Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. 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.
GLM 4.7 (Z.ai, 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. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 8.6× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.6/$2.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: GLM 4.7 holds 12× more — 200K (~304 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: GLM 4.7 is the newer model by about 12 months (released December 22, 2025), 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
GLM 4.7
Microsoft Phi-4
Provider
Z.ai (China)
Microsoft (US)
Released
December 22, 2025
January 10, 2025
Context window
200K (~304 pages)
16K (~25 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it carries the larger 200K context.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — Microsoft Phi-4 is comparatively weak here — no first-party per-token API; hosted prices are third-party
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — Its 200K window holds about 12× more than Microsoft Phi-4's 16K in a single prompt.
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 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: GLM 4.7 — Its 200K window is about 12× larger than Microsoft Phi-4's 16K, fitting roughly 304 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 GLM 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 4.7 — Larger 200K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — 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 GLM 4.7 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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." GLM 4.7 (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 GLM 4.7 or Microsoft Phi-4 better for coding?
Public SWE-Bench figures are not available for Microsoft Phi-4, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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, GLM 4.7 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.6/$2.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 8.6× apart on input.
Which has the bigger context window?
GLM 4.7 — 200K vs 16K, about 12× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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, GLM 4.7 or Microsoft Phi-4?
GLM 4.7 — released December 22, 2025, about 12 months after Microsoft Phi-4.
GLM 4.7 vs Microsoft Phi-4
Z.ai · China | Microsoft · US · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. 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.
GLM 4.7 (Z.ai, 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. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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 8.6× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.6/$2.2 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: GLM 4.7 holds 12× more — 200K (~304 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: GLM 4.7 is the newer model by about 12 months (released December 22, 2025), 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
GLM 4.7
Microsoft Phi-4
Provider
Z.ai (China)
Microsoft (US)
Released
December 22, 2025
January 10, 2025
Context window
200K (~304 pages)
16K (~25 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it carries the larger 200K context.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
Microsoft Phi-4 is comparatively weak here — no first-party per-token API; hosted prices are third-party
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
Its 200K window holds about 12× more than Microsoft Phi-4's 16K in a single prompt.
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 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
GLM 4.7
Its 200K window is about 12× larger than Microsoft Phi-4's 16K, fitting roughly 304 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 GLM 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 4.7
Larger 200K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
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 GLM 4.7
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 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." GLM 4.7 (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 GLM 4.7 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.
Public SWE-Bench figures are not available for Microsoft Phi-4, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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, GLM 4.7 or Microsoft Phi-4?
Microsoft Phi-4 is cheaper — $0.6/$2.2 per 1M tokens vs $0.07/$0.14 per 1M tokens, roughly 8.6× apart on input.
Which has the bigger context window?
GLM 4.7 — 200K vs 16K, about 12× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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, GLM 4.7 or Microsoft Phi-4?
GLM 4.7 — released December 22, 2025, about 12 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.