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. Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B is the value pick.
Microsoft Phi-4 (Microsoft, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Qwen3 235B A22B holds 16× more — 256K (~393 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: Qwen3 235B A22B is the newer model by about 6 months (released July 21, 2025), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Microsoft Phi-4
Qwen3 235B A22B
Provider
Microsoft (US)
Alibaba (China)
Released
January 10, 2025
July 21, 2025
Context window
16K (~25 pages)
256K (~393 pages)
Price (in/out)
$0.07/$0.14 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
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
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — Microsoft Phi-4 lists mIT-licensed — fully self-hostable at no per-token cost among its strengths; Qwen3 235B A22B does not.
Runs on modest or local hardware: Microsoft Phi-4 — Qwen3 235B A22B is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B — An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it carries the larger 256K context.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B — An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it is the newer of the two.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B — Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Microsoft Phi-4 does not.
Lowest cost at scale: Qwen3 235B A22B — 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: Qwen3 235B A22B — Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3 235B A22B — At Open weight (self-host / free) it undercuts Microsoft Phi-4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen3 235B A22B — Larger 256K window fits more in one prompt.
Anyone whose priority is strong reasoning for a small 14b open-weight model: Microsoft Phi-4 — It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B — That is its strongest area.
An enterprise with regional data-residency rules: Microsoft Phi-4 or Qwen3 235B A22B — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Microsoft Phi-4 (US) and Qwen3 235B A22B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Qwen3 235B A22B 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 Microsoft Phi-4 or Qwen3 235B A22B 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, Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Microsoft Phi-4 or Qwen3 235B A22B?
Qwen3 235B A22B is cheaper — $0.07/$0.14 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3 235B A22B — 256K vs 16K, about 16× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Microsoft Phi-4 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you Microsoft Phi-4, Qwen3 235B A22B 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, Microsoft Phi-4 or Qwen3 235B A22B?
Qwen3 235B A22B — released July 21, 2025, about 6 months after Microsoft Phi-4.
Microsoft Phi-4 vs Qwen3 235B A22B
Microsoft · US | Alibaba · China · Updated June 2026
Quick verdict
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. Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B is the value pick.
Microsoft Phi-4 (Microsoft, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Qwen3 235B A22B holds 16× more — 256K (~393 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: Qwen3 235B A22B is the newer model by about 6 months (released July 21, 2025), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Microsoft Phi-4
Qwen3 235B A22B
Provider
Microsoft (US)
Alibaba (China)
Released
January 10, 2025
July 21, 2025
Context window
16K (~25 pages)
256K (~393 pages)
Price (in/out)
$0.07/$0.14 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
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
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Qwen3 235B A22B is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
Microsoft Phi-4 lists mIT-licensed — fully self-hostable at no per-token cost among its strengths; Qwen3 235B A22B does not.
Runs on modest or local hardware
Microsoft Phi-4
Qwen3 235B A22B is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it carries the larger 256K context.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and it is the newer of the two.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B
Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Microsoft Phi-4 does not.
Lowest cost at scale
Qwen3 235B A22B
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
Qwen3 235B A22B
Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3 235B A22B
At Open weight (self-host / free) it undercuts Microsoft Phi-4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen3 235B A22B
Larger 256K window fits more in one prompt.
Anyone whose priority is strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B
That is its strongest area.
An enterprise with regional data-residency rules
→ Microsoft Phi-4 or Qwen3 235B A22B
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Microsoft Phi-4 (US) and Qwen3 235B A22B (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Qwen3 235B A22B 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 Microsoft Phi-4 and Qwen3 235B A22B 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 Microsoft Phi-4 or Qwen3 235B A22B 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, Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Microsoft Phi-4 or Qwen3 235B A22B?
Qwen3 235B A22B is cheaper — $0.07/$0.14 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3 235B A22B — 256K vs 16K, about 16× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Microsoft Phi-4 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you Microsoft Phi-4, Qwen3 235B A22B 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, Microsoft Phi-4 or Qwen3 235B A22B?
Qwen3 235B A22B — released July 21, 2025, about 6 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.