Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) | Quasar 438B (max, based on GLM-5.2) |
|---|---|---|
| Input ($/M tokens) | $0.6 | $0.6 |
| Output ($/M tokens) | $1.8 | $1.8 |
Verdict. Quasar 438B (max, based on GLM-5.2) 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, Quasar 438B (max, based on GLM-5.2) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Quasar 438B (max, based on GLM-5.2) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is strongest on Intelligence Index (8.9). Quasar 438B (max, based on GLM-5.2) leads on Coding Index (61.2), Intelligence Index (43.0).
Speed. On throughput, Quasar 438B (max, based on GLM-5.2) generates tokens at 193 tok/s versus 52 tok/s — about 73% faster. On time-to-first-token, Quasar 438B (max, based on GLM-5.2) responds in 1050ms vs 2430ms, which matters most for chat-style UIs.
Provider. NVIDIA and Multiverse Computing sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while Multiverse Computing 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): Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) costs $45.00 ($540/year); Quasar 438B (max, based on GLM-5.2) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) ≈ $6.60/run, Quasar 438B (max, based on GLM-5.2) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) ≈ $300/run, Quasar 438B (max, based on GLM-5.2) ≈ $300/run.
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.
Quasar 438B (max, based on GLM-5.2) wins 2 out of 12 benchmarks compared to 0 for Llama 3.1 Nemotron Ultra 253B v1 (Reasoning). See the detailed benchmark chart above for per-category results.
Quasar 438B (max, based on GLM-5.2) generates tokens faster at 193 tok/s vs 52 tok/s. However, Quasar 438B (max, based on GLM-5.2) has lower time-to-first-token (1.05s vs 2.43s).
Choose based on your priorities: both are similarly priced, Quasar 438B (max, based on GLM-5.2) for stronger benchmark performance, and Quasar 438B (max, based on GLM-5.2) for faster generation. For latency-sensitive apps, check the TTFT comparison above.