DeepSeek-V4.1-Flash-1017 vs Hy3
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 42.6. Hy3 is 2.1x cheaper per token.
DeepSeek · Tencent · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 42.6, ranking #15 overall.
The models split the 2 individual benchmarks reported for both models evenly.
On price, Hy3 is roughly 2.1x 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-V4.1-Flash-1017
- overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Hy3
- cost matters — it's about 2.1x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 31 for Hy3
DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (NL2Repo), while Hy3 is better at 1 benchmark (Humanity's Last Exam (no tools, text-only)).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 2.1x more expensive than Hy3 ($0.14/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.1x more expensive than Hy3 ($0.58/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Hy3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 468.2B more parameters than Hy3, making it 158.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Hy3's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Hy3 is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Hy3 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
Hy3
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Hy3 uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Hy3 was released on 2026-07-06.
DeepSeek-V4.1-Flash-1017 is 3 months newer than Hy3.
Oct 17, 2026
3 days ago
3mo newerJul 6, 2026
3 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Hy3 is available from DeepInfra.
DeepSeek-V4.1-Flash-1017
Hy3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Hy3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Hy3.
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