Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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 Kimi K3 if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Kimi K3 (Moonshot AI, China) and MAI-Thinking-1 (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. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Kimi K3 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: Kimi K3 holds 4.1× more — 1M (~1,573 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: Kimi K3 is the newer model by about 55 days (released July 27, 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
Kimi K3
MAI-Thinking-1
Provider
Moonshot AI (China)
Microsoft (US)
Released
July 27, 2026
June 2, 2026
Context window
1M (~1,573 pages)
256K (~384 pages)
Price (in/out)
$3/$15 per 1M tokens
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: Kimi K3 — Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
1M-token context with native vision (text, image and video): Kimi K3 — Its 1M window holds about 4.1× more than MAI-Thinking-1's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: Kimi K3 — MAI-Thinking-1 is comparatively weak here — benchmarks are largely self-reported
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%): MAI-Thinking-1 — MAI-Thinking-1 lists very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%) among its strengths; Kimi K3 does not.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation: MAI-Thinking-1 — MAI-Thinking-1 lists microsoft's first in-house flagship reasoner, trained without OpenAI distillation among its strengths; Kimi K3 does not.
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; Kimi K3 does not.
Lowest cost at scale: MAI-Thinking-1 — Its weights are open, so at volume you pay for your own hardware instead of Kimi K3's $3/$15 per 1M tokens.
Largest single-prompt input: Kimi K3 — Its 1M window is about 4.1× larger than MAI-Thinking-1's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MAI-Thinking-1 — At Not published it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K3 — Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs: Kimi K3 — Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable: Kimi K3 — 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.
An enterprise with regional data-residency rules: MAI-Thinking-1 or Kimi K3 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
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 June 2, 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. Kimi K3 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 Kimi K3 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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, Kimi K3 or MAI-Thinking-1?
Kimi K3 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?
Kimi K3 — 1M vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K3 and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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, Kimi K3 or MAI-Thinking-1?
Kimi K3 — released July 27, 2026, about 55 days after MAI-Thinking-1.
Kimi K3 vs MAI-Thinking-1
Moonshot AI · China | Microsoft · US · Updated June 2026
Quick verdict
Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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 Kimi K3 if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Kimi K3 (Moonshot AI, China) and MAI-Thinking-1 (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. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Kimi K3 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: Kimi K3 holds 4.1× more — 1M (~1,573 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: Kimi K3 is the newer model by about 55 days (released July 27, 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
Kimi K3
MAI-Thinking-1
Provider
Moonshot AI (China)
Microsoft (US)
Released
July 27, 2026
June 2, 2026
Context window
1M (~1,573 pages)
256K (~384 pages)
Price (in/out)
$3/$15 per 1M tokens
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 4.1× more than MAI-Thinking-1's 256K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
MAI-Thinking-1 is comparatively weak here — benchmarks are largely self-reported
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
MAI-Thinking-1 lists very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%) among its strengths; Kimi K3 does not.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
MAI-Thinking-1 lists microsoft's first in-house flagship reasoner, trained without OpenAI distillation among its strengths; Kimi K3 does not.
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; Kimi K3 does not.
Lowest cost at scale
MAI-Thinking-1
Its weights are open, so at volume you pay for your own hardware instead of Kimi K3's $3/$15 per 1M tokens.
Largest single-prompt input
Kimi K3
Its 1M window is about 4.1× larger than MAI-Thinking-1's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
At Not published it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Kimi K3
Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
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.
An enterprise with regional data-residency rules
→ MAI-Thinking-1 or Kimi K3
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Kimi K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
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 June 2, 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. Kimi K3 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 Kimi K3 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable 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, Kimi K3 or MAI-Thinking-1?
Kimi K3 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?
Kimi K3 — 1M vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Kimi K3 and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Kimi K3, 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, Kimi K3 or MAI-Thinking-1?
Kimi K3 — released July 27, 2026, about 55 days after MAI-Thinking-1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.