Atria Dawn Preview vs Particle-1
Shanghai AI Laboratory · China | Particle AI · US · Updated June 2026
Quick verdict
Pick Atria Dawn Preview for best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) or 744b-parameter moe built on a glm-5.2 base, released under a fully open mit license. 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. Choose Atria Dawn Preview if you need self-hosting or data privacy; Particle-1 if you want a managed API.
Atria Dawn Preview (Shanghai AI Laboratory, China) and Particle-1 (Particle AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Atria Dawn Preview is a free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: Atria Dawn Preview ships open weights you can self-host (hardware cost only, no per-token fee), while Particle-1 is API-metered at $0.25/$1 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Particle-1 holds 4× more — 1M tokens (~1,573 pages) vs 256K tokens (~393 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 47 days (released October 28, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Atria Dawn Preview | Particle-1 |
|---|---|---|
| Provider | Shanghai AI Laboratory (China) | Particle AI (US) |
| Released | September 11, 2026 | October 28, 2026 |
| Context window | 256K tokens (~393 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.25/$1 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0)
Atria Dawn Preview
Particle-1 is comparatively weak here — all benchmarks are vendor-reported on Particle's own harness; none were independently reproduced as of October 31
744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license
Atria Dawn Preview
Open weights make this possible at all — Particle-1 is API-only, so it cannot leave the vendor's servers.
Free to self-host — no API pricing, run entirely on your own hardware
Atria Dawn 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
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
Particle-1
Its 1M tokens window holds about 4× more than Atria Dawn Preview's 256K tokens 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
Atria Dawn Preview is comparatively weak here — context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate
Lowest cost at scale
Atria Dawn 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 4× larger than Atria Dawn Preview's 256K tokens, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Atria Dawn Preview
At Open weight (self-host / free) 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.
A team with data-privacy or self-hosting needs
→ Atria Dawn Preview
Open weights let you run it on your own hardware; Particle-1 is API-only.
Anyone whose priority is best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0)
→ Atria Dawn 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.
An enterprise with regional data-residency rules
→ Particle-1 or Atria Dawn Preview
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Atria Dawn Preview: where it fits
A free, MIT-licensed 744B open-weight model from Shanghai AI Lab that beat GPT-5.6 Sol on the BrowseComp benchmark — released quietly, weights before paper. Released September 11, 2026 by Shanghai AI Laboratory, it is built for best-in-class among tested models on several of Shanghai AI Lab's own benchmarks, including BrowseComp (92.5, ahead of GPT-5.6 Sol's 92.2) and DeepSearchQA (96.0), 744B-parameter MoE built on a GLM-5.2 base, released under a fully open MIT license, and free to self-host — no API pricing, run entirely on your own hardware.
Its trade-offs are real: context window is reported inconsistently across trackers — most list 256K tokens, at least one lists 1M; unconfirmed which is accurate, released quietly, with weights and an API posted to GitHub before Shanghai AI Lab's own technical report went up on arXiv days later, and a lab research preview rather than a commercial product — support and update cadence are unclear. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
The defining split here is open vs. closed. Atria Dawn Preview gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Particle-1 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 Atria Dawn 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 pricingFrequently asked questions
Is Atria Dawn 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, Atria Dawn Preview leans toward best-in-class among tested models on several of shanghai ai lab's own benchmarks, including browsecomp (92.5, ahead of gpt-5.6 sol's 92.2) and deepsearchqa (96.0) 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, Atria Dawn Preview or Particle-1?
Atria Dawn Preview is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Particle-1 is API-metered at $0.25/$1 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?
Particle-1 — 1M tokens vs 256K tokens, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Atria Dawn Preview and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Atria Dawn 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, Atria Dawn Preview or Particle-1?
Particle-1 — released October 28, 2026, about 47 days after Atria Dawn 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.