Compare/Llama 3.1 Nemotron Instruct 70B vs MiniMax-M2.5

Llama 3.1 Nemotron Instruct 70BvsMiniMax-M2.5

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

NVIDIA

Llama 3.1 Nemotron Instruct 70B

Input
$1.2/M
Output
$1.2/M
Speed
33 tok/s
TTFT
0.39s
MiniMax

MiniMax-M2.5

Input
$0.3/M
Output
$1.2/M
Speed
47 tok/s
TTFT
1.40s

Winner by Category

Cheaper
MiniMax-M2.5
Faster (tok/s)
MiniMax-M2.5
Lower Latency
Llama 3.1 Nemotron Instruct 70B
Benchmarks (5-7)
MiniMax-M2.5

Pricing Comparison

MetricLlama 3.1 Nemotron Instruct 70BMiniMax-M2.5
Input ($/M tokens)$1.2$0.3
Output ($/M tokens)$1.2$1.2
Cost for 1M input + 100K output tokens:
Llama 3.1 Nemotron Instruct 70B$1.32
MiniMax-M2.5$0.42

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.1 Nemotron Instruct 70B
33 tok/s
MiniMax-M2.5
47 tok/s
Time to First Token (seconds) — lower is better
Llama 3.1 Nemotron Instruct 70B
0.39s
MiniMax-M2.5
1.40s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
13.441.9
Coding Index
10.837.4
Math Index
11.0
GPQA Diamond
46.5%84.8%
MMLU-Pro
69.0%
LiveCodeBench
16.9%
AIME 2025
11.0%
MATH-500
73.3%
Humanity's Last Exam
4.6%19.1%
SciCode
23.3%42.6%
IFBench
30.7%71.6%
TerminalBench
4.5%34.8%
Llama 3.1 Nemotron Instruct 70B5 wins
7 winsMiniMax-M2.5

Frequently Asked Questions

Which is cheaper, Llama 3.1 Nemotron Instruct 70B or MiniMax-M2.5?

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.

Which model performs better on benchmarks?

MiniMax-M2.5 wins 7 out of 12 benchmarks compared to 5 for Llama 3.1 Nemotron Instruct 70B. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

MiniMax-M2.5 generates tokens faster at 47 tok/s vs 33 tok/s. Llama 3.1 Nemotron Instruct 70B also has lower time-to-first-token (0.39s vs 1.40s).

When should I use Llama 3.1 Nemotron Instruct 70B vs MiniMax-M2.5?

Choose based on your priorities: MiniMax-M2.5 for lower cost, MiniMax-M2.5 for stronger benchmark performance, and MiniMax-M2.5 for faster generation. For latency-sensitive apps, check the TTFT comparison above.