Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Nemotron Instruct 70B | MiniMax-M2.5 |
|---|---|---|
| Input ($/M tokens) | $1.2 | $0.3 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. MiniMax-M2.5 takes the aggregate benchmark matchup 1–0 across 1 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, MiniMax-M2.5 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). MiniMax-M2.5 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.1 Nemotron Instruct 70B is strongest on Intelligence Index (6.9). MiniMax-M2.5 leads on Intelligence Index (22.8).
Speed. On throughput, Llama 3.1 Nemotron Instruct 70B generates tokens at 126 tok/s versus 93 tok/s — about 26% faster. On time-to-first-token, MiniMax-M2.5 responds in 1680ms vs 3220ms, which matters most for chat-style UIs.
Provider. NVIDIA and MiniMax sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while MiniMax 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 3.1 Nemotron Instruct 70B costs $54.00 ($648/year); MiniMax-M2.5 costs $27.00 ($324/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Nemotron Instruct 70B ≈ $8.40/run, MiniMax-M2.5 ≈ $3.90/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Nemotron Instruct 70B ≈ $360/run, MiniMax-M2.5 ≈ $180/run. MiniMax-M2.5 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
MiniMax-M2.5 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.53/M tokens vs $1.20/M for Llama 3.1 Nemotron Instruct 70B.
MiniMax-M2.5 wins 1 out of 12 benchmarks compared to 0 for Llama 3.1 Nemotron Instruct 70B. See the detailed benchmark chart above for per-category results.
Llama 3.1 Nemotron Instruct 70B generates tokens faster at 126 tok/s vs 93 tok/s. However, MiniMax-M2.5 has lower time-to-first-token (1.68s vs 3.22s).
Choose based on your priorities: MiniMax-M2.5 for lower cost, MiniMax-M2.5 for stronger benchmark performance, and Llama 3.1 Nemotron Instruct 70B for faster generation. For latency-sensitive apps, check the TTFT comparison above.