Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.3 Instruct 70B | Celeris-1 |
|---|---|---|
| Input ($/M tokens) | $0.66 | $0.2 |
| Output ($/M tokens) | $0.72 | $0.7 |
Verdict. Llama 3.3 Instruct 70B 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, Celeris-1 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Celeris-1 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.3 Instruct 70B is strongest on Coding Index (11.9), Intelligence Index (7.7). Celeris-1 leads on Coding Index (14.4), Intelligence Index (6.3).
Speed. On throughput, Celeris-1 generates tokens at 1460 tok/s versus 87 tok/s — about 94% faster. On time-to-first-token, Celeris-1 responds in 600ms vs 1640ms, 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 3.3 Instruct 70B costs $30.60 ($367/year); Celeris-1 costs $16.50 ($198/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.3 Instruct 70B ≈ $4.74/run, Celeris-1 ≈ $2.40/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.3 Instruct 70B ≈ $204/run, Celeris-1 ≈ $110/run. Celeris-1 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
Celeris-1 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.33/M tokens vs $0.68/M for Llama 3.3 Instruct 70B.
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 87 tok/s. However, Celeris-1 has lower time-to-first-token (0.60s vs 1.64s).
Choose based on your priorities: Celeris-1 for lower cost, both perform similarly on benchmarks, and Celeris-1 for faster generation. For latency-sensitive apps, check the TTFT comparison above.