Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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 (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 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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
Price: Microsoft Phi-4 is about 4.3× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.3/$1.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: MiniMax M3 holds 64× more — 1M (~1,573 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: MiniMax M3 is the newer model by about 17 months (released June 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
MiniMax M3
Microsoft Phi-4
Provider
MiniMax (China)
Microsoft (US)
Released
June 2026
January 10, 2025
Context window
1M (~1,573 pages)
16K (~25 pages)
Price (in/out)
$0.3/$1.2 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight 428B MoE (~23B active per token) with a 1M-token context: MiniMax M3 — Its 1M window holds about 64× more than Microsoft Phi-4's 16K in a single prompt.
Native multimodal input — text, image and video: MiniMax M3 — Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5: MiniMax M3 — MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it carries the larger 1M context.
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 ($0.3/$1.2 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 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 — Its 1M 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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiniMax M3 — Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context: MiniMax M3 — 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 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released June 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.3 in / $1.2 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 (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.
Frequently asked questions
Is MiniMax M3 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 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.3/$1.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 4.3× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 16K, about 64× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M3 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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 or Microsoft Phi-4?
MiniMax M3 — released June 2026, about 17 months after Microsoft Phi-4.
MiniMax M3 vs Microsoft Phi-4
MiniMax · China | Microsoft · US · Updated June 2026
Quick verdict
Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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 (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 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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
▸Price: Microsoft Phi-4 is about 4.3× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.3/$1.2 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: MiniMax M3 holds 64× more — 1M (~1,573 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: MiniMax M3 is the newer model by about 17 months (released June 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
MiniMax M3
Microsoft Phi-4
Provider
MiniMax (China)
Microsoft (US)
Released
June 2026
January 10, 2025
Context window
1M (~1,573 pages)
16K (~25 pages)
Price (in/out)
$0.3/$1.2 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
Its 1M window holds about 64× more than Microsoft Phi-4's 16K in a single prompt.
Native multimodal input — text, image and video
MiniMax M3
Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it carries the larger 1M context.
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 ($0.3/$1.2 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 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
Its 1M 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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
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
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released June 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.3 in / $1.2 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 (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 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 MiniMax M3 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 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.3/$1.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 4.3× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 16K, about 64× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M3 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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 or Microsoft Phi-4?
MiniMax M3 — released June 2026, about 17 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.