DeepSeek-R1 vs DeepSeek-V4.1-Flash-1017
Comparing DeepSeek-R1 and DeepSeek-V4.1-Flash-1017 across benchmarks, pricing, and capabilities.
DeepSeek · DeepSeek · Updated for 2026
Which is better?
DeepSeek-R1 and DeepSeek-V4.1-Flash-1017 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V4.1-Flash-1017 is roughly 1.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1
- you want predictable pricing at $0.55/M input and $2.19/M output
Choose DeepSeek-V4.1-Flash-1017
- cost matters — it's about 1.8x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 11 for DeepSeek-V4.1-Flash-1017
DeepSeek-R1 and DeepSeek-V4.1-Flash-1017don'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
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1 ($0.55/1M tokens) is 1.8x more expensive than DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens).
For output processing, DeepSeek-R1 ($2.19/1M tokens) is 1.8x more expensive than DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than DeepSeek-V4.1-Flash-1017.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 92.2B more parameters than DeepSeek-R1, making it 13.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to DeepSeek-R1's 131,072 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while DeepSeek-R1 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas DeepSeek-R1 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-R1
DeepSeek-V4.1-Flash-1017
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while DeepSeek-V4.1-Flash-1017 was released on 2026-10-17.
DeepSeek-V4.1-Flash-1017 is 21 months newer than DeepSeek-R1.
Jan 20, 2025
1.7 years ago
Oct 17, 2026
2 days ago
1.7yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. DeepSeek-V4.1-Flash-1017 is available from DeepSeek.
DeepSeek-R1
DeepSeek-V4.1-Flash-1017
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and DeepSeek-V4.1-Flash-1017 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs DeepSeek-V4.1-Flash-1017.
Related comparisons
More DeepSeek-R1 comparisons
More DeepSeek-V4.1-Flash-1017 comparisons
- DeepSeek-V4.1-Flash-1017 vs MiniMax M3.1 Flash
- DeepSeek-V4.1-Flash-1017 vs Nemotron Labs Dory 1.0 (4B)
- DeepSeek-V4.1-Flash-1017 vs Muse Spark Open
- DeepSeek-V4.1-Flash-1017 vs Nemotron 3.5 Bolt (74B A9B 24GB)
- DeepSeek-V4.1-Flash-1017 vs Nemotron 3.5 Super VL (120B A12B)
- DeepSeek-V4.1-Flash-1017 vs Muse Wonder