Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Muse Spark 1.2 (xhigh) | GLM-5.1 (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $1.25 | $1.2 |
| Output ($/M tokens) | $4.25 | $4.4 |
Verdict. Muse Spark 1.2 (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 1.2 (xhigh) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Muse Spark 1.2 (xhigh) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Muse Spark 1.2 (xhigh) is strongest on Coding Index (72.2), Intelligence Index (39.8). GLM-5.1 (Reasoning) leads on Coding Index (55.8), Intelligence Index (26.4).
Speed. On throughput, Muse Spark 1.2 (xhigh) generates tokens at 181 tok/s versus 62 tok/s — about 66% faster. On time-to-first-token, GLM-5.1 (Reasoning) responds in 1730ms vs 18730ms, which matters most for chat-style UIs.
Provider. Meta and Z AI sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Z AI 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 1.2 (xhigh) costs $101.25 ($1215/year); GLM-5.1 (Reasoning) costs $102.00 ($1224/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Muse Spark 1.2 (xhigh) ≈ $14.75/run, GLM-5.1 (Reasoning) ≈ $14.80/run. At agent/realtime scale (200M input / 100M output per million requests): Muse Spark 1.2 (xhigh) ≈ $675/run, GLM-5.1 (Reasoning) ≈ $680/run. Muse Spark 1.2 (xhigh) 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
Data from Artificial Analysis API — 12 benchmarks
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
Muse Spark 1.2 (xhigh) wins 2 out of 12 benchmarks compared to 0 for GLM-5.1 (Reasoning). See the detailed benchmark chart above for per-category results.
Muse Spark 1.2 (xhigh) generates tokens faster at 181 tok/s vs 62 tok/s. However, GLM-5.1 (Reasoning) has lower time-to-first-token (1.73s vs 18.73s).
Choose based on your priorities: both are similarly priced, Muse Spark 1.2 (xhigh) for stronger benchmark performance, and Muse Spark 1.2 (xhigh) for faster generation. For latency-sensitive apps, check the TTFT comparison above.