Gemini 4 Argon vs Microsoft Phi-4

Google · US  |  Microsoft · US · Updated June 2026

Quick verdict

Pick Gemini 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses or google-reported deepswe v1.1 of 77.9% and lvbench long-video understanding of 91.7%. 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 4 Argon if you want a managed API.

Gemini 4 Argon (Google) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Gemini 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). 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

Side-by-side specs

SpecGemini 4 ArgonMicrosoft Phi-4
ProviderGoogle (US) Microsoft (US)
ReleasedOctober 13, 2026 January 10, 2025
Context window1M tokens (~1,573 pages) 16K (~25 pages)
Price (in/out)$2/$10 per 1M tokens $0.07/$0.14 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image, audio, video, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

Gemini 4 Argon

Its 1M tokens window holds about 64× more than Microsoft Phi-4's 16K in a single prompt.

Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%

Gemini 4 Argon

Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input

Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)

Gemini 4 Argon

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1) — and it carries the larger 1M tokens context.

Strong reasoning for a small 14B open-weight model

Microsoft Phi-4

Open weights make this possible at all — Gemini 4 Argon 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 4 Argon ($2/$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 4 Argon

Its 1M tokens window is about 64× larger than Microsoft Phi-4's 16K, fitting roughly 1,573 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 4 Argon, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Gemini 4 Argon

Larger 1M tokens 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 4 Argon is API-only.

Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

→ Gemini 4 Argon

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 4 Argon: where it fits

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).

Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 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 4 Argon 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 4 Argon 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.

See pricing

Frequently asked questions

Is Gemini 4 Argon 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 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses 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 4 Argon 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 4 Argon is API-metered at $2/$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 4 Argon — 1M tokens vs 16K, about 64× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Gemini 4 Argon and Microsoft Phi-4 together?

Yes — a multi-model platform like LumiChats gives you Gemini 4 Argon, 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 4 Argon or Microsoft Phi-4?

Gemini 4 Argon — released October 13, 2026, about 21 months after Microsoft Phi-4.

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.