DeepSeek-V4.1-Flash vs Hy3
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 43.2. Hy3 is 1.3x cheaper per token.
DeepSeek · Tencent · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 43.2, ranking #13 overall.
In the 5 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Hy3 is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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
- overall performance matters — it scores 51.8 and ranks #13 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 5 exact shared results
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Hy3
- cost matters — it's about 1.3x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 31 for Hy3
DeepSeek-V4.1-Flash outperforms in 4 benchmarks (GPQA, MathArena Apex, NL2Repo, Terminal-Bench 2.1), while Hy3 is better at 1 benchmark (Humanity's Last Exam (no tools, text-only)).
DeepSeek-V4.1-Flash significantly outperforms across most 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 ($0.22/1M tokens) is 1.6x more expensive than Hy3 ($0.14/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.1x more expensive than Hy3 ($0.58/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Hy3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 468.2B more parameters than Hy3, making it 158.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Hy3's 262,144 tokens. DeepSeek-V4.1-Flash 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 supports multimodal inputs, whereas Hy3 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
Hy3
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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 was released on 2026-09-10, while Hy3 was released on 2026-07-06.
DeepSeek-V4.1-Flash is 2 months newer than Hy3.
Sep 10, 2026
1 weeks ago
2mo newerJul 6, 2026
2 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 is available from Fireworks, DeepInfra, DeepSeek, Novita. Hy3 is available from DeepInfra.
DeepSeek-V4.1-Flash
Hy3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Hy3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Hy3.