MiniMax M3.1 Flash vs Microsoft Phi-4

MiniMax · China  |  Microsoft · US · Updated June 2026

Quick verdict

Pick MiniMax M3.1 Flash for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length or 428b-parameter moe (about 23b active) with weights on hugging face from october 23, 2026. 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. On a tight budget at scale, Microsoft Phi-4 is the value pick.

MiniMax M3.1 Flash (MiniMax, China) and Microsoft Phi-4 (Microsoft, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. MiniMax M3.1 Flash is miniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. 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 and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMiniMax M3.1 FlashMicrosoft Phi-4
ProviderMiniMax (China) Microsoft (US)
ReleasedOctober 19, 2026 January 10, 2025
Context window1M tokens (~1,500 pages) 16K (~25 pages)
Price (in/out)$0.15/$0.6 per 1M tokens $0.07/$0.14 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

$0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

MiniMax M3.1 Flash

Its 1M tokens window holds about 61× more than Microsoft Phi-4's 16K in a single prompt.

428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it carries the larger 1M tokens context.

DeepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported)

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it is the newer of the two.

Strong reasoning for a small 14B open-weight model

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.

MIT-licensed — fully self-hostable at no per-token cost

Microsoft Phi-4

At $0.07/$0.14 per 1M tokens it undercuts MiniMax M3.1 Flash ($0.15/$0.6 per 1M tokens), and that gap compounds at volume.

Runs on modest or local hardware

Microsoft Phi-4

Microsoft Phi-4 lists runs on modest or local hardware among its strengths; MiniMax M3.1 Flash does not.

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

MiniMax M3.1 Flash

Its 1M tokens 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 MiniMax M3.1 Flash, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ MiniMax M3.1 Flash

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

→ MiniMax M3.1 Flash

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.

An enterprise with regional data-residency rules

→ Microsoft Phi-4 or MiniMax M3.1 Flash

Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

MiniMax M3.1 Flash: where it fits

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. Released October 19, 2026 by MiniMax, it is built for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, 428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026, deepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported), and artificial Analysis Intelligence Index of 42, 13 points above MiniMax M3.

Its trade-offs are real: thinking is always on and default effort is max, which inflates output-token cost, behind DeepSeek V4.1 Flash 1017 on Artificial Analysis's independent index, custom MiniMax Community license: attribution required, and products above $20M annual revenue need written authorization, and benchmarks are mostly self-reported; no SWE-bench Verified figure published. At $0.15 in / $0.6 out per million tokens, it sits in the budget 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

This is less "which is smarter" and more "which ecosystem fits." MiniMax M3.1 Flash (China) and Microsoft Phi-4 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Microsoft Phi-4 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both MiniMax M3.1 Flash 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 MiniMax M3.1 Flash 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, MiniMax M3.1 Flash leans toward $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length 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, MiniMax M3.1 Flash or Microsoft Phi-4?

Microsoft Phi-4 is cheaper — $0.15/$0.6 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 2.1× apart on input.

Which has the bigger context window?

MiniMax M3.1 Flash — 1M tokens vs 16K, about 61× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both MiniMax M3.1 Flash and Microsoft Phi-4 together?

Yes — a multi-model platform like LumiChats gives you MiniMax M3.1 Flash, 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, MiniMax M3.1 Flash or Microsoft Phi-4?

MiniMax M3.1 Flash — released October 19, 2026, about 22 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.