Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. 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. Choose Microsoft Phi-4 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Microsoft Phi-4 is about 1.4× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.1/$0.5 per 1M tokens) — modest, but it adds up at steady volume.
Context window: GPT-6 Luna holds 64× more — 1.05M tokens (~1,575 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: GPT-6 Luna is the newer model by about 21 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-6 Luna
Microsoft Phi-4
Provider
OpenAI (US)
Microsoft (US)
Released
September 22, 2026
January 10, 2025
Context window
1.05M tokens (~1,575 pages)
16K (~25 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — Its 1.05M tokens window holds about 64× more than Microsoft Phi-4's 16K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Open weights make this possible at all — GPT-6 Luna is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — At $0.07/$0.14 per 1M tokens it undercuts GPT-6 Luna ($0.1/$0.5 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: GPT-6 Luna — Its 1.05M tokens window is about 64× larger than Microsoft Phi-4's 16K, fitting roughly 1,575 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 GPT-6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-6 Luna — Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: Microsoft Phi-4 — Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — 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.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 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
The defining split here is open vs. closed. Microsoft Phi-4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is GPT-6 Luna 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, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume 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, GPT-6 Luna or Microsoft Phi-4?
Microsoft Phi-4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 16K, about 64× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, 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, GPT-6 Luna or Microsoft Phi-4?
GPT-6 Luna — released September 22, 2026, about 21 months after Microsoft Phi-4.
GPT-6 Luna vs Microsoft Phi-4
OpenAI · US | Microsoft · US · Updated June 2026
Quick verdict
Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. 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. Choose Microsoft Phi-4 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
GPT-6 Luna (OpenAI) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Microsoft Phi-4 is about 1.4× cheaper on input ($0.07/$0.14 per 1M tokens vs $0.1/$0.5 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: GPT-6 Luna holds 64× more — 1.05M tokens (~1,575 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: GPT-6 Luna is the newer model by about 21 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-6 Luna
Microsoft Phi-4
Provider
OpenAI (US)
Microsoft (US)
Released
September 22, 2026
January 10, 2025
Context window
1.05M tokens (~1,575 pages)
16K (~25 pages)
Price (in/out)
$0.1/$0.5 per 1M tokens
$0.07/$0.14 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
GPT-6 Luna
Its 1.05M tokens window holds about 64× more than Microsoft Phi-4's 16K in a single prompt.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it is the newer of the two.
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Open weights make this possible at all — GPT-6 Luna is API-only, so it cannot leave the vendor's servers.
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
At $0.07/$0.14 per 1M tokens it undercuts GPT-6 Luna ($0.1/$0.5 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
GPT-6 Luna
Its 1.05M tokens window is about 64× larger than Microsoft Phi-4's 16K, fitting roughly 1,575 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 GPT-6 Luna, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Luna
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Microsoft Phi-4
Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
→ GPT-6 Luna
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.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs are real: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 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
The defining split here is open vs. closed. Microsoft Phi-4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both GPT-6 Luna 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 GPT-6 Luna 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, GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume 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, GPT-6 Luna or Microsoft Phi-4?
Microsoft Phi-4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 16K, about 64× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-6 Luna and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you GPT-6 Luna, 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, GPT-6 Luna or Microsoft Phi-4?
GPT-6 Luna — released September 22, 2026, about 21 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.