Pick Command A+ for cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model or a 218b-total/25b-active moe released with open weights (apache 2.0) on hugging face. 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 Command A+ if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Command A+ (Cohere, Canada) 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. Command A+ is cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Their biggest split is open vs. closed weights, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Cost model: Command A+ 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: both advertise 256K (unconfirmed) (~384 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: MAI-Thinking-1 is the newer model by about 3 months (released August 12, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Command A+
MAI-Thinking-1
Provider
Cohere (Canada)
Microsoft (US)
Released
May 19, 2026
August 12, 2026
Context window
256K (unconfirmed) (~384 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model: Command A+ — Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support — and its weights are open while MAI-Thinking-1 is API-only.
A 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face: Command A+ — Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
Supports 48 languages, including all official EU languages: Command A+ — Command A+ lists supports 48 languages, including all official EU languages among its strengths; MAI-Thinking-1 does not.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%): MAI-Thinking-1 — Command A+ is comparatively weak here — enterprise/multilingual focus rather than topping general reasoning leaderboards
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation: MAI-Thinking-1 — Command A+ is comparatively weak here — newer release with less independent benchmark verification than established flagships
Efficient reasoning at low token cost for its class: MAI-Thinking-1 — Command A+ is comparatively weak here — no official context window or per-token price published yet - both are estimated/unavailable
Which should you pick?
A team with data-privacy or self-hosting needs: Command A+ — Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
Anyone whose priority is cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model: Command A+ — 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 Command A+ — Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Command A+: where it fits
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Released May 19, 2026 by Cohere, it is built for cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model, a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face, supports 48 languages, including all official EU languages, and positioned by Cohere as its 'most efficient and performant model to date'.
Its trade-offs are real: no official context window or per-token price published yet - both are estimated/unavailable, newer release with less independent benchmark verification than established flagships, and enterprise/multilingual focus rather than topping general reasoning leaderboards. 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. Command A+ 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 Command A+ 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, Command A+ leans toward cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model 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, Command A+ or MAI-Thinking-1?
Command A+ 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?
Both advertise 256K (unconfirmed) (~384 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Command A+ and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Command A+, 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, Command A+ or MAI-Thinking-1?
MAI-Thinking-1 — released August 12, 2026, about 3 months after Command A+.
Command A+ vs MAI-Thinking-1
Cohere · Canada | Microsoft · US · Updated June 2026
Quick verdict
Pick Command A+ for cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model or a 218b-total/25b-active moe released with open weights (apache 2.0) on hugging face. 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 Command A+ if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
Command A+ (Cohere, Canada) 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. Command A+ is cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Their biggest split is open vs. closed weights, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Cost model: Command A+ 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: both advertise 256K (unconfirmed) (~384 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: MAI-Thinking-1 is the newer model by about 3 months (released August 12, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Command A+
MAI-Thinking-1
Provider
Cohere (Canada)
Microsoft (US)
Released
May 19, 2026
August 12, 2026
Context window
256K (unconfirmed) (~384 pages)
256K (~384 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model
Command A+
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support — and its weights are open while MAI-Thinking-1 is API-only.
A 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face
Command A+
Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
Supports 48 languages, including all official EU languages
Command A+
Command A+ lists supports 48 languages, including all official EU languages among its strengths; MAI-Thinking-1 does not.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
Command A+ is comparatively weak here — enterprise/multilingual focus rather than topping general reasoning leaderboards
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
Command A+ is comparatively weak here — newer release with less independent benchmark verification than established flagships
Efficient reasoning at low token cost for its class
MAI-Thinking-1
Command A+ is comparatively weak here — no official context window or per-token price published yet - both are estimated/unavailable
Which should you pick?
A team with data-privacy or self-hosting needs
→ Command A+
Open weights let you run it on your own hardware; MAI-Thinking-1 is API-only.
Anyone whose priority is cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model
→ Command A+
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 Command A+
Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Command A+: where it fits
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Released May 19, 2026 by Cohere, it is built for cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model, a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face, supports 48 languages, including all official EU languages, and positioned by Cohere as its 'most efficient and performant model to date'.
Its trade-offs are real: no official context window or per-token price published yet - both are estimated/unavailable, newer release with less independent benchmark verification than established flagships, and enterprise/multilingual focus rather than topping general reasoning leaderboards. 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. Command A+ 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 Command A+ 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.
Is Command A+ 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, Command A+ leans toward cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model 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, Command A+ or MAI-Thinking-1?
Command A+ 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?
Both advertise 256K (unconfirmed) (~384 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Command A+ and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Command A+, 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, Command A+ or MAI-Thinking-1?
MAI-Thinking-1 — released August 12, 2026, about 3 months after Command A+.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.