Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose Gemma 4 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
Gemma 4 (Google) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-1-preview is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Gemma 4 holds 2× more — 256K (~384 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Gemma 4 is the newer model by about 7 months (released April 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemma 4
MAI-1-preview
Provider
Google (US)
Microsoft (US)
Released
April 2, 2026
August 28, 2025
Context window
256K (~384 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment: Gemma 4 — Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
Running locally or on edge devices: Gemma 4 — Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting — and it carries the larger 256K context.
Fine-tuning on your own data: Gemma 4 — Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting — and its weights are open while MAI-1-preview is API-only.
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI: MAI-1-preview — Gemma 4 is comparatively weak here — trails frontier closed models on the hardest tasks
Ranked in the top 15 on LM Arena at launch: MAI-1-preview — MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; Gemma 4 does not.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment: MAI-1-preview — MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Gemma 4 does not.
Largest single-prompt input: Gemma 4 — Its 256K window is about 2× larger than MAI-1-preview's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Gemma 4 — Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs: Gemma 4 — Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is self-hosted, data-private deployment: Gemma 4 — It is specifically built for that.
Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai: MAI-1-preview — That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MAI-1-preview: where it fits
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.
Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.
The bottom line for this matchup
The defining split here is open vs. closed. Gemma 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview 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.
Frequently asked questions
Is Gemma 4 or MAI-1-preview 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 leans toward self-hosted, data-private deployment while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or MAI-1-preview?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. 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?
Gemma 4 — 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 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, MAI-1-preview 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 or MAI-1-preview?
Gemma 4 — released April 2, 2026, about 7 months after MAI-1-preview.
Gemma 4 vs MAI-1-preview
Google · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose Gemma 4 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
Gemma 4 (Google) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Gemma 4 ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-1-preview is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Gemma 4 holds 2× more — 256K (~384 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Gemma 4 is the newer model by about 7 months (released April 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemma 4
MAI-1-preview
Provider
Google (US)
Microsoft (US)
Released
April 2, 2026
August 28, 2025
Context window
256K (~384 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Self-hosted, data-private deployment
Gemma 4
Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
Running locally or on edge devices
Gemma 4
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting — and it carries the larger 256K context.
Fine-tuning on your own data
Gemma 4
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting — and its weights are open while MAI-1-preview is API-only.
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI
MAI-1-preview
Gemma 4 is comparatively weak here — trails frontier closed models on the hardest tasks
Ranked in the top 15 on LM Arena at launch
MAI-1-preview
MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; Gemma 4 does not.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment
MAI-1-preview
MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Gemma 4 does not.
Largest single-prompt input
Gemma 4
Its 256K window is about 2× larger than MAI-1-preview's 128K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Gemma 4
Larger 256K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Gemma 4
Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is self-hosted, data-private deployment
→ Gemma 4
It is specifically built for that.
Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai
→ MAI-1-preview
That is its strongest area.
Gemma 4: where it fits
Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.
Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
MAI-1-preview: where it fits
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.
Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.
The bottom line for this matchup
The defining split here is open vs. closed. Gemma 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview 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 and MAI-1-preview 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.
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 leans toward self-hosted, data-private deployment while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemma 4 or MAI-1-preview?
Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. 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?
Gemma 4 — 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 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you Gemma 4, MAI-1-preview 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 or MAI-1-preview?
Gemma 4 — released April 2, 2026, about 7 months after MAI-1-preview.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.