Compare/Muse Spark 1.3 (xhigh) vs o3-mini

Muse Spark 1.3 (xhigh)vso3-mini

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Meta

Muse Spark 1.3 (xhigh)

Input
$1.25/M
Output
$4.25/M
Speed
186 tok/s
TTFT
42.55s
OpenAI

o3-mini

Input
$1.1/M
Output
$4.4/M
Speed
203 tok/s
TTFT
5.10s

Winner by Category

Cheaper
o3-mini
Faster (tok/s)
o3-mini
Lower Latency
o3-mini
Benchmarks (2-0)
Muse Spark 1.3 (xhigh)

Pricing Comparison

MetricMuse Spark 1.3 (xhigh)o3-mini
Input ($/M tokens)$1.25$1.1
Output ($/M tokens)$4.25$4.4
Cost for 1M input + 100K output tokens:
Muse Spark 1.3 (xhigh)$1.68
o3-mini$1.54

Speed Comparison

Output Speed (tokens/s) — higher is better
Muse Spark 1.3 (xhigh)
186 tok/s
o3-mini
203 tok/s
Time to First Token (seconds) — lower is better
Muse Spark 1.3 (xhigh)
42.55s
o3-mini
5.10s

Editorial Analysis

Verdict. Muse Spark 1.3 (xhigh) wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.

Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Muse Spark 1.3 (xhigh) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Muse Spark 1.3 (xhigh) makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Muse Spark 1.3 (xhigh) is strongest on Coding Index (76.5), Intelligence Index (60.8). o3-mini leads on Intelligence Index (19.2).

Speed. Throughput is comparable — 186 tok/s vs 203 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.

Provider. Meta and OpenAI sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Muse Spark 1.3 (xhigh) costs $101.25 ($1215/year); o3-mini costs $99.00 ($1188/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Muse Spark 1.3 (xhigh) ≈ $14.75/run, o3-mini ≈ $14.30/run. At agent/realtime scale (200M input / 100M output per million requests): Muse Spark 1.3 (xhigh) ≈ $675/run, o3-mini ≈ $660/run. o3-mini becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.

Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.

Head-to-head deltas

  • Time-to-first-token differs by 8.3× — o3-mini responds in 5100ms vs 42550ms. For interactive chat UIs this can matter more than raw benchmark wins.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
60.819.2
Coding Index
76.5
Math Index
GPQA Diamond
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
SciCode
IFBench
TerminalBench
Muse Spark 1.3 (xhigh)2 wins
0 winso3-mini

Frequently Asked Questions

Which is cheaper, Muse Spark 1.3 (xhigh) or o3-mini?

o3-mini is cheaper overall. Its blended price (3:1 input/output ratio) is $1.93/M tokens vs $2.00/M for Muse Spark 1.3 (xhigh).

Which model performs better on benchmarks?

Muse Spark 1.3 (xhigh) wins 2 out of 12 benchmarks compared to 0 for o3-mini. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

o3-mini generates tokens faster at 203 tok/s vs 186 tok/s. However, o3-mini has lower time-to-first-token (5.10s vs 42.55s).

When should I use Muse Spark 1.3 (xhigh) vs o3-mini?

Choose based on your priorities: o3-mini for lower cost, Muse Spark 1.3 (xhigh) for stronger benchmark performance, and o3-mini for faster generation. For latency-sensitive apps, check the TTFT comparison above.