Gemini 3.1 Pro vs Particle-1
Google · US | Particle AI · US · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. 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, Particle-1 is the value pick.
Gemini 3.1 Pro (Google) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. 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. 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 8× cheaper on input ($0.25/$1 per 1M tokens vs $2/$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 (~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 8 months (released October 28, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Gemini 3.1 Pro | Particle-1 |
|---|---|---|
| Provider | Google (US) | Particle AI (US) |
| Released | February 19, 2026 | October 28, 2026 |
| Context window | 1M (~1,573 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $2/$12 per 1M tokens | $0.25/$1 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, audio, video, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | 26.3% | Not published |
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window
Gemini 3.1 Pro
Gemini 3.1 Pro lists full multimodal input — text, image, audio and video in one 1M-token window among its strengths; Particle-1 does not.
Long video and document analysis
Gemini 3.1 Pro
Gemini 3.1 Pro lists long video and document analysis among its strengths; Particle-1 does not.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
Gemini 3.1 Pro lists agentic reasoning (high ARC-AGI-2) among its strengths; Particle-1 does not.
Token-efficient coding model, post-trained with reinforcement learning on DeepSeek V4.1 Flash
Particle-1
Gemini 3.1 Pro is comparatively weak here — premium price per token at $2/$12
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
Gemini 3.1 Pro is comparatively weak here — superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work
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 runs cheaper at $0.25/$1 per 1M tokens.
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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window
→ Gemini 3.1 Pro
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.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid price band.
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
Gemini 3.1 Pro and Particle-1 overlap enough that the right pick depends on your specific job. Particle-1 costs less per token; and each leads in its own area — Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window, 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 Gemini 3.1 Pro 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 Gemini 3.1 Pro 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window 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, Gemini 3.1 Pro or Particle-1?
Particle-1 is cheaper — $2/$12 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 8× apart on input.
Which has the bigger context window?
Both advertise 1M (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.1 Pro and Particle-1 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or Particle-1?
Particle-1 — released October 28, 2026, about 8 months after Gemini 3.1 Pro.
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.