Particle-1 vs Qwen3.8 Max Prime
Particle AI · US | Alibaba · China · Updated June 2026
Quick verdict
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. Pick Qwen3.8 Max Prime for same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving or alibaba advertises 1.5-2x higher output throughput than standard qwen3.8 max for latency-sensitive workloads. On a tight budget at scale, Particle-1 is the value pick.
Particle-1 (Particle AI, US) and Qwen3.8 Max Prime (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3.8 Max Prime is alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Price: Particle-1 is about 16× cheaper on input ($0.25/$1 per 1M tokens vs $4/$12 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
- ▸Context window: both advertise 1M tokens (~1,573 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: Particle-1 is the newer model by about 35 days (released October 28, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Particle-1 | Qwen3.8 Max Prime |
|---|---|---|
| Provider | Particle AI (US) | Alibaba (China) |
| Released | October 28, 2026 | September 23, 2026 |
| Context window | 1M tokens (~1,573 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $0.25/$1 per 1M tokens | $4/$12 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, code | text, image, video, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
Particle-1
Qwen3.8 Max Prime is comparatively weak here — no official Alibaba announcement — Alibaba Cloud's Model Studio documentation still lists only qwen3.8-max and its September 2 snapshot, suggesting minimal official backing
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 runs cheaper at $0.25/$1 per 1M tokens.
Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks
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 is the newer of the two.
Same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving
Qwen3.8 Max Prime
Qwen3.8 Max Prime lists same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving among its strengths; Particle-1 does not.
Alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads
Qwen3.8 Max Prime
Qwen3.8 Max Prime lists alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads among its strengths; Particle-1 does not.
1M-token context window with up to 131,072 output tokens, reasoning enabled by default
Qwen3.8 Max Prime
Qwen3.8 Max Prime lists 1M-token context window with up to 131,072 output tokens, reasoning enabled by default among its strengths; Particle-1 does not.
Lowest cost at scale
Particle-1
At $0.25/$1 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Particle-1
At $0.25/$1 per 1M tokens it undercuts Qwen3.8 Max Prime, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash
→ Particle-1
It is specifically built for that.
Anyone whose priority is same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving
→ Qwen3.8 Max Prime
That is its strongest area.
An enterprise with regional data-residency rules
→ Particle-1 or Qwen3.8 Max Prime
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3.8 Max Prime: where it fits
Alibaba's September 23, 2026 high-throughput variant of Qwen3.8 Max — identical 2.4T-parameter architecture at double the price, with throughput gains public testing hasn't yet confirmed. Released September 23, 2026 by Alibaba, it is built for same 2.4T-parameter MoE architecture and full capability set as Qwen3.8 Max, repackaged for higher-throughput serving, alibaba advertises 1.5-2x higher output throughput than standard Qwen3.8 Max for latency-sensitive workloads, 1M-token context window with up to 131,072 output tokens, reasoning enabled by default, and accepts text, image, and video input alongside text output, inherited unchanged from the base model.
Its trade-offs: priced exactly double standard Qwen3.8 Max ($4/$12 vs $2/$6) for a throughput claim the only public measurements available don't show as measurably faster, no official Alibaba announcement — Alibaba Cloud's Model Studio documentation still lists only qwen3.8-max and its September 2 snapshot, suggesting minimal official backing, closed API only — base Qwen3.8 weights went public on Hugging Face in August 2026, but they are text-only and this hosted tier has no separate download, and purely a speed tier, not a capability upgrade — same benchmark ceiling as the cheaper base model. At $4 in / $12 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." Particle-1 (US) and Qwen3.8 Max Prime (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Particle-1 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Particle-1 and Qwen3.8 Max Prime 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 Particle-1 or Qwen3.8 Max Prime 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, Particle-1 leans toward token-efficient coding model, post-trained with reinforcement learning on deepseek v4.1 flash while Qwen3.8 Max Prime leans toward same 2.4t-parameter moe architecture and full capability set as qwen3.8 max, repackaged for higher-throughput serving, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Particle-1 or Qwen3.8 Max Prime?
Particle-1 is cheaper — $0.25/$1 per 1M tokens vs $4/$12 per 1M tokens, roughly 16× apart on input.
Which has the bigger context window?
Both advertise 1M tokens (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Particle-1 and Qwen3.8 Max Prime together?
Yes — a multi-model platform like LumiChats gives you Particle-1, Qwen3.8 Max Prime 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, Particle-1 or Qwen3.8 Max Prime?
Particle-1 — released October 28, 2026, about 35 days after Qwen3.8 Max Prime.
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.