Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 4 Scout | Celeris-1 |
|---|---|---|
| Input ($/M tokens) | $0.19 | $0.2 |
| Output ($/M tokens) | $0.68 | $0.7 |
Verdict. Llama 4 Scout and Celeris-1 split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Llama 4 Scout is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama 4 Scout makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 4 Scout is strongest on Coding Index (8.2), Intelligence Index (6.5). Celeris-1 leads on Coding Index (14.4), Intelligence Index (6.3).
Speed. On throughput, Celeris-1 generates tokens at 1460 tok/s versus 121 tok/s — about 92% faster. On time-to-first-token, Celeris-1 responds in 600ms vs 850ms, which matters most for chat-style UIs.
Provider. Meta and Celeris sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Celeris 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 Scout costs $15.90 ($191/year); Celeris-1 costs $16.50 ($198/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 4 Scout ≈ $2.31/run, Celeris-1 ≈ $2.40/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 4 Scout ≈ $106/run, Celeris-1 ≈ $110/run. Llama 4 Scout 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
Llama 4 Scout is cheaper overall. Its blended price (3:1 input/output ratio) is $0.31/M tokens vs $0.33/M for Celeris-1.
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Celeris-1 generates tokens faster at 1460 tok/s vs 121 tok/s. However, Celeris-1 has lower time-to-first-token (0.60s vs 0.85s).
Choose based on your priorities: Llama 4 Scout for lower cost, both perform similarly on benchmarks, and Celeris-1 for faster generation. For latency-sensitive apps, check the TTFT comparison above.