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DeepSeek-V4.1-Flash vs Llama-3.3 Nemotron Super 49B v1

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 13.1.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 13.1, ranking #13 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Sep 2026

Choose Llama-3.3 Nemotron Super 49B v1

  • you are already invested in the NVIDIA ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Llama-3.3 Nemotron Super 49B v1
NVIDIA
Core performance indexes
LLM Stats Score
51.8
#13
13.1
#246
Reasoning
48.9
#18
13.2
#239
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash
Llama-3.3 Nemotron Super 49B v1
Math
35.2#43
11.7#237
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 7 for Llama-3.3 Nemotron Super 49B v1

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Llama-3.3 Nemotron Super 49B v1 is better at 0 benchmarks.

DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.

Sat Sep 19 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

713.3B diff

DeepSeek-V4.1-Flash has 713.3B more parameters than Llama-3.3 Nemotron Super 49B v1, making it 1429.5% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
NVIDIA
Llama-3.3 Nemotron Super 49B v1
49.9Bparameters
763.2B
DeepSeek-V4.1-Flash
49.9B
Llama-3.3 Nemotron Super 49B v1

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
NVIDIA
Llama-3.3 Nemotron Super 49B v1
Input- tokens
Output- tokens
Sat Sep 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas Llama-3.3 Nemotron Super 49B v1 does not.

DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Llama-3.3 Nemotron Super 49B v1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Llama-3.3 Nemotron Super 49B v1 uses Llama 3.1 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash

MIT

Open weights

Llama-3.3 Nemotron Super 49B v1

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Llama-3.3 Nemotron Super 49B v1 was released on 2025-03-18.

DeepSeek-V4.1-Flash is 18 months newer than Llama-3.3 Nemotron Super 49B v1.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 weeks ago

1.5yr newer
Llama-3.3 Nemotron Super 49B v1

Mar 18, 2025

1.5 years ago

Knowledge Cutoff

When training data ends

Llama-3.3 Nemotron Super 49B v1 has a documented knowledge cutoff of 2023-12-31, while DeepSeek-V4.1-Flash's cutoff date is not specified.

We can confirm Llama-3.3 Nemotron Super 49B v1's training data extends to 2023-12-31, but cannot make a direct comparison without DeepSeek-V4.1-Flash's cutoff date.

DeepSeek-V4.1-Flash

—

Llama-3.3 Nemotron Super 49B v1

Dec 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash and Llama-3.3 Nemotron Super 49B v1 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Llama-3.3 Nemotron Super 49B v1
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Llama-3.3 Nemotron Super 49B v1.

Which is better, DeepSeek-V4.1-Flash or Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 13.1. DeepSeek-V4.1-Flash is made by DeepSeek and Llama-3.3 Nemotron Super 49B v1 is made by NVIDIA. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash compare to Llama-3.3 Nemotron Super 49B v1 in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. Llama-3.3 Nemotron Super 49B v1 scores MATH-500: 96.6%, MT-Bench: 91.7%, MBPP: 91.3%, Arena Hard: 88.3%, BFCL v2: 73.7%.

What are the context window sizes for DeepSeek-V4.1-Flash and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash supports 1.0M tokens and Llama-3.3 Nemotron Super 49B v1 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash and Llama-3.3 Nemotron Super 49B v1?

Key differences include LLM Stats Score (51.8 vs 13.1), multimodal support (yes vs no), licensing (MIT vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash is developed by DeepSeek and Llama-3.3 Nemotron Super 49B v1 is developed by NVIDIA.

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