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Compare/Muse Glimmer (high) vs Magistral Small 1.2

Muse Glimmer (high)vsMagistral Small 1.2

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

Meta

Muse Glimmer (high)

Input
$0.35/M
Output
$1.5/M
Speed
90 tok/s
TTFT
1.06s
Mistral

Magistral Small 1.2

Input
$0.5/M
Output
$1.5/M
Speed
—
TTFT
—

Winner by Category

Cheaper
Muse Glimmer (high)
Faster (tok/s)
Muse Glimmer (high)
Lower Latency
Muse Glimmer (high)
Benchmarks (2-0)
Muse Glimmer (high)

Pricing Comparison

MetricMuse Glimmer (high)Magistral Small 1.2
Input ($/M tokens)$0.35$0.5
Output ($/M tokens)$1.5$1.5
Cost for 1M input + 100K output tokens:
Muse Glimmer (high)$0.50
Magistral Small 1.2$0.65

Speed Comparison

Output Speed (tokens/s) — higher is better
Muse Glimmer (high)
90 tok/s
Magistral Small 1.2
—
Time to First Token (seconds) — lower is better
Muse Glimmer (high)
1.06s
Magistral Small 1.2
—

Editorial Analysis

Verdict. Muse Glimmer (high) 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 budget bracket for output-token pricing. At 1.0× the per-million-token cost, Magistral Small 1.2 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Magistral Small 1.2 makes more sense when output volume is low and absolute reasoning quality justifies the premium.

Strengths. Muse Glimmer (high) is strongest on Coding Index (49.0), Intelligence Index (18.1). Magistral Small 1.2 leads on Coding Index (14.7), Intelligence Index (8.6).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. Meta and Mistral sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Mistral 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 Glimmer (high) costs $33.00 ($396/year); Magistral Small 1.2 costs $37.50 ($450/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Muse Glimmer (high) ≈ $4.75/run, Magistral Small 1.2 ≈ $5.50/run. At agent/realtime scale (200M input / 100M output per million requests): Muse Glimmer (high) ≈ $220/run, Magistral Small 1.2 ≈ $250/run. Muse Glimmer (high) 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.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
18.18.6
Coding Index
49.014.7
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Muse Glimmer (high)2 wins
0 winsMagistral Small 1.2

Frequently Asked Questions

Which is cheaper, Muse Glimmer (high) or Magistral Small 1.2?

Muse Glimmer (high) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.64/M tokens vs $0.75/M for Magistral Small 1.2.

Which model performs better on benchmarks?

Muse Glimmer (high) wins 2 out of 12 benchmarks compared to 0 for Magistral Small 1.2. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Muse Glimmer (high) generates tokens faster at 90 tok/s vs — tok/s. Muse Glimmer (high) also has lower time-to-first-token (1.06s vs —s).

When should I use Muse Glimmer (high) vs Magistral Small 1.2?

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