Both are Microsoft models. MAI-1-preview is the newer, generally stronger default; reach for Microsoft Phi-4 when a specific cost or latency profile matters more than the latest capabilities.
MAI-1-preview and Microsoft Phi-4 are both Microsoft models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Cost model: Microsoft Phi-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: MAI-1-preview holds 7.8× more — 128K (~192 pages) vs 16K (~25 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: MAI-1-preview is the newer model by about 8 months (released August 28, 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
MAI-1-preview
Microsoft Phi-4
Provider
Microsoft (US)
Microsoft (US)
Released
August 28, 2025
January 10, 2025
Context window
128K (~192 pages)
16K (~25 pages)
Price (in/out)
Not published
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI: MAI-1-preview — Microsoft Phi-4 is comparatively weak here — an early-2025 small model, outclassed on hard tasks by 2026 flagships
Ranked in the top 15 on LM Arena at launch: MAI-1-preview — Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs — and it carries the larger 128K context.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment: MAI-1-preview — Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — MAI-1-preview is comparatively weak here — no public per-token API pricing - not sold as a standalone product
Runs on modest or local hardware: Microsoft Phi-4 — Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only — and its weights are open while MAI-1-preview is API-only.
Lowest cost at scale: MAI-1-preview — Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input: MAI-1-preview — Its 128K window is about 7.8× larger than Microsoft Phi-4's 16K, fitting roughly 192 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MAI-1-preview — At Not published it undercuts Microsoft Phi-4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MAI-1-preview — Larger 128K window fits more in one prompt.
A team with data-privacy or self-hosting needs: Microsoft Phi-4 — Open weights let you run it on your own hardware; MAI-1-preview is API-only.
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 — It is specifically built for that.
Anyone whose priority is strong reasoning for a small 14b open-weight model: Microsoft Phi-4 — That is its strongest area.
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 are real: 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because MAI-1-preview and Microsoft Phi-4 come from the same lab (Microsoft), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. MAI-1-preview is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to MAI-1-preview and drop down only with a concrete reason.
Frequently asked questions
Is MAI-1-preview or Microsoft Phi-4 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, MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai while Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MAI-1-preview or Microsoft Phi-4?
Microsoft Phi-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?
MAI-1-preview — 128K vs 16K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Microsoft Phi-4 to MAI-1-preview?
Since both are Microsoft models, the newer one (MAI-1-preview) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, MAI-1-preview or Microsoft Phi-4?
MAI-1-preview — released August 28, 2025, about 8 months after Microsoft Phi-4.
MAI-1-preview vs Microsoft Phi-4
Microsoft · US | Microsoft · US · Updated June 2026
Quick verdict
Both are Microsoft models. MAI-1-preview is the newer, generally stronger default; reach for Microsoft Phi-4 when a specific cost or latency profile matters more than the latest capabilities.
MAI-1-preview and Microsoft Phi-4 are both Microsoft models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Cost model: Microsoft Phi-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: MAI-1-preview holds 7.8× more — 128K (~192 pages) vs 16K (~25 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: MAI-1-preview is the newer model by about 8 months (released August 28, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
MAI-1-preview
Microsoft Phi-4
Provider
Microsoft (US)
Microsoft (US)
Released
August 28, 2025
January 10, 2025
Context window
128K (~192 pages)
16K (~25 pages)
Price (in/out)
Not published
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI
MAI-1-preview
Microsoft Phi-4 is comparatively weak here — an early-2025 small model, outclassed on hard tasks by 2026 flagships
Ranked in the top 15 on LM Arena at launch
MAI-1-preview
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs — and it carries the larger 128K context.
Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment
MAI-1-preview
Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Open weights make this possible at all — MAI-1-preview is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
MAI-1-preview is comparatively weak here — no public per-token API pricing - not sold as a standalone product
Runs on modest or local hardware
Microsoft Phi-4
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only — and its weights are open while MAI-1-preview is API-only.
Lowest cost at scale
MAI-1-preview
Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input
MAI-1-preview
Its 128K window is about 7.8× larger than Microsoft Phi-4's 16K, fitting roughly 192 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-1-preview
At Not published it undercuts Microsoft Phi-4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MAI-1-preview
Larger 128K window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Microsoft Phi-4
Open weights let you run it on your own hardware; MAI-1-preview is API-only.
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
It is specifically built for that.
Anyone whose priority is strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
That is its strongest area.
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 are real: 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because MAI-1-preview and Microsoft Phi-4 come from the same lab (Microsoft), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. MAI-1-preview is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to MAI-1-preview and drop down only with a concrete reason.
Want both MAI-1-preview and Microsoft Phi-4 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 MAI-1-preview or Microsoft Phi-4 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, MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai while Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MAI-1-preview or Microsoft Phi-4?
Microsoft Phi-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?
MAI-1-preview — 128K vs 16K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Microsoft Phi-4 to MAI-1-preview?
Since both are Microsoft models, the newer one (MAI-1-preview) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, MAI-1-preview or Microsoft Phi-4?
MAI-1-preview — released August 28, 2025, about 8 months after Microsoft Phi-4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.