MAI-1-preview vs Particle-1

Microsoft · US  |  Particle AI · 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 Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash or particle reports 72.57% task pass rate versus 71.15% for deepseek v4.1 flash, at $0.2211 versus $0.3368 per task. On a tight budget at scale, MAI-1-preview is the value pick.

MAI-1-preview (Microsoft) and Particle-1 (Particle AI) 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. Particle-1 is particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. 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-previewParticle-1
ProviderMicrosoft (US) Particle AI (US)
ReleasedAugust 28, 2025 October 28, 2026
Context window128K (~192 pages) 1M tokens (~1,573 pages)
Price (in/out)Not published $0.25/$1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, code text, image, code
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

Particle-1 is comparatively weak here — aPI-only with no published weights, although its DeepSeek base model is open

Ranked in the top 15 on LM Arena at launch

MAI-1-preview

Particle-1 is comparatively weak here — pricing is inconsistent across sources: Particle's launch post said $0.30/$1.20 and OpenRouter shows $0.05/$0.50, while its billing docs say $0.25/$1.00

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; Particle-1 does not.

Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash

Particle-1

Its 1M tokens window holds about 8.2× more than MAI-1-preview's 128K in a single prompt.

Particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task

Particle-1

Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates — and it carries the larger 1M tokens context.

Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks

Particle-1

MAI-1-preview is comparatively weak here — a 'preview' release, not yet positioned as Microsoft's primary Copilot model

Lowest cost at scale

MAI-1-preview

Its weights are open, so at volume you pay for your own hardware instead of Particle-1's $0.25/$1 per 1M tokens.

Largest single-prompt input

Particle-1

Its 1M tokens window is about 8.2× larger than MAI-1-preview's 128K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ MAI-1-preview

At Not published it undercuts Particle-1, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Particle-1

Larger 1M 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 token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash

→ Particle-1

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.

Particle-1: where it fits

Particle AI's October 28, 2026 debut - a closed, token-efficient coding model post-trained on DeepSeek V4.1 Flash, with 1M context at $0.25/$1.00 at peak rates. Released October 28, 2026 by Particle AI, it is built for token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash, particle reports 72.57% task pass rate versus 71.15% for DeepSeek V4.1 Flash, at $0.2211 versus $0.3368 per task, particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks, and peak API price of $0.25/$1.00 per million tokens, halved off-peak to $0.125/$0.50 (20:30-04:00 New York time).

Its trade-offs: aPI-only with no published weights, although its DeepSeek base model is open, all benchmarks are vendor-reported on Particle's own harness; none were independently reproduced as of October 31, and pricing is inconsistent across sources: Particle's launch post said $0.30/$1.20 and OpenRouter shows $0.05/$0.50, while its billing docs say $0.25/$1.00. At $0.25 in / $1 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

MAI-1-preview and Particle-1 overlap enough that the right pick depends on your specific job. MAI-1-preview costs less per token; Particle-1 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, Particle-1 for token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both MAI-1-preview and Particle-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 MAI-1-preview or Particle-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, 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 Particle-1 leans toward token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, MAI-1-preview or Particle-1?

MAI-1-preview is cheaper — Not published vs $0.25/$1 per 1M tokens.

Which has the bigger context window?

Particle-1 — 1M tokens vs 128K, about 8.2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both MAI-1-preview and Particle-1 together?

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

Particle-1 — released October 28, 2026, about 14 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.