Gemini 4 Argon vs Particle-1

Google · US  |  Particle AI · US · Updated June 2026

Quick verdict

Pick Gemini 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses or google-reported deepswe v1.1 of 77.9% and lvbench long-video understanding of 91.7%. 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 4 Argon (Google) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Gemini 4 Argon is google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). 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

Side-by-side specs

SpecGemini 4 ArgonParticle-1
ProviderGoogle (US) Particle AI (US)
ReleasedOctober 13, 2026 October 28, 2026
Context window1M tokens (~1,573 pages) 1M tokens (~1,573 pages)
Price (in/out)$2/$10 per 1M tokens $0.25/$1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image, audio, video, code text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

Gemini 4 Argon

Gemini 4 Argon lists 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses among its strengths; Particle-1 does not.

Google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%

Gemini 4 Argon

Particle-1 is comparatively weak here — all benchmarks are vendor-reported on Particle's own harness; none were independently reproduced as of October 31

Artificial Analysis Intelligence Index of 53 at high effort (AI Weekly)

Gemini 4 Argon

Gemini 4 Argon lists artificial Analysis Intelligence Index of 53 at high effort (AI Weekly) among its strengths; Particle-1 does not.

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

Particle-1

Gemini 4 Argon is comparatively weak here — verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high

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.

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 4 Argon, and on millions of tokens that margin decides the monthly bill.

Anyone whose priority is 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses

→ Gemini 4 Argon

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 4 Argon: where it fits

Google's Gemini 4 Argon, broadly available October 13, 2026 - a 1M-context model at $2/$10 introductory pricing (doubling December 1). Released October 13, 2026 by Google, it is built for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, google-reported DeepSWE v1.1 of 77.9% and LVBench long-video understanding of 91.7%, and artificial Analysis Intelligence Index of 53 at high effort (AI Weekly).

Its trade-offs are real: introductory $2/$10 pricing ends November 30; from December 1 it doubles to $4/$20, verbose: about 62K output tokens per Artificial Analysis task, so cost per task is high, and announced September 30 for trusted cyber defenders only; broad paid-tier availability followed October 13 (not on the free tier). At $2 in / $10 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 4 Argon 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 4 Argon for 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses, 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 4 Argon 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 Gemini 4 Argon 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 4 Argon leans toward 1,048,576-token unified context shared by input, thinking and output, with 'long decode' for very long responses 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 4 Argon or Particle-1?

Particle-1 is cheaper — $2/$10 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 8× 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 Gemini 4 Argon and Particle-1 together?

Yes — a multi-model platform like LumiChats gives you Gemini 4 Argon, 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 4 Argon or Particle-1?

Particle-1 — released October 28, 2026, about 15 days after Gemini 4 Argon.

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.