MAI-Thinking-1 vs Particle-1
Microsoft · US | Particle AI · US · Updated June 2026
Quick verdict
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. 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-Thinking-1 is the value pick.
MAI-Thinking-1 (Microsoft) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. 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
- ▸Context window: Particle-1 holds 4.1× more — 1M tokens (~1,573 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Particle-1 is the newer model by about 3 months (released October 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | MAI-Thinking-1 | Particle-1 |
|---|---|---|
| Provider | Microsoft (US) | Particle AI (US) |
| Released | August 12, 2026 | October 28, 2026 |
| Context window | 256K (~384 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 |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
MAI-Thinking-1 lists very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%) among its strengths; Particle-1 does not.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
MAI-Thinking-1 lists microsoft's first in-house flagship reasoner, trained without OpenAI distillation among its strengths; Particle-1 does not.
Efficient reasoning at low token cost for its class
MAI-Thinking-1
MAI-Thinking-1 lists efficient reasoning at low token cost for its class 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 4.1× more than MAI-Thinking-1's 256K 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-Thinking-1 is comparatively weak here — benchmarks are largely self-reported
Lowest cost at scale
MAI-Thinking-1
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 4.1× larger than MAI-Thinking-1's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
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-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 are real: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
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-Thinking-1 and Particle-1 overlap enough that the right pick depends on your specific job. MAI-Thinking-1 costs less per token; Particle-1 holds the larger context; and each leads in its own area — MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%), 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-Thinking-1 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 pricingFrequently asked questions
Is MAI-Thinking-1 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-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%) 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-Thinking-1 or Particle-1?
MAI-Thinking-1 is cheaper — Not published vs $0.25/$1 per 1M tokens.
Which has the bigger context window?
Particle-1 — 1M tokens vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MAI-Thinking-1 and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you MAI-Thinking-1, 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-Thinking-1 or Particle-1?
Particle-1 — released October 28, 2026, about 3 months 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.