Command A+ vs GPT-5.6 Luna

Cohere · Canada  |  OpenAI · 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 GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation or fast, affordable execution while keeping respectable coding. Choose Command A+ if you need self-hosting or data privacy; GPT-5.6 Luna if you want a managed API.

Command A+ (Cohere, Canada) and GPT-5.6 Luna (OpenAI, 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. GPT-5.6 Luna is the budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. 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

Side-by-side specs

SpecCommand A+GPT-5.6 Luna
ProviderCohere (Canada) OpenAI (US)
ReleasedMay 19, 2026 July 9, 2026
Context window256K (unconfirmed) (~384 pages) 1M (~1,500 pages)
Price (in/out)Open weight (self-host / free) $1/$6 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot 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 GPT-5.6 Luna 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 — GPT-5.6 Luna 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; GPT-5.6 Luna does not.

Cheapest GPT-5.6 tier for high-volume drafting and automation

GPT-5.6 Luna

The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning — and it carries the larger 1M context.

Fast, affordable execution while keeping respectable coding

GPT-5.6 Luna

The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning — and it is the newer of the two.

Same 1M context and programmatic tool calling as its siblings

GPT-5.6 Luna

Its 1M window holds about 3.9× more than Command A+'s 256K (unconfirmed) in a single prompt.

Lowest cost at scale

Command A+

Its weights are open, so at volume you pay for your own hardware instead of GPT-5.6 Luna's $1/$6 per 1M tokens.

Largest single-prompt input

GPT-5.6 Luna

Its 1M window is about 3.9× larger than Command A+'s 256K (unconfirmed), fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Command A+

At Open weight (self-host / free) it undercuts GPT-5.6 Luna, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

GPT-5.6 Luna

Larger 1M window fits more in one prompt.

A team with data-privacy or self-hosting needs

Command A+

Open weights let you run it on your own hardware; GPT-5.6 Luna 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 cheapest gpt-5.6 tier for high-volume drafting and automation

GPT-5.6 Luna

That is its strongest area.

An enterprise with regional data-residency rules

GPT-5.6 Luna 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.

GPT-5.6 Luna: where it fits

The budget, high-throughput GPT-5.6 tier — built for cheap, fast, large-volume work rather than deep long-context reasoning. Released July 9, 2026 by OpenAI, it is built for cheapest GPT-5.6 tier for high-volume drafting and automation, fast, affordable execution while keeping respectable coding, same 1M context and programmatic tool calling as its siblings, and high-throughput simple agentic jobs.

Its trade-offs: weak long-context recall deep in its 1M window (MRCR far below Sol), and lowest raw capability of the three GPT-5.6 tiers; no open weights. At $1 in / $6 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. Command A+ gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-5.6 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 Command A+ and GPT-5.6 Luna 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.

See pricing

Frequently asked questions

Is Command A+ or GPT-5.6 Luna 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 GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Command A+ or GPT-5.6 Luna?

Command A+ is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-5.6 Luna is API-metered at $1/$6 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-5.6 Luna — 1M vs 256K (unconfirmed), about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Command A+ and GPT-5.6 Luna together?

Yes — a multi-model platform like LumiChats gives you Command A+, GPT-5.6 Luna 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 GPT-5.6 Luna?

GPT-5.6 Luna — released July 9, 2026, about 51 days after Command A+.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.