DeepSeek V4.1-Flash
A lightweight Flash-series model released by DeepSeek, designed for high-speed inference and low latency. It shows notable improvements over its predecessor in code generation and multimedia generation capabilities, with overall performance exceeding the V4-Pro version.
Core Facts
The DeepSeek Flash version is a distilled model optimized for low-latency scenarios, trading some complex reasoning capability for faster response speeds and lower token unit prices.
DeepSeek V4.1 Flash claims to achieve breakthroughs across three dimensions: capability, speed, and cost.
DeepSeek-V4.1-Flash's KV cache is compressed to 890 bytes per token, only one-quarter that of V4 Flash.
Timeline (last 90 days)
Scaleway's Generative APIs offer more than 40 available models, with LightOn OCR 3, DeepSeek V4.1-Flash and GLM 5.3 newly added this time.
The experiment used DeepSeek V4.1-Flash as the execution model; the cliff effect is more pronounced with weaker models, and the same file may hurt one model without hurting another.
The model weights of DeepSeek-V4.1-Flash-1017 are open-sourced under the MIT license on Hugging Face.
In the third round of routing tests, using the weak model DeepSeek V4.1-Flash, across 480 routing judgments, all failures in the pipeline group manifested as the model refusing to trigger rather than choosing the wrong skill; after removing the boundary negation clause from the description, such failures dropped from 10 to 0.
After DeepSeek halved the compression step from 4 to 2 in V4.1-Flash, the needle-in-a-haystack accuracy gap dropped from 40% to 6.1%, but the problem was not eradicated.
Under the premise of identical task completion quality, the Astra solution costs $0.43 per task while the DeepSeek V4.1-Flash solution costs $6.52 per task, a difference of more than 15 times.
DeepSeek-V4.1-Flash's KV cache is compressed to 890 bytes per token, only one-quarter that of V4 Flash.
DeepSeek V4.1-Flash produced graphics with proper layout and basically usable structure in the first generation, but with detail flaws such as incorrect leg and wing proportions and extreme knee bending.
This hands-on test reflects a single-use impression and does not include a horizontal benchmark comparison against previous versions of DeepSeek or other models.
The "Flash" naming convention typically indicates a lightweight version focused on speed and efficiency.
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All Facts (20)
The DeepSeek Flash version is a distilled model optimized for low-latency scenarios, trading some complex reasoning capability for faster response speeds and lower token unit prices.
90%VerifiedDeepSeek V4.1 Flash claims to achieve breakthroughs across three dimensions: capability, speed, and cost.
75%VerifiedDeepSeek-V4.1-Flash's KV cache is compressed to 890 bytes per token, only one-quarter that of V4 Flash.
65%VerifiedThe browser operating system interface implemented by DeepSeek V4.1 Flash presents a dynamic desktop with animation effects, features refined popup designs, and Dock bar applications can be invoked normally.
65%VerifiedV4.1-Flash scores 1 point higher than a GPT-5.6-level model in comparison.
65%VerifiedCode benchmark tests primarily cover algorithm problems and function-level code generation; a high benchmark score does not directly equate to strong real-world engineering capability.
65%VerifiedDeepSeek has released an experimental multimodal model, DeepSeek V4 Flash Vision EXP, which performs excellently in both text and multimodal tasks.
90%UnverifiedScaleway's Generative APIs offer more than 40 available models, with LightOn OCR 3, DeepSeek V4.1-Flash and GLM 5.3 newly added this time.
50%UnverifiedThe experiment used DeepSeek V4.1-Flash as the execution model; the cliff effect is more pronounced with weaker models, and the same file may hurt one model without hurting another.
50%UnverifiedThe model weights of DeepSeek-V4.1-Flash-1017 are open-sourced under the MIT license on Hugging Face.
50%UnverifiedIn the third round of routing tests, using the weak model DeepSeek V4.1-Flash, across 480 routing judgments, all failures in the pipeline group manifested as the model refusing to trigger rather than choosing the wrong skill; after removing the boundary negation clause from the description, such failures dropped from 10 to 0.
50%UnverifiedAfter DeepSeek halved the compression step from 4 to 2 in V4.1-Flash, the needle-in-a-haystack accuracy gap dropped from 40% to 6.1%, but the problem was not eradicated.
50%UnverifiedUnder the premise of identical task completion quality, the Astra solution costs $0.43 per task while the DeepSeek V4.1-Flash solution costs $6.52 per task, a difference of more than 15 times.
50%UnverifiedThe "Flash" naming convention typically indicates a lightweight version focused on speed and efficiency.
50%UnverifiedIn the third iteration, DeepSeek V4.1-Flash showed minimal improvement in optimizing the chainring area, with almost no change.
50%UnverifiedIn the second iteration, DeepSeek V4.1-Flash accurately understood subjective descriptions such as "smooth" and improved the leg lines to be smoother.
50%UnverifiedDeepSeek V4.1-Flash produced graphics with proper layout and basically usable structure in the first generation, but with detail flaws such as incorrect leg and wing proportions and extreme knee bending.
50%UnverifiedDeepSeek V4.1-Flash shows a marked improvement in output quality compared to its predecessor in areas such as CSS layout and responsive design.
50%UnverifiedThe most significant improvements in DeepSeek V4.1-Flash are concentrated in the area of frontend development.
50%UnverifiedDeepSeek V4.1-Flash's generation speed is very fast, leaving a strong impression on evaluators during benchmarking.
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