GPT-5.6 Luna vs MAI-Thinking-1

OpenAI · US  |  Microsoft · US · Updated June 2026

Quick verdict

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. 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. On a tight budget at scale, MAI-Thinking-1 is the value pick.

GPT-5.6 Luna (OpenAI) and MAI-Thinking-1 (Microsoft) are two of the models people most often weigh against each other in 2026. 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. 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 and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGPT-5.6 LunaMAI-Thinking-1
ProviderOpenAI (US) Microsoft (US)
ReleasedJuly 9, 2026 June 2, 2026
Context window1M (~1,500 pages) 256K (~384 pages)
Price (in/out)$1/$6 per 1M tokens Not published
Open weight?No — API only No — API only
Modalitiestext, image, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

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

GPT-5.6 Luna

A core design strength of GPT-5.6 Luna.

Fast, affordable execution while keeping respectable coding

GPT-5.6 Luna

A core design strength of GPT-5.6 Luna.

Same 1M context and programmatic tool calling as its siblings

GPT-5.6 Luna

A core design strength of GPT-5.6 Luna.

Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)

MAI-Thinking-1

A core design strength of MAI-Thinking-1.

Microsoft's first in-house flagship reasoner, trained without OpenAI distillation

MAI-Thinking-1

A core design strength of MAI-Thinking-1.

Efficient reasoning at low token cost for its class

MAI-Thinking-1

A core design strength of MAI-Thinking-1.

Lowest cost at scale

MAI-Thinking-1

At Not published, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

GPT-5.6 Luna

Its 1M window is about 3.9× larger, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

MAI-Thinking-1

At Not published 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.

Anyone whose priority is cheapest gpt-5.6 tier for high-volume drafting and automation

GPT-5.6 Luna

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.

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 are real: 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.

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

GPT-5.6 Luna and MAI-Thinking-1 overlap enough that the right pick depends on your specific job. MAI-Thinking-1 costs less per token; GPT-5.6 Luna holds the larger context; and each leads in its own area — GPT-5.6 Luna for cheapest gpt-5.6 tier for high-volume drafting and automation, MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%). Rather than crowning one, run the same hard task through both once and let the results decide.

Want both GPT-5.6 Luna 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.

See pricing

Frequently asked questions

Is GPT-5.6 Luna 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, GPT-5.6 Luna leans toward cheapest gpt-5.6 tier for high-volume drafting and automation 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, GPT-5.6 Luna or MAI-Thinking-1?

MAI-Thinking-1 is cheaper — $1/$6 per 1M tokens vs Not published.

Which has the bigger context window?

GPT-5.6 Luna — 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 GPT-5.6 Luna and MAI-Thinking-1 together?

Yes — a multi-model platform like LumiChats gives you GPT-5.6 Luna, 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, GPT-5.6 Luna or MAI-Thinking-1?

GPT-5.6 Luna — released July 9, 2026, about 37 days after MAI-Thinking-1.

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.