemma 4 directly (the rest discuss Qwen, hardware, or other topics); 6 of 10 categories have direct data, 4 are null. Disk-only evidence, no truncated data.
How was the Gemma 4 — October 2026 benchmark collected?
From 10 posts and 400 comments on r/LocalLLaMA sampled over the past month window.
Which categories does the Gemma 4 — October 2026 report score?
Intelligence, Coding quality, Instruction following, Long context, Price / value, Speed, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
What are the main community pros and cons for Gemma 4 — October 2026?
Top pros: MoE Gemma 4 is praised as 'much quicker with no quality loss' compared to the dense base for dialogue-style output (1xjdjy0).; Gemma 4 is called 'goated for stuff like translation' (1xkcyn6).. Main cons: 'Gemma4 is not a model trained for agentic coding work. It can easily get stuck when driving agents' (1x2mw1o).; Gemma perceived as less persistent than Qwen on coding: 'one just wants to finish the job fast while the other is more stubborn about getting it right' (1x2mw1o)..
When was the Gemma 4 — October 2026 data collected?
Collected on November 10, 2026; the report was generated on November 10, 2026.

Collected from public r/LocalLLaMA posts and comments via Reddit JSON API on November 10, 2026.

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