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Compare/Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) vs MiMo-V2.5-Pro

Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)vsMiMo-V2.5-Pro

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

NVIDIA

Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)

Input
—
Output
—
Speed
—
TTFT
—
Xiaomi

MiMo-V2.5-Pro

Input
$0.43/M
Output
$0.87/M
Speed
41 tok/s
TTFT
6.14s

Winner by Category

Cheaper
MiMo-V2.5-Pro
Faster (tok/s)
MiMo-V2.5-Pro
Lower Latency
MiMo-V2.5-Pro
Benchmarks (0-2)
MiMo-V2.5-Pro

Pricing Comparison

MetricLlama 3.3 Nemotron Super 49B v1 (Non-reasoning)MiMo-V2.5-Pro
Input ($/M tokens)—$0.43
Output ($/M tokens)—$0.87
Cost for 1M input + 100K output tokens:
MiMo-V2.5-Pro$0.52

Speed Comparison

Output Speed (tokens/s) — higher is better
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)
—
MiMo-V2.5-Pro
41 tok/s
Time to First Token (seconds) — lower is better
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)
—
MiMo-V2.5-Pro
6.14s

Editorial Analysis

Verdict. MiMo-V2.5-Pro 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 3.3 Nemotron Super 49B v1 (Non-reasoning) is strongest on Intelligence Index (7.3). MiMo-V2.5-Pro leads on Coding Index (60.2), Intelligence Index (26.4).

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

Provider. NVIDIA and Xiaomi sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while Xiaomi 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
7.326.4
Coding Index
—60.2
Math Index
——
GPQA Diamond
——
MMLU-Pro
——
LiveCodeBench
——
AIME 2025
——
MATH-500
——
Humanity's Last Exam
——
SciCode
——
IFBench
——
TerminalBench
——
Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)0 wins
2 winsMiMo-V2.5-Pro

Frequently Asked Questions

Which is cheaper, Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) or MiMo-V2.5-Pro?

MiMo-V2.5-Pro is cheaper overall. Its blended price (3:1 input/output ratio) is $0.54/M tokens vs $—/M for Llama 3.3 Nemotron Super 49B v1 (Non-reasoning).

Which model performs better on benchmarks?

MiMo-V2.5-Pro wins 2 out of 12 benchmarks compared to 0 for Llama 3.3 Nemotron Super 49B v1 (Non-reasoning). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

MiMo-V2.5-Pro generates tokens faster at 41 tok/s vs — tok/s. However, MiMo-V2.5-Pro has lower time-to-first-token (6.14s vs —s).

When should I use Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) vs MiMo-V2.5-Pro?

Choose based on your priorities: MiMo-V2.5-Pro for lower cost, MiMo-V2.5-Pro for stronger benchmark performance, and MiMo-V2.5-Pro for faster generation. For latency-sensitive apps, check the TTFT comparison above.