Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. 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, Hunyuan Hy3 is the value pick.
Hunyuan Hy3 (Tencent, 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. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. 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
Context window: Hunyuan Hy3 holds 16× more — 256K (~384 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: Hunyuan Hy3 is the newer model by about 18 months (released July 6, 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
Hunyuan Hy3
Microsoft Phi-4
Provider
Tencent (China)
Microsoft (US)
Released
July 6, 2026
January 10, 2025
Context window
256K (~384 pages)
16K (~25 pages)
Price (in/out)
Open weight (self-host / free)
$0.07/$0.14 per 1M tokens
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
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token: Hunyuan Hy3 — Its 256K window holds about 16× more than Microsoft Phi-4's 16K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out: Hunyuan Hy3 — A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Microsoft Phi-4 lists strong reasoning for a small 14B open-weight model among its strengths; Hunyuan Hy3 does not.
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; Hunyuan Hy3 does not.
Runs on modest or local hardware: Microsoft Phi-4 — Hunyuan Hy3 is comparatively weak here — the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware
Lowest cost at scale: Hunyuan Hy3 — 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: Hunyuan Hy3 — Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Hunyuan Hy3 — 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: Hunyuan Hy3 — Larger 256K window fits more in one prompt.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — 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 Hunyuan Hy3 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs are real: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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." Hunyuan Hy3 (China) and Microsoft Phi-4 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Hunyuan Hy3 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 Hunyuan Hy3 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, Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost 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, Hunyuan Hy3 or Microsoft Phi-4?
Hunyuan Hy3 is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Hunyuan Hy3 — 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 Hunyuan Hy3 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, 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, Hunyuan Hy3 or Microsoft Phi-4?
Hunyuan Hy3 — released July 6, 2026, about 18 months after Microsoft Phi-4.
Hunyuan Hy3 vs Microsoft Phi-4
Tencent · China | Microsoft · US · Updated June 2026
Quick verdict
Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. 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, Hunyuan Hy3 is the value pick.
Hunyuan Hy3 (Tencent, 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. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. 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
▸Context window: Hunyuan Hy3 holds 16× more — 256K (~384 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: Hunyuan Hy3 is the newer model by about 18 months (released July 6, 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
Hunyuan Hy3
Microsoft Phi-4
Provider
Tencent (China)
Microsoft (US)
Released
July 6, 2026
January 10, 2025
Context window
256K (~384 pages)
16K (~25 pages)
Price (in/out)
Open weight (self-host / free)
$0.07/$0.14 per 1M tokens
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
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost
Hunyuan Hy3
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
Its 256K window holds about 16× more than Microsoft Phi-4's 16K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Microsoft Phi-4 lists strong reasoning for a small 14B open-weight model among its strengths; Hunyuan Hy3 does not.
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; Hunyuan Hy3 does not.
Runs on modest or local hardware
Microsoft Phi-4
Hunyuan Hy3 is comparatively weak here — the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware
Lowest cost at scale
Hunyuan Hy3
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
Hunyuan Hy3
Its 256K window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Hunyuan Hy3
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
→ Hunyuan Hy3
Larger 256K window fits more in one prompt.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost
→ Hunyuan Hy3
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 Hunyuan Hy3
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs are real: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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." Hunyuan Hy3 (China) and Microsoft Phi-4 (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Hunyuan Hy3 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 Hunyuan Hy3 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 Hunyuan Hy3 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, Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost 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, Hunyuan Hy3 or Microsoft Phi-4?
Hunyuan Hy3 is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Hunyuan Hy3 — 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 Hunyuan Hy3 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, 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, Hunyuan Hy3 or Microsoft Phi-4?
Hunyuan Hy3 — released July 6, 2026, about 18 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.