Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. 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, Inkling is the value pick.
Inkling (Thinking Machines Lab) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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: Inkling holds 61× more — 1M (~1,500 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: Inkling is the newer model by about 18 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Inkling
Microsoft Phi-4
Provider
Thinking Machines Lab (US)
Microsoft (US)
Released
July 15, 2026
January 10, 2025
Context window
1M (~1,500 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, image, audio, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI: Inkling — Microsoft Phi-4 is comparatively weak here — an early-2025 small model, outclassed on hard tasks by 2026 flagships
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model: Inkling — Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request: Inkling — Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and it carries the larger 1M context.
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; Inkling 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; Inkling does not.
Runs on modest or local hardware: Microsoft Phi-4 — Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Inkling does not.
Lowest cost at scale: Inkling — 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: Inkling — Its 1M window is about 61× larger than Microsoft Phi-4's 16K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Inkling — 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: Inkling — Larger 1M window fits more in one prompt.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai: Inkling — 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.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. 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
Inkling and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Inkling costs less per token; Inkling holds the larger context; and each leads in its own area — Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Inkling 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, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai 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, Inkling or Microsoft Phi-4?
Inkling is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Inkling — 1M vs 16K, about 61× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Inkling and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Microsoft Phi-4?
Inkling — released July 15, 2026, about 18 months after Microsoft Phi-4.
Inkling vs Microsoft Phi-4
Thinking Machines Lab · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. 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, Inkling is the value pick.
Inkling (Thinking Machines Lab) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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: Inkling holds 61× more — 1M (~1,500 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: Inkling is the newer model by about 18 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Inkling
Microsoft Phi-4
Provider
Thinking Machines Lab (US)
Microsoft (US)
Released
July 15, 2026
January 10, 2025
Context window
1M (~1,500 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, image, audio, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Microsoft Phi-4 is comparatively weak here — an early-2025 small model, outclassed on hard tasks by 2026 flagships
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and it carries the larger 1M context.
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; Inkling 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; Inkling does not.
Runs on modest or local hardware
Microsoft Phi-4
Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Inkling does not.
Lowest cost at scale
Inkling
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
Inkling
Its 1M window is about 61× larger than Microsoft Phi-4's 16K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Inkling
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
→ Inkling
Larger 1M window fits more in one prompt.
Anyone whose priority is the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
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.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. 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
Inkling and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Inkling costs less per token; Inkling holds the larger context; and each leads in its own area — Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Inkling 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 either model, so the honest test is your own repository — run an identical real bug through both. By design, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai 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, Inkling or Microsoft Phi-4?
Inkling is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Inkling — 1M vs 16K, about 61× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Inkling and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Microsoft Phi-4?
Inkling — released July 15, 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.