Pick Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine. On a tight budget at scale, Muse Glimmer is the value pick.
Gemma 4 26B A4B (Google) and Muse Glimmer (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Gemma 4 26B A4B holds 2× more — 256K (~393 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Muse Glimmer is the newer model by about 4 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemma 4 26B A4B
Muse Glimmer
Provider
Google (US)
Meta (US)
Released
April 2, 2026
August 10, 2026
Context window
256K (~393 pages)
128K (~197 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total): Gemma 4 26B A4B — An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost — and it carries the larger 256K context.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost: Gemma 4 26B A4B — Its 256K window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6): Gemma 4 26B A4B — Gemma 4 26B A4B lists strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6) among its strengths; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit: Muse Glimmer — Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine: Muse Glimmer — Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; Gemma 4 26B A4B does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery: Muse Glimmer — Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; Gemma 4 26B A4B does not.
Lowest cost at scale: Muse Glimmer — Its weights are open, so at volume you pay for your own hardware instead of Gemma 4 26B A4B's $0.15/$0.6 per 1M tokens.
Largest single-prompt input: Gemma 4 26B A4B — Its 256K window is about 2× larger than Muse Glimmer's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Muse Glimmer — At Open weight (self-host / free) it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemma 4 26B A4B — Larger 256K window fits more in one prompt.
Anyone whose priority is fast, cheap inference from a sparse moe (3.8b active of 25.2b total): Gemma 4 26B A4B — It is specifically built for that.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit: Muse Glimmer — That is its strongest area.
Gemma 4 26B A4B: where it fits
An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.
Its trade-offs are real: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
Muse Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Gemma 4 26B A4B and Muse Glimmer overlap enough that the right pick depends on your specific job. Muse Glimmer costs less per token; Gemma 4 26B A4B holds the larger context; and each leads in its own area — Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total), Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Gemma 4 26B A4B or Muse Glimmer better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total) while Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 26B A4B or Muse Glimmer?
Muse Glimmer is cheaper — $0.15/$0.6 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Gemma 4 26B A4B — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 26B A4B and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Muse Glimmer and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemma 4 26B A4B or Muse Glimmer?
Muse Glimmer — released August 10, 2026, about 4 months after Gemma 4 26B A4B.
Gemma 4 26B A4B vs Muse Glimmer
Google · US | Meta · US · Updated June 2026
Quick verdict
Pick Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. Pick Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit or open weights (apache 2.0), free to self-host and offline-capable - data never leaves your machine. On a tight budget at scale, Muse Glimmer is the value pick.
Gemma 4 26B A4B (Google) and Muse Glimmer (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Gemma 4 26B A4B holds 2× more — 256K (~393 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Muse Glimmer is the newer model by about 4 months (released August 10, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemma 4 26B A4B
Muse Glimmer
Provider
Google (US)
Meta (US)
Released
April 2, 2026
August 10, 2026
Context window
256K (~393 pages)
128K (~197 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total)
Gemma 4 26B A4B
An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost — and it carries the larger 256K context.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost
Gemma 4 26B A4B
Its 256K window holds about 2× more than Muse Glimmer's 128K in a single prompt.
Gemma 4 26B A4B lists strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6) among its strengths; Muse Glimmer does not.
Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit
Muse Glimmer
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and it is the newer of the two.
Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine
Muse Glimmer
Muse Glimmer lists open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine among its strengths; Gemma 4 26B A4B does not.
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery
Muse Glimmer
Muse Glimmer lists agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery among its strengths; Gemma 4 26B A4B does not.
Lowest cost at scale
Muse Glimmer
Its weights are open, so at volume you pay for your own hardware instead of Gemma 4 26B A4B's $0.15/$0.6 per 1M tokens.
Largest single-prompt input
Gemma 4 26B A4B
Its 256K window is about 2× larger than Muse Glimmer's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Muse Glimmer
At Open weight (self-host / free) it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemma 4 26B A4B
Larger 256K window fits more in one prompt.
Anyone whose priority is fast, cheap inference from a sparse moe (3.8b active of 25.2b total)
→ Gemma 4 26B A4B
It is specifically built for that.
Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit
→ Muse Glimmer
That is its strongest area.
Gemma 4 26B A4B: where it fits
An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.
Its trade-offs are real: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
Muse Glimmer: where it fits
Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.
Its trade-offs: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Gemma 4 26B A4B and Muse Glimmer overlap enough that the right pick depends on your specific job. Muse Glimmer costs less per token; Gemma 4 26B A4B holds the larger context; and each leads in its own area — Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total), Muse Glimmer for runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Gemma 4 26B A4B and Muse Glimmer without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Gemma 4 26B A4B or Muse Glimmer better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total) while Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 26B A4B or Muse Glimmer?
Muse Glimmer is cheaper — $0.15/$0.6 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Gemma 4 26B A4B — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemma 4 26B A4B and Muse Glimmer together?
Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Muse Glimmer and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemma 4 26B A4B or Muse Glimmer?
Muse Glimmer — released August 10, 2026, about 4 months after Gemma 4 26B A4B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.