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 Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters or open weights under openmdw-1.1, shipped day one in bf16, fp8, nvfp4 and int4 across every major runtime. On a tight budget at scale, Laguna XS 2.1 is the value pick.
Gemma 4 26B A4B (Google) and Laguna XS 2.1 (Poolside) 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. Laguna XS 2.1 is a 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Price: Laguna XS 2.1 is about 1.5× cheaper on input ($0.1/$0.2 per 1M tokens vs $0.15/$0.6 per 1M tokens) — modest, but it adds up at steady volume.
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Laguna XS 2.1 is the newer model by about 3 months (released July 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemma 4 26B A4B
Laguna XS 2.1
Provider
Google (US)
Poolside (US)
Released
April 2, 2026
July 2, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
70.9%
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 — Gemma 4 26B A4B lists fast, cheap inference from a sparse MoE (3.8B active of 25.2B total) among its strengths; Laguna XS 2.1 does not.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost: Gemma 4 26B A4B — Gemma 4 26B A4B lists near-31B-dense quality at a fraction of the compute and memory-bandwidth cost among its strengths; Laguna XS 2.1 does not.
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; Laguna XS 2.1 does not.
Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters: Laguna XS 2.1 — Gemma 4 26B A4B is comparatively weak here — all 25.2B parameters must be loaded into memory even though only 3.8B are active per token
Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime: Laguna XS 2.1 — A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it runs cheaper at $0.1/$0.2 per 1M tokens.
Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens it undercuts Gemma 4 26B A4B ($0.15/$0.6 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume: Laguna XS 2.1 — At $0.1/$0.2 per 1M tokens it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
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 remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters: Laguna XS 2.1 — 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.
Laguna XS 2.1: where it fits
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Released July 2, 2026 by Poolside, it is built for remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters, open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime, cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price, and unusually transparent evaluation — it publishes its harness, step limits, and sandbox specs.
Its trade-offs: weeks old with no independent replication; every published score traces back to Poolside's own harness, the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case, and weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise. At $0.1 in / $0.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Gemma 4 26B A4B and Laguna XS 2.1 overlap enough that the right pick depends on your specific job. Laguna XS 2.1 costs less per token; 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), Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters. 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 Laguna XS 2.1 better for coding?
Public SWE-Bench figures are not available for Gemma 4 26B A4B, 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 Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 26B A4B or Laguna XS 2.1?
Laguna XS 2.1 is cheaper — $0.15/$0.6 per 1M tokens vs $0.1/$0.2 per 1M tokens, roughly 1.5× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemma 4 26B A4B and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Laguna XS 2.1 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 Laguna XS 2.1?
Laguna XS 2.1 — released July 2, 2026, about 3 months after Gemma 4 26B A4B.
Gemma 4 26B A4B vs Laguna XS 2.1
Google · US | Poolside · 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 Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters or open weights under openmdw-1.1, shipped day one in bf16, fp8, nvfp4 and int4 across every major runtime. On a tight budget at scale, Laguna XS 2.1 is the value pick.
Gemma 4 26B A4B (Google) and Laguna XS 2.1 (Poolside) 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. Laguna XS 2.1 is a 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Price: Laguna XS 2.1 is about 1.5× cheaper on input ($0.1/$0.2 per 1M tokens vs $0.15/$0.6 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Laguna XS 2.1 is the newer model by about 3 months (released July 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemma 4 26B A4B
Laguna XS 2.1
Provider
Google (US)
Poolside (US)
Released
April 2, 2026
July 2, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
$0.1/$0.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
70.9%
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
Gemma 4 26B A4B lists fast, cheap inference from a sparse MoE (3.8B active of 25.2B total) among its strengths; Laguna XS 2.1 does not.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost
Gemma 4 26B A4B
Gemma 4 26B A4B lists near-31B-dense quality at a fraction of the compute and memory-bandwidth cost among its strengths; Laguna XS 2.1 does not.
Gemma 4 26B A4B lists strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6) among its strengths; Laguna XS 2.1 does not.
Remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters
Laguna XS 2.1
Gemma 4 26B A4B is comparatively weak here — all 25.2B parameters must be loaded into memory even though only 3.8B are active per token
Open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime
Laguna XS 2.1
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven — and it runs cheaper at $0.1/$0.2 per 1M tokens.
Cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price
Laguna XS 2.1
At $0.1/$0.2 per 1M tokens it undercuts Gemma 4 26B A4B ($0.15/$0.6 per 1M tokens), and that gap compounds at volume.
Lowest cost at scale
Laguna XS 2.1
At $0.1/$0.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Laguna XS 2.1
At $0.1/$0.2 per 1M tokens it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
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 remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters
→ Laguna XS 2.1
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.
Laguna XS 2.1: where it fits
A 33B open-weight coding MoE running on 3B active parameters — 70.9% SWE-Bench Verified and very cheap, but unproven. Released July 2, 2026 by Poolside, it is built for remarkable efficiency — 70.9% on SWE-Bench Verified from only 3B active parameters, open weights under OpenMDW-1.1, shipped day one in BF16, FP8, NVFP4 and INT4 across every major runtime, cheap even on the paid tier, at roughly a sixth of GLM 4.7's input price, and unusually transparent evaluation — it publishes its harness, step limits, and sandbox specs.
Its trade-offs: weeks old with no independent replication; every published score traces back to Poolside's own harness, the free endpoint trains on your inputs and outputs — disqualifying for proprietary code, which is its main use case, and weak on harder agentic work (37.5 on Terminal-Bench 2.0), and its gain over XS.2 is barely above noise. At $0.1 in / $0.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Gemma 4 26B A4B and Laguna XS 2.1 overlap enough that the right pick depends on your specific job. Laguna XS 2.1 costs less per token; 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), Laguna XS 2.1 for remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Gemma 4 26B A4B and Laguna XS 2.1 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 Laguna XS 2.1 better for coding?
Public SWE-Bench figures are not available for Gemma 4 26B A4B, 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 Laguna XS 2.1 leans toward remarkable efficiency — 70.9% on swe-bench verified from only 3b active parameters, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 26B A4B or Laguna XS 2.1?
Laguna XS 2.1 is cheaper — $0.15/$0.6 per 1M tokens vs $0.1/$0.2 per 1M tokens, roughly 1.5× apart on input.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemma 4 26B A4B and Laguna XS 2.1 together?
Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Laguna XS 2.1 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 Laguna XS 2.1?
Laguna XS 2.1 — released July 2, 2026, about 3 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.