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DeepSeek-V4.1-Flash-1017 vs Llama-3.3 Nemotron Super 49B v1

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 12.6.

DeepSeek · NVIDIA · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 12.6, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 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-1017
DeepSeek
Llama-3.3 Nemotron Super 49B v1
NVIDIA
Core performance indexes
LLM Stats Score
51.2
#15
12.6
#272
Reasoning
48.8
#18
12.8
#264
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Llama-3.3 Nemotron Super 49B v1
Math
31.8#69
11.6#250
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 7 for Llama-3.3 Nemotron Super 49B v1

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Llama-3.3 Nemotron Super 49B v1don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

713.3B diff

DeepSeek-V4.1-Flash-1017 has 713.3B more parameters than Llama-3.3 Nemotron Super 49B v1, making it 1429.5% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
NVIDIA
Llama-3.3 Nemotron Super 49B v1
49.9Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
49.9B
Llama-3.3 Nemotron Super 49B v1

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
NVIDIA
Llama-3.3 Nemotron Super 49B v1
Input- tokens
Output- tokens
Fri Oct 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Llama-3.3 Nemotron Super 49B v1 does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Llama-3.3 Nemotron Super 49B v1

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 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-1017

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-1017 was released on 2026-10-17, while Llama-3.3 Nemotron Super 49B v1 was released on 2025-03-18.

DeepSeek-V4.1-Flash-1017 is 19 months newer than Llama-3.3 Nemotron Super 49B v1.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

3 days ago

1.6yr newer
Llama-3.3 Nemotron Super 49B v1

Mar 18, 2025

1.6 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-1017'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-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

Llama-3.3 Nemotron Super 49B v1

Dec 2023

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Llama-3.3 Nemotron Super 49B v1 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Llama-3.3 Nemotron Super 49B v1
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Llama-3.3 Nemotron Super 49B v1.

Which is better, DeepSeek-V4.1-Flash-1017 or Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 12.6. DeepSeek-V4.1-Flash-1017 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-1017 compare to Llama-3.3 Nemotron Super 49B v1 in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. 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-1017 and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash-1017 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-1017 and Llama-3.3 Nemotron Super 49B v1?

Key differences include LLM Stats Score (51.2 vs 12.6), 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-1017 and Llama-3.3 Nemotron Super 49B v1?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Llama-3.3 Nemotron Super 49B v1 is developed by NVIDIA.

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