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Compare/Magistral Small 1.2 vs Muse Glimmer (high)

Magistral Small 1.2vsMuse Glimmer (high)

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

Mistral

Magistral Small 1.2

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

Muse Glimmer (high)

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

Winner by Category

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

Pricing Comparison

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

Speed Comparison

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

Editorial Analysis

Verdict. Muse Glimmer (high) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

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

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

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

Provider. Mistral and Meta sell to overlapping but distinct developer audiences: Mistral tends to ship frontier reasoning models with premium positioning, while Meta 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): Magistral Small 1.2 costs $37.50 ($450/year); Muse Glimmer (high) costs $33.00 ($396/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Magistral Small 1.2 ≈ $5.50/run, Muse Glimmer (high) ≈ $4.75/run. At agent/realtime scale (200M input / 100M output per million requests): Magistral Small 1.2 ≈ $250/run, Muse Glimmer (high) ≈ $220/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
8.618.1
Coding Index
14.749.0
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Magistral Small 1.20 wins
2 winsMuse Glimmer (high)

Frequently Asked Questions

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

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. However, Muse Glimmer (high) has lower time-to-first-token (1.06s vs —s).

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

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.