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 MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. Choose Inkling if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Inkling (Thinking Machines Lab) and MAI-Thinking-1 (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. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-Thinking-1 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Inkling holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: MAI-Thinking-1 is the newer model by about 28 days (released August 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Inkling
MAI-Thinking-1
Provider
Thinking Machines Lab (US)
Microsoft (US)
Released
July 15, 2026
August 12, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
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 — Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model: 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.
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 its weights are open while MAI-Thinking-1 is API-only.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%): MAI-Thinking-1 — Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it is the newer of the two.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation: MAI-Thinking-1 — Inkling is comparatively weak here — a brand-new lab's first release - no multi-generation track record yet
Efficient reasoning at low token cost for its class: MAI-Thinking-1 — MAI-Thinking-1 lists efficient reasoning at low token cost for its class among its strengths; Inkling does not.
Largest single-prompt input: Inkling — Its 1M window is about 3.9× larger than MAI-Thinking-1's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Inkling — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Inkling — Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%): MAI-Thinking-1 — 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.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released August 12, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
The defining split here is open vs. closed. Inkling gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-Thinking-1 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 Inkling or MAI-Thinking-1 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 MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling or MAI-Thinking-1?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-Thinking-1 is API-metered at Not published. 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?
Inkling — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Inkling and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Inkling, MAI-Thinking-1 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 MAI-Thinking-1?
MAI-Thinking-1 — released August 12, 2026, about 28 days after Inkling.
Inkling vs MAI-Thinking-1
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 MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. Choose Inkling if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Inkling (Thinking Machines Lab) and MAI-Thinking-1 (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. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Inkling ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-Thinking-1 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Inkling holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: MAI-Thinking-1 is the newer model by about 28 days (released August 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Inkling
MAI-Thinking-1
Provider
Thinking Machines Lab (US)
Microsoft (US)
Released
July 15, 2026
August 12, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
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
Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
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.
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 its weights are open while MAI-Thinking-1 is API-only.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence — and it is the newer of the two.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
Inkling is comparatively weak here — a brand-new lab's first release - no multi-generation track record yet
Efficient reasoning at low token cost for its class
MAI-Thinking-1
MAI-Thinking-1 lists efficient reasoning at low token cost for its class among its strengths; Inkling does not.
Largest single-prompt input
Inkling
Its 1M window is about 3.9× larger than MAI-Thinking-1's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Inkling
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Inkling
Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
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.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released August 12, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
The defining split here is open vs. closed. Inkling gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-Thinking-1 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 Inkling and MAI-Thinking-1 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 MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling or MAI-Thinking-1?
Inkling is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-Thinking-1 is API-metered at Not published. 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?
Inkling — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Inkling and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Inkling, MAI-Thinking-1 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 MAI-Thinking-1?
MAI-Thinking-1 — released August 12, 2026, about 28 days after Inkling.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.