Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-4.6V (Non-reasoning) | Llama 4 Maverick |
|---|---|---|
| Input ($/M tokens) | $0.3 | $0.26 |
| Output ($/M tokens) | $0.9 | $0.91 |
Verdict. Llama 4 Maverick 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, GLM-4.6V (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GLM-4.6V (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-4.6V (Non-reasoning) is strongest on Intelligence Index (10.9). Llama 4 Maverick leads on Coding Index (16.3), Intelligence Index (14.5).
Speed. On throughput, Llama 4 Maverick generates tokens at 96 tok/s versus 73 tok/s — about 24% faster. On time-to-first-token, Llama 4 Maverick responds in 930ms vs 3810ms, which matters most for chat-style UIs.
Provider. Z AI and Meta sell to overlapping but distinct developer audiences: Z AI 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): GLM-4.6V (Non-reasoning) costs $22.50 ($270/year); Llama 4 Maverick costs $21.45 ($257/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-4.6V (Non-reasoning) ≈ $3.30/run, Llama 4 Maverick ≈ $3.12/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-4.6V (Non-reasoning) ≈ $150/run, Llama 4 Maverick ≈ $143/run. Llama 4 Maverick 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.
Data from Artificial Analysis API — 12 benchmarks
Llama 4 Maverick is cheaper overall. Its blended price (3:1 input/output ratio) is $0.42/M tokens vs $0.45/M for GLM-4.6V (Non-reasoning).
Llama 4 Maverick wins 2 out of 12 benchmarks compared to 0 for GLM-4.6V (Non-reasoning). See the detailed benchmark chart above for per-category results.
Llama 4 Maverick generates tokens faster at 96 tok/s vs 73 tok/s. However, Llama 4 Maverick has lower time-to-first-token (0.93s vs 3.81s).
Choose based on your priorities: Llama 4 Maverick for lower cost, Llama 4 Maverick for stronger benchmark performance, and Llama 4 Maverick for faster generation. For latency-sensitive apps, check the TTFT comparison above.