Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Celeris-1 | Mistral Large 2 (Jul '24) |
|---|---|---|
| Input ($/M tokens) | $2 | $2 |
| Output ($/M tokens) | $6 | $6 |
Verdict. Mistral Large 2 (Jul '24) takes the aggregate benchmark matchup 7–4 across 11 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Mistral Large 2 (Jul '24) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Large 2 (Jul '24) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Celeris-1 is strongest on GPQA Diamond (63%), SciCode (21%), Coding Index (14.4). Mistral Large 2 (Jul '24) leads on MATH-500 (71%), MMLU-Pro (68%), GPQA Diamond (47%).
Speed. On throughput, Celeris-1 generates tokens at 2195 tok/s versus 0 tok/s — about 100% faster. On time-to-first-token, Mistral Large 2 (Jul '24) responds in 0ms vs 482ms, which matters most for chat-style UIs.
Provider. Celeris and Mistral sell to overlapping but distinct developer audiences: Celeris tends to ship frontier reasoning models with premium positioning, while Mistral 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): Celeris-1 costs $150.00 ($1800/year); Mistral Large 2 (Jul '24) costs $150.00 ($1800/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Celeris-1 ≈ $22.00/run, Mistral Large 2 (Jul '24) ≈ $22.00/run. At agent/realtime scale (200M input / 100M output per million requests): Celeris-1 ≈ $1000/run, Mistral Large 2 (Jul '24) ≈ $1000/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.
Mistral Large 2 (Jul '24) wins 7 out of 12 benchmarks compared to 4 for Celeris-1. See the detailed benchmark chart above for per-category results.
Celeris-1 generates tokens faster at 2195 tok/s vs 0 tok/s. However, Mistral Large 2 (Jul '24) has lower time-to-first-token (0.00s vs 0.48s).
Choose based on your priorities: both are similarly priced, Mistral Large 2 (Jul '24) for stronger benchmark performance, and Celeris-1 for faster generation. For latency-sensitive apps, check the TTFT comparison above.