Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 4 Maverick | Trinity Large Thinking |
|---|---|---|
| Input ($/M tokens) | $0.26 | $0.25 |
| Output ($/M tokens) | $0.91 | $0.9 |
Verdict. Trinity Large Thinking 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, Trinity Large Thinking is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Trinity Large Thinking makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 4 Maverick is strongest on Coding Index (16.3), Intelligence Index (14.5). Trinity Large Thinking leads on Coding Index (25.8), Intelligence Index (18.7).
Speed. On throughput, Trinity Large Thinking generates tokens at 314 tok/s versus 86 tok/s — about 73% faster. On time-to-first-token, Llama 4 Maverick responds in 910ms vs 1150ms, which matters most for chat-style UIs.
Provider. Meta and Arcee AI sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Arcee 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): Llama 4 Maverick costs $21.45 ($257/year); Trinity Large Thinking costs $21.00 ($252/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 4 Maverick ≈ $3.12/run, Trinity Large Thinking ≈ $3.05/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 4 Maverick ≈ $143/run, Trinity Large Thinking ≈ $140/run. Trinity Large Thinking 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
Trinity Large Thinking is cheaper overall. Its blended price (3:1 input/output ratio) is $0.41/M tokens vs $0.42/M for Llama 4 Maverick.
Trinity Large Thinking wins 2 out of 12 benchmarks compared to 0 for Llama 4 Maverick. See the detailed benchmark chart above for per-category results.
Trinity Large Thinking generates tokens faster at 314 tok/s vs 86 tok/s. Llama 4 Maverick also has lower time-to-first-token (0.91s vs 1.15s).
Choose based on your priorities: Trinity Large Thinking for lower cost, Trinity Large Thinking for stronger benchmark performance, and Trinity Large Thinking for faster generation. For latency-sensitive apps, check the TTFT comparison above.