LLM Compare
Head to head

Particle-1 vs Muse Spark Open

How Particle-1 and Muse Spark Open stack up across benchmarks, pricing, speed, and the workloads that matter.

Particle-1

Particle AI · Proprietary

Blended price$0.33per 1M tokens
Context1.05Mtokens
Speedn/atok/s
Benchmarks3results
VS

Muse Spark Open

Meta · Open weights

Blended price$1.58per 1M tokens
Context1.05Mtokens
Speedn/atok/s
Benchmarks17results

Particle-1 is the cheaper of the two at $0.33 per million blended tokens, about 4.75x less than Muse Spark Open at $1.58. On reasoning, Particle-1 leads by about 34 points in our normalized benchmark average.

The table below breaks down every workload axis where both models have published results. Each score is a normalized 0 to 1 average across the benchmarks tagged with that category. A gap of a few points is noise; a gap of ten or more is a real difference in capability. Where one model has not reported a result, the cell shows n/a and the missing benchmark does not drag its average down.

Benchmark scores by workload

WorkloadParticle-1Muse Spark OpenEdge
Reasoning 88% 55% +33.5 pts
Coding n/a 68% n/a
Agents & tool use n/a 74% n/a
Math 86% 60% +26.3 pts
Vision & multimodal 89% 66% +22.9 pts
Writing n/a 62% n/a
Knowledge & factuality 88% 39% +49.1 pts

The headline benchmarks below are the most widely cited individual tests. GPQA Diamond measures graduate-level reasoning in physics, chemistry, and biology. SWE-Bench Verified tests whether a model can fix real GitHub issues. MMMU-Pro covers college-level multimodal understanding across six disciplines. AIME and FrontierMath push competitive and research math. BrowseComp measures web research ability.

Headline benchmarks

BenchmarkParticle-1Muse Spark Open
GPQA 90% 89%

Benchmarks tell you what a model can do in a controlled setting. They do not tell you whether it will work on your specific task, with your specific data, at your specific scale. The recommendations below map each common workload to whichever of these two models scores higher on the relevant axis. Use them as a starting point, not a final answer.

Which should you choose?

Hard reasoning

Multi-step analysis, research, and problems that need sustained thought.

Particle-1

Coding & software

Writing, reviewing, and debugging code across a real codebase.

Muse Spark Open

Agents & tool use

Long-running agents that call tools, browse, and act on their own.

Muse Spark Open

Math & science

Formal math, competitive problems, and quantitative science.

Particle-1

Vision & documents

Reading images, screenshots, charts, and dense documents.

Particle-1

Writing & drafting

Copy, emails, and long-form drafting where tone matters.

Muse Spark Open

High-volume & cost-sensitive

Cheap, repetitive calls where the blended token price dominates.

Particle-1

Scores are normalized from public benchmarks published by llm-stats.com and averaged per workload. Pricing is the blended input/output cost per million tokens at an 8:1 mix. Refreshes daily. How this works.