Pick Gemini 2.5 Pro for 1m context via api or strong multimodal reasoning. Pick Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. Choose Microsoft Phi-4 if you need self-hosting or data privacy; Gemini 2.5 Pro if you want a managed API.
Gemini 2.5 Pro (Google) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Pro is google's previous-gen 2M flagship — still a strong long-context multimodal option. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Microsoft Phi-4 is about 18× cheaper on input ($0.07/$0.14 per 1M tokens vs $1.25/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Gemini 2.5 Pro holds 61× more — 1M (~1,500 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: Gemini 2.5 Pro is the newer model by about 5 months (released June 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 2.5 Pro
Microsoft Phi-4
Provider
Google (US)
Microsoft (US)
Released
June 2025
January 10, 2025
Context window
1M (~1,500 pages)
16K (~25 pages)
Price (in/out)
$1.25/$10 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
1M context via API: Gemini 2.5 Pro — Its 1M window holds about 61× more than Microsoft Phi-4's 16K in a single prompt.
Strong multimodal reasoning: Gemini 2.5 Pro — Google's previous-gen 2M flagship — still a strong long-context multimodal option — and it carries the larger 1M context.
Science and maths benchmarks: Gemini 2.5 Pro — Google's previous-gen 2M flagship — still a strong long-context multimodal option — 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 — Gemini 2.5 Pro is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts Gemini 2.5 Pro ($1.25/$10 per 1M tokens), and that gap compounds at volume.
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 it runs cheaper at $0.07/$0.14 per 1M tokens.
Lowest cost at scale: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 2.5 Pro — Its 1M window is about 61× larger than Microsoft Phi-4's 16K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts Gemini 2.5 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 2.5 Pro — Larger 1M 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; Gemini 2.5 Pro is API-only.
Anyone whose priority is 1m context via api: Gemini 2.5 Pro — 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.
Gemini 2.5 Pro: where it fits
Google's previous-gen 2M flagship — still a strong long-context multimodal option. Released June 2025 by Google, it is built for 1M context via API, strong multimodal reasoning, science and maths benchmarks, and whole-book and video analysis.
Its trade-offs are real: superseded by 3.x for newest features, and recall degrades on very long inputs. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
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
The defining split here is open vs. closed. Microsoft Phi-4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 2.5 Pro 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 Gemini 2.5 Pro 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, Gemini 2.5 Pro leans toward 1m context via api 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, Gemini 2.5 Pro or Microsoft Phi-4?
Microsoft Phi-4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 2.5 Pro is API-metered at $1.25/$10 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?
Gemini 2.5 Pro — 1M vs 16K, about 61× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 2.5 Pro and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Pro, Microsoft Phi-4 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, Gemini 2.5 Pro or Microsoft Phi-4?
Gemini 2.5 Pro — released June 2025, about 5 months after Microsoft Phi-4.
Gemini 2.5 Pro vs Microsoft Phi-4
Google · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Gemini 2.5 Pro for 1m context via api or strong multimodal reasoning. Pick Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. Choose Microsoft Phi-4 if you need self-hosting or data privacy; Gemini 2.5 Pro if you want a managed API.
Gemini 2.5 Pro (Google) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Pro is google's previous-gen 2M flagship — still a strong long-context multimodal option. 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. 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: Microsoft Phi-4 is about 18× cheaper on input ($0.07/$0.14 per 1M tokens vs $1.25/$10 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Gemini 2.5 Pro holds 61× more — 1M (~1,500 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: Gemini 2.5 Pro is the newer model by about 5 months (released June 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 2.5 Pro
Microsoft Phi-4
Provider
Google (US)
Microsoft (US)
Released
June 2025
January 10, 2025
Context window
1M (~1,500 pages)
16K (~25 pages)
Price (in/out)
$1.25/$10 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
1M context via API
Gemini 2.5 Pro
Its 1M window holds about 61× more than Microsoft Phi-4's 16K in a single prompt.
Strong multimodal reasoning
Gemini 2.5 Pro
Google's previous-gen 2M flagship — still a strong long-context multimodal option — and it carries the larger 1M context.
Science and maths benchmarks
Gemini 2.5 Pro
Google's previous-gen 2M flagship — still a strong long-context multimodal option — 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 — Gemini 2.5 Pro is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts Gemini 2.5 Pro ($1.25/$10 per 1M tokens), and that gap compounds at volume.
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 it runs cheaper at $0.07/$0.14 per 1M tokens.
Lowest cost at scale
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 2.5 Pro
Its 1M window is about 61× larger than Microsoft Phi-4's 16K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts Gemini 2.5 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 2.5 Pro
Larger 1M 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; Gemini 2.5 Pro is API-only.
Anyone whose priority is 1m context via api
→ Gemini 2.5 Pro
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.
Gemini 2.5 Pro: where it fits
Google's previous-gen 2M flagship — still a strong long-context multimodal option. Released June 2025 by Google, it is built for 1M context via API, strong multimodal reasoning, science and maths benchmarks, and whole-book and video analysis.
Its trade-offs are real: superseded by 3.x for newest features, and recall degrades on very long inputs. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
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
The defining split here is open vs. closed. Microsoft Phi-4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 2.5 Pro 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 Gemini 2.5 Pro 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 Gemini 2.5 Pro 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, Gemini 2.5 Pro leans toward 1m context via api 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, Gemini 2.5 Pro or Microsoft Phi-4?
Microsoft Phi-4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 2.5 Pro is API-metered at $1.25/$10 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?
Gemini 2.5 Pro — 1M vs 16K, about 61× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 2.5 Pro and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Pro, Microsoft Phi-4 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, Gemini 2.5 Pro or Microsoft Phi-4?
Gemini 2.5 Pro — released June 2025, about 5 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.