Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.5V (Reasoning) | Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.6 | $0.6 |
| Output ($/M tokens) | $1.8 | $1.8 |
Verdict. GLM-4.5V (Reasoning) wins the overall benchmark matchup 1–0 across 1 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, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.5V (Reasoning) is strongest on Intelligence Index (9.0). Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) leads on Intelligence Index (8.9).
Speed. On throughput, GLM-4.5V (Reasoning) generates tokens at 83 tok/s versus 48 tok/s — about 42% faster. On time-to-first-token, GLM-4.5V (Reasoning) responds in 1770ms vs 2340ms, which matters most for chat-style UIs.
Provider. Z AI and NVIDIA sell to overlapping but distinct developer audiences: Z AI tends to ship frontier reasoning models with premium positioning, while NVIDIA 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): GLM-4.5V (Reasoning) costs $45.00 ($540/year); Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.5V (Reasoning) ≈ $6.60/run, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.5V (Reasoning) ≈ $300/run, Llama 3.1 Nemotron Ultra 253B v1 (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.
GLM-4.5V (Reasoning) wins 1 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.
GLM-4.5V (Reasoning) generates tokens faster at 83 tok/s vs 48 tok/s. GLM-4.5V (Reasoning) also has lower time-to-first-token (1.77s vs 2.34s).
Choose based on your priorities: both are similarly priced, GLM-4.5V (Reasoning) for stronger benchmark performance, and GLM-4.5V (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.