Gemma 4 26B A4B vs Qwen3.8 Max Prime
Google · US | Alibaba · China · 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 Qwen3.8 Max Prime for same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving or alibaba advertises 1.5-2x higher output throughput than standard qwen3.8 max for latency-sensitive workloads. Choose Gemma 4 26B A4B if you need self-hosting or data privacy; Qwen3.8 Max Prime if you want a managed API.
Gemma 4 26B A4B (Google, US) and Qwen3.8 Max Prime (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3.8 Max Prime is alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Gemma 4 26B A4B is about 33× cheaper on input ($0.12/$0.37 per 1M tokens vs $4/$12 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: Qwen3.8 Max Prime holds 4× more — 1M tokens (~1,573 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Qwen3.8 Max Prime is the newer model by about 6 months (released September 23, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Gemma 4 26B A4B | Qwen3.8 Max Prime |
|---|---|---|
| Provider | Google (US) | Alibaba (China) |
| Released | April 2, 2026 | September 23, 2026 |
| Context window | 256K (~393 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $0.12/$0.37 per 1M tokens | $4/$12 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, image, video, code | text, image, video, 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
At $0.12/$0.37 per 1M tokens it undercuts Qwen3.8 Max Prime ($4/$12 per 1M tokens), and that gap compounds at volume.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost
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 runs cheaper at $0.12/$0.37 per 1M tokens.
Strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6)
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 its weights are open while Qwen3.8 Max Prime is API-only.
Same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving
Qwen3.8 Max Prime
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
Alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads
Qwen3.8 Max Prime
Alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed — and it carries the larger 1M tokens context.
1M-token context window with up to 131,072 output tokens, reasoning enabled by default
Qwen3.8 Max Prime
Its 1M tokens window holds about 4× more than Gemma 4 26B A4B's 256K in a single prompt.
Lowest cost at scale
Gemma 4 26B A4B
At $0.12/$0.37 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Qwen3.8 Max Prime
Its 1M tokens window is about 4× larger than Gemma 4 26B A4B's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Gemma 4 26B A4B
At $0.12/$0.37 per 1M tokens it undercuts Qwen3.8 Max Prime, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen3.8 Max Prime
Larger 1M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Gemma 4 26B A4B
Open weights let you run it on your own hardware; Qwen3.8 Max Prime is API-only.
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 same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving
→ Qwen3.8 Max Prime
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemma 4 26B A4B or Qwen3.8 Max Prime
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 $0.12 in / $0.37 out per million tokens, it sits in the budget price band.
Qwen3.8 Max Prime: where it fits
Alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. Released September 23, 2026 by Alibaba, it is built for same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving, alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads, 1M-token context window with up to 131,072 output tokens, reasoning enabled by default, and accepts text, image, and video input alongside text output, inherited unchanged from the base model.
Its trade-offs: priced exactly double standard Qwen3.8 Max ($4/$12 vs $2/$6) for a throughput claim the only public measurements available don't show as measurably faster, no official Alibaba announcement — Alibaba Cloud's Model Studio documentation still lists only qwen3.8-max and its September 2 snapshot, suggesting minimal official backing, like the base model, open weights have been promised but not shipped — closed API only, and purely a speed tier, not a capability upgrade — same benchmark ceiling as the cheaper base model. At $4 in / $12 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
The defining split here is open vs. closed. Gemma 4 26B A4B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Qwen3.8 Max Prime gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemma 4 26B A4B and Qwen3.8 Max Prime 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.
See pricingFrequently asked questions
Is Gemma 4 26B A4B or Qwen3.8 Max Prime 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 Qwen3.8 Max Prime leans toward same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 26B A4B or Qwen3.8 Max Prime?
Gemma 4 26B A4B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen3.8 Max Prime is API-metered at $4/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Qwen3.8 Max Prime — 1M tokens vs 256K, about 4× 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 Qwen3.8 Max Prime together?
Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, Qwen3.8 Max Prime 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 Qwen3.8 Max Prime?
Qwen3.8 Max Prime — released September 23, 2026, about 6 months after Gemma 4 26B A4B.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.