Pick IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed or efficient hybrid mamba-2/transformer design - much lower memory and faster inference. 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 IBM Granite 4.1 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
IBM Granite 4.1 (IBM) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. IBM Granite 4.1 is iBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. 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: IBM Granite 4.1 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: IBM Granite 4.1 holds 4× more — 512K (~768 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: IBM Granite 4.1 is the newer model by about 8 months (released April 29, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
IBM Granite 4.1
MAI-1-preview
Provider
IBM (US)
Microsoft (US)
Released
April 29, 2026
August 28, 2025
Context window
512K (~768 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed: IBM Granite 4.1 — Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
Efficient hybrid Mamba-2/transformer design - much lower memory and faster inference: IBM Granite 4.1 — Its 512K window holds about 4× more than MAI-1-preview's 128K in a single prompt.
512K-token context on small, deployable dense models (3B/8B/30B): IBM Granite 4.1 — MAI-1-preview is comparatively weak here — no public per-token API pricing - not sold as a standalone product
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI: MAI-1-preview — IBM Granite 4.1 is comparatively weak here — best as a workhorse; reasoning-heavy tasks favor larger models
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; IBM Granite 4.1 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; IBM Granite 4.1 does not.
Largest single-prompt input: IBM Granite 4.1 — Its 512K window is about 4× larger than MAI-1-preview's 128K, fitting roughly 768 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: IBM Granite 4.1 — Larger 512K window fits more in one prompt.
A team with data-privacy or self-hosting needs: IBM Granite 4.1 — Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed: IBM Granite 4.1 — 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.
IBM Granite 4.1: where it fits
IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. Released April 29, 2026 by IBM, it is built for enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed, efficient hybrid Mamba-2/transformer design - much lower memory and faster inference, 512K-token context on small, deployable dense models (3B/8B/30B), and free to self-host; governance-friendly for on-prem and regulated deployments.
Its trade-offs are real: not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores, best as a workhorse; reasoning-heavy tasks favor larger models, instruct models are text-focused (vision and speech are separate family members), and efficiency and performance claims are IBM's own. 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. IBM Granite 4.1 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 IBM Granite 4.1 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, IBM Granite 4.1 leans toward enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed 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, IBM Granite 4.1 or MAI-1-preview?
IBM Granite 4.1 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?
IBM Granite 4.1 — 512K vs 128K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both IBM Granite 4.1 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you IBM Granite 4.1, 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, IBM Granite 4.1 or MAI-1-preview?
IBM Granite 4.1 — released April 29, 2026, about 8 months after MAI-1-preview.
IBM Granite 4.1 vs MAI-1-preview
IBM · US | Microsoft · US · Updated June 2026
Quick verdict
Pick IBM Granite 4.1 for enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed or efficient hybrid mamba-2/transformer design - much lower memory and faster inference. 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 IBM Granite 4.1 if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.
IBM Granite 4.1 (IBM) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. IBM Granite 4.1 is iBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. 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: IBM Granite 4.1 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: IBM Granite 4.1 holds 4× more — 512K (~768 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: IBM Granite 4.1 is the newer model by about 8 months (released April 29, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
IBM Granite 4.1
MAI-1-preview
Provider
IBM (US)
Microsoft (US)
Released
April 29, 2026
August 28, 2025
Context window
512K (~768 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed
IBM Granite 4.1
Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
Efficient hybrid Mamba-2/transformer design - much lower memory and faster inference
IBM Granite 4.1
Its 512K window holds about 4× more than MAI-1-preview's 128K in a single prompt.
512K-token context on small, deployable dense models (3B/8B/30B)
IBM Granite 4.1
MAI-1-preview is comparatively weak here — no public per-token API pricing - not sold as a standalone product
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI
MAI-1-preview
IBM Granite 4.1 is comparatively weak here — best as a workhorse; reasoning-heavy tasks favor larger models
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; IBM Granite 4.1 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; IBM Granite 4.1 does not.
Largest single-prompt input
IBM Granite 4.1
Its 512K window is about 4× larger than MAI-1-preview's 128K, fitting roughly 768 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ IBM Granite 4.1
Larger 512K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ IBM Granite 4.1
Open weights let you run it on your own hardware; MAI-1-preview is API-only.
Anyone whose priority is enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed
→ IBM Granite 4.1
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.
IBM Granite 4.1: where it fits
IBM's most complete open-weight enterprise release - small, Apache-2.0, long-context and governance-friendly, built to deploy efficiently rather than top the leaderboard. Released April 29, 2026 by IBM, it is built for enterprise-grade open weights - Apache 2.0, ISO 42001-certified, cryptographically signed, efficient hybrid Mamba-2/transformer design - much lower memory and faster inference, 512K-token context on small, deployable dense models (3B/8B/30B), and free to self-host; governance-friendly for on-prem and regulated deployments.
Its trade-offs are real: not a frontier-intelligence competitor - built for efficient deployment, not top benchmark scores, best as a workhorse; reasoning-heavy tasks favor larger models, instruct models are text-focused (vision and speech are separate family members), and efficiency and performance claims are IBM's own. 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. IBM Granite 4.1 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 IBM Granite 4.1 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.
Is IBM Granite 4.1 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, IBM Granite 4.1 leans toward enterprise-grade open weights - apache 2.0, iso 42001-certified, cryptographically signed 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, IBM Granite 4.1 or MAI-1-preview?
IBM Granite 4.1 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?
IBM Granite 4.1 — 512K vs 128K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both IBM Granite 4.1 and MAI-1-preview together?
Yes — a multi-model platform like LumiChats gives you IBM Granite 4.1, 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, IBM Granite 4.1 or MAI-1-preview?
IBM Granite 4.1 — released April 29, 2026, about 8 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.