Muse Spark 1.3 vs Particle-1

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

Quick verdict

Pick Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 or deepswe v1.1 long-horizon software engineering (75.4). 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.

Muse Spark 1.3 (Meta) and Particle-1 (Particle AI) are two of the models people most often weigh against each other in 2026. Muse Spark 1.3 is meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. 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

Side-by-side specs

SpecMuse Spark 1.3Particle-1
ProviderMeta (US) Particle AI (US)
ReleasedSeptember 2, 2026 October 28, 2026
Context window1M tokens (~1,500 pages) 1M tokens (~1,573 pages)
Price (in/out)$1.25/$4.25 per 1M tokens $0.25/$1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image, video text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1M98.1% Not published

Who wins what

Agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2

Muse Spark 1.3

Muse Spark 1.3 lists agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2 among its strengths; Particle-1 does not.

DeepSWE v1.1 long-horizon software engineering (75.4)

Muse Spark 1.3

Muse Spark 1.3 lists deepSWE v1.1 long-horizon software engineering (75.4) among its strengths; Particle-1 does not.

Near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1)

Muse Spark 1.3

Muse Spark 1.3 lists near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1) among its strengths; Particle-1 does not.

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

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 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 is the newer of the two.

Particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks

Particle-1

Particle-1 lists particle reports 28.4% to 52.2% fewer output tokens than the base model across four benchmarks among its strengths; Muse Spark 1.3 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 Muse Spark 1.3, 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 agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2

→ Muse Spark 1.3

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.

Muse Spark 1.3: where it fits

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. Released September 2, 2026 by Meta, it is built for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2, deepSWE v1.1 long-horizon software engineering (75.4), near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1), and ranks third overall on the Artificial Analysis Intelligence Index (score 61, xhigh variant; the limited-preview max variant scores 62) - behind only Claude Fable 5.1 and Claude Opus 5.

Its trade-offs are real: strongest 'max' reasoning configuration still gated pending additional safety testing, not yet open-weight, despite Meta roadmapping a future Muse Spark weights release, and no official SWE-bench Verified score published. At $1.25 in / $4.25 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

Muse Spark 1.3 and Particle-1 overlap enough that the right pick depends on your specific job. Particle-1 costs less per token; Particle-1 holds the larger context; and each leads in its own area — Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2, 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 Muse Spark 1.3 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 Muse Spark 1.3 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, Muse Spark 1.3 leans toward agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 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, Muse Spark 1.3 or Particle-1?

Particle-1 is cheaper — $1.25/$4.25 per 1M tokens vs $0.25/$1 per 1M tokens, roughly 5× apart on input.

Which has the bigger context window?

Effectively neither — 1M tokens vs 1M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Muse Spark 1.3 and Particle-1 together?

Yes — a multi-model platform like LumiChats gives you Muse Spark 1.3, 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, Muse Spark 1.3 or Particle-1?

Particle-1 — released October 28, 2026, about 56 days after Muse Spark 1.3.

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.