Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Quasar 438B (max, based on GLM-5.2) | GLM-4.5V (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.6 | $0.6 |
| Output ($/M tokens) | $1.8 | $1.8 |
Verdict. Quasar 438B (max, based on GLM-5.2) 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, GLM-4.5V (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GLM-4.5V (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Quasar 438B (max, based on GLM-5.2) is strongest on Coding Index (61.2), Intelligence Index (43.0). GLM-4.5V (Reasoning) leads on Intelligence Index (9.0).
Speed. On throughput, Quasar 438B (max, based on GLM-5.2) generates tokens at 193 tok/s versus 72 tok/s — about 63% faster. On time-to-first-token, Quasar 438B (max, based on GLM-5.2) responds in 1050ms vs 2420ms, which matters most for chat-style UIs.
Provider. Multiverse Computing and Z AI sell to overlapping but distinct developer audiences: Multiverse Computing 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): Quasar 438B (max, based on GLM-5.2) costs $45.00 ($540/year); GLM-4.5V (Reasoning) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Quasar 438B (max, based on GLM-5.2) ≈ $6.60/run, GLM-4.5V (Reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): Quasar 438B (max, based on GLM-5.2) ≈ $300/run, GLM-4.5V (Reasoning) ≈ $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 GLM-4.5V (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 72 tok/s. Quasar 438B (max, based on GLM-5.2) also has lower time-to-first-token (1.05s vs 2.42s).
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.