MAI-1-preview vs Mercury 2.5 Preview

Microsoft · US  |  Inception Labs · US · Updated June 2026

Quick verdict

Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Pick Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation or coding accuracy (95.7%, 91st percentile among cost-optimized models). On a tight budget at scale, MAI-1-preview is the value pick.

MAI-1-preview (Microsoft) and Mercury 2.5 Preview (Inception Labs) are two of the models people most often weigh against each other in 2026. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Mercury 2.5 Preview is inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. 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

SpecMAI-1-previewMercury 2.5 Preview
ProviderMicrosoft (US) Inception Labs (US)
ReleasedAugust 28, 2025 August 31, 2026
Context window128K (~192 pages) 260K tokens (~390 pages)
Price (in/out)Not published $0.04/$0.15 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, code text
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI

MAI-1-preview

Mercury 2.5 Preview is comparatively weak here — 260K context window is far shorter than frontier 1M-token models

Ranked in the top 15 on LM Arena at launch

MAI-1-preview

Mercury 2.5 Preview is comparatively weak here — current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026)

Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment

MAI-1-preview

MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; Mercury 2.5 Preview does not.

Very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation

Mercury 2.5 Preview

Its 260K tokens window holds about 2× more than MAI-1-preview's 128K in a single prompt.

Coding accuracy (95.7%, 91st percentile among cost-optimized models)

Mercury 2.5 Preview

Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it carries the larger 260K tokens context.

Mathematics accuracy (97.0%, 97th percentile)

Mercury 2.5 Preview

Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality — and it is the newer of the two.

Lowest cost at scale

MAI-1-preview

Its weights are open, so at volume you pay for your own hardware instead of Mercury 2.5 Preview's $0.04/$0.15 per 1M tokens.

Largest single-prompt input

Mercury 2.5 Preview

Its 260K tokens window is about 2× larger than MAI-1-preview's 128K, fitting roughly 390 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

MAI-1-preview

At Not published it undercuts Mercury 2.5 Preview, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Mercury 2.5 Preview

Larger 260K tokens window fits more in one prompt.

Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai

MAI-1-preview

It is specifically built for that.

Anyone whose priority is very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation

Mercury 2.5 Preview

That is its strongest area.

MAI-1-preview: where it fits

Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.

Its trade-offs are real: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.

Mercury 2.5 Preview: where it fits

Inception Labs' August 31, 2026 diffusion-based language model preview, refining tokens in parallel for roughly 10x the throughput of comparable autoregressive models at cost-optimized-tier quality. Released August 31, 2026 by Inception Labs, it is built for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, coding accuracy (95.7%, 91st percentile among cost-optimized models), mathematics accuracy (97.0%, 97th percentile), and tunable reasoning levels with parallel tool calls and schema-aligned JSON output.

Its trade-offs: 260K context window is far shorter than frontier 1M-token models, no vision or audio modalities, positioned only against cost-optimized models (GPT-5.6 Luna Low, Gemini 3.5 Flash-Lite), not frontier-class, and current $0.04/$0.15 pricing includes a limited-time launch promotion on OpenRouter (list price is $0.20/$0.75, may rise after Sept 8, 2026). At $0.04 in / $0.15 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

MAI-1-preview and Mercury 2.5 Preview overlap enough that the right pick depends on your specific job. MAI-1-preview costs less per token; Mercury 2.5 Preview holds the larger context; and each leads in its own area — MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, Mercury 2.5 Preview for very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both MAI-1-preview and Mercury 2.5 Preview 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 MAI-1-preview or Mercury 2.5 Preview 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, MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai while Mercury 2.5 Preview leans toward very high inference throughput (1,107 tok/s) via diffusion-based parallel token generation, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, MAI-1-preview or Mercury 2.5 Preview?

MAI-1-preview is cheaper — Not published vs $0.04/$0.15 per 1M tokens.

Which has the bigger context window?

Mercury 2.5 Preview — 260K tokens vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both MAI-1-preview and Mercury 2.5 Preview together?

Yes — a multi-model platform like LumiChats gives you MAI-1-preview, Mercury 2.5 Preview 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, MAI-1-preview or Mercury 2.5 Preview?

Mercury 2.5 Preview — released August 31, 2026, about 12 months after MAI-1-preview.

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.