Advertisement
Compare/Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) vs Mercury 2

Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)vsMercury 2

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

NVIDIA

Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)

Input
—
Output
—
Speed
—
TTFT
—
Inception

Mercury 2

Input
$0.25/M
Output
$0.75/M
Speed
881 tok/s
TTFT
4.60s

Winner by Category

Cheaper
Mercury 2
Faster (tok/s)
Mercury 2
Lower Latency
Mercury 2
Benchmarks (0-2)
Mercury 2

Pricing Comparison

MetricLlama 3.1 Nemotron Ultra 253B v1 (Reasoning)Mercury 2
Input ($/M tokens)—$0.25
Output ($/M tokens)—$0.75
Cost for 1M input + 100K output tokens:
Mercury 2$0.33

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
—
Mercury 2
881 tok/s
Time to First Token (seconds) — lower is better
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)
—
Mercury 2
4.60s

Editorial Analysis

Verdict. Mercury 2 takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.

Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.

Strengths. Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is strongest on Intelligence Index (7.5). Mercury 2 leads on Coding Index (31.1), Intelligence Index (11.5).

Speed. Speed data is incomplete for this pair; benchmark and price should decide.

Provider. NVIDIA and Inception sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while Inception often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.

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.

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
7.511.5
Coding Index
—31.1
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning)0 wins
2 winsMercury 2

Frequently Asked Questions

Which is cheaper, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) or Mercury 2?

Mercury 2 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.38/M tokens vs $—/M for Llama 3.1 Nemotron Ultra 253B v1 (Reasoning).

Which model performs better on benchmarks?

Mercury 2 wins 2 out of 12 benchmarks compared to 0 for Llama 3.1 Nemotron Ultra 253B v1 (Reasoning). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Mercury 2 generates tokens faster at 881 tok/s vs — tok/s. However, Mercury 2 has lower time-to-first-token (4.60s vs —s).

When should I use Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) vs Mercury 2?

Choose based on your priorities: Mercury 2 for lower cost, Mercury 2 for stronger benchmark performance, and Mercury 2 for faster generation. For latency-sensitive apps, check the TTFT comparison above.