Models › DeepSeek-V4.1 family

DeepSeek-V4.1-Flash

deepseek-ai MIT out 45 days ago

763B parameters (6 of 384 experts per token), images and text in, text out, 1M tokens of context. 34 providers serve it from $0.020 per million input tokens.

Verified 2026-10-25 Rank #10 in LLMs · score 129.4 method v1 Sources 5 Downloads 30d 1.6M · #79 of 619 tracked

Changed this week: Cheapest input price rose 148%: $0.020 → $0.050 per 1M (OpenRouter).2026-10-24 · 12 new quantized builds on the Hub (137 in all).2026-10-24 · 1 new provider: Together AI.2026-10-23

Can I run it?458GB at INT4Estimateneeds a multi-GPU nodemethodCommunity: Show HN: Warp – Run DeepSeek v4.1 Flash with 5 GB of RAM at 3.77 tok/s →
What does it cost?$0.020/1M incheapest of 32OpenInferencefp42026-10-24
Can I use it?MITPermissivelicence file2026-10-24
Do people use it?1.6Mdownloads, 30 days#79 of 619 we track · top 13%+131 likes, downloads +20% this week4,720 likesHugging Face2026-10-24

The ringer: four playful tests

A pelican steals a bicycle from a supermarket

as received✕ no SVG

No drawable SVG in the reply.

Draw a pelican stealing a bicycle from a supermarket. Reply with one complete SVG document only, no prose and no code fences. Use viewBox="0 0 200 150" and a transparent background. Use only these colours for every fill and stroke: #06e0c0 #ffc298 #cd85f9 #f1f2f2.

Finish the dad joke

as received● 1 line · 10 words

Why did the open-weights model cross the road?

To get to the other side without an API key.

Finish this dad joke. Reply with the punchline only, on one line, without quotes: Why did the open-weights model cross the road?

A parasailer, in ASCII

as received● 10×32 fits 12×32
            ______
          /        \
         /          \
        |            |
         \          /
          \________/
             |  |
             |  |
              /o\
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Draw ASCII art of a parasailer over the sea. At most 12 lines and 32 columns. Reply with the art only: no code fences, no explanation.

Animate a bouncing ball

as received● 8 frames · 16×16
Make a 16x16 pixel sprite animation of a ball bouncing, 8 frames. Reply with JSON only, no code fences: {"palette": {"a": "#rrggbb", ...}, "fps": <number>, "frames": [[16 strings of 16 characters], ...]}. At most 4 palette colours with single-letter keys; "." means transparent; exactly 8 frames.
1 of 4 · swipe for more

The ringer: the same four playful tests for every model, replies shown as received and checked by code. A joke, not a benchmark; never ranked.3 of 4 pass · parasail-deepseek-v41-flash on parasail · run 2026-10-09 · temperature 0 · cost $0.036 · 2026-10 thing: supermarket · all models →

Worth reading

Picked by rule from 26 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post

DeepSeek-v4.1-Flash: smarter, faster, more efficient

DeepSeek launches V4.1-Flash, the smallest model in its new architecture family with native visual understanding and a 552B-parameter MoE design.

deepseek.com 2026-09-10 1 HN pts · 1 comments 293 words
Latest · 38 days ago

DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression

DeepSeek-V4.1 Flash is a 552B multimodal model that pushes KV cache compression to handle long-horizon agent workflows with contexts up to 1 million tokens.

  • The model abandons block-based high-compression structures in favor of optimized CSA2 for KV Cache reduction.
  • The Encoder merges 2 adjacent positions into 1 cache entry using channel-wise learned weights while the Decoder retains per-position entries.
zartbot.github.io 2026-09-17 131 HN pts · 10 comments 12.6k words

25 best of 26 tracked

The 3 picks above, plus these 22. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. DiscussionDeepSeek v4.1 Flashtwitter.com · 2026-09-10 · 1,016 HN pts
  2. ArticleDeepSeek v4.1 Flash Is Now Our Best Hacking Modelenclave.ai · 2026-09-16 · 177 HN pts
  3. CodeDeepSeek v4.1 Flash (518GB, 4-bit) on a 128GB MacBook: 2.7x prefill, 17 tok/sgithub.com · 2026-09-15 · 14 HN pts
  4. DiscussionDeepSeek v4.1 Flash is now available for internal beta testingnews.ycombinator.com · 2026-09-08 · 22 HN pts
  5. DiscussionDeepSeek v4.1 flash runs 23 seconds/token on a 2020 16gb M1 Mac Minitwitter.com · 2026-09-12 · 22 HN pts
  6. ArticleDeepSeek v4.1 Flash – Artificial Analysisartificialanalysis.ai · 2026-09-10 · 4 HN pts
  7. ArticleSGLang and Miles Add Day-0 Support for DeepSeek-v4.1lmsys.org · 2026-09-16 · 1 HN pts
  8. ArticleDeepSeek v4.1-Flash on Openrouteropenrouter.ai · 2026-09-10
  9. ArticleDeepSeek-V4.1-Flash: more efficient prefill for coding agentsbaseten.co · 2026-09-11
  10. ArticleDeepSeek v4.1 Flash avg 102 tps on 4x RTX6000 pro max-q, 2.1x up from v4-flashforum.level1techs.com · 2026-09-18
  11. DiscussionAsk HN: DeepSeek v4.1 Flash on Local hardware, what tok/s do you see?news.ycombinator.com · 2026-09-19
  12. ArticleDeepSeek v4.1 Flash vs. ChatGPT Pro Subscriptionaicharts.io · 2026-09-10
  13. DiscussionOpenCode: $60 of usage for DeepSeek v4.1 Flash is now permanenttwitter.com · 2026-09-25
  14. ArticleFast single-box DeepSeek v4.1 Flash runtimetangled.org · 2026-10-07
  15. ArticleDeepSeek v4.1 Flash Beta on Vercelvercel.com · 2026-09-09
  16. ArticleDeepSeek v4.1 Flash at 378 tok/s 99.7% Cache hit rateruninfra.ai · 2026-10-07
  17. ArticleSetting Up Pi with DeepSeek v4.1 Flash on OpenRoutervincentschmalbach.com · 2026-09-16
  18. ArticleDeepSeek V4.1 Flash: Benchmarks, Capabilities, and Architecturegingerlabs.ai · 2026-09-15
  19. ArticleDeepSeek V4.1 Flash Review — Frontier Agentic Scores at Flash Pricing | AI Agents & Agentic Workflows Guideagentguides.dev · 2026-09-13
  20. ArticleDeepSeek V4.1 Flash: New Base Model, Not a Point Releaseorcarouter.ai · 2026-09-10
  21. ArticleDeepSeek V4.1 Flash Review: Price & Benchmarks · OmniaKeyomniakey.com · 2026-09-10
  22. ArticleDeepSeek-V4.1-Flash vs V4-Pro vs V4-Flash: Benchmarks, Cost, and Speed Comparedapidog.com · 2026-09-10

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

Prices for the same weights vary 23×

32 providers with a live price, from $0.020 to $0.45 per million input tokens; the median is $0.21. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00OpenInference: $0.020 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashSail Research: $0.050 in / $0.15 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashBaidu: $0.054 in / $0.21 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashRelace: $0.070 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashDecart: $0.090 in / $0.18 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashInferenceNet: $0.10 in / $0.71 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashReka: $0.10 in / $0.84 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashAtlasCloud: $0.14 in / $0.54 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashStreamLake: $0.15 in / $0.59 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashCoreWeave: $0.15 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashDigitalOcean: $0.17 in / $0.66 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashAlibaba: $0.17 in / $0.66 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashModelRun: $0.17 in / $0.67 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashGMICloud: $0.18 in / $0.72 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashNovita: $0.20 in / $0.78 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingMancer 2: $0.20 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashFireworks AI: $0.21 in / $0.84 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingPhala: $0.21 in / $0.84 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashWafer: $0.22 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashMakora: $0.26 in / $1.17 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashCrusoe: $0.29 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashTogether AI: $0.30 in / $1.20 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingBaseten: $0.30 in / $1.20 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingParasail: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashNebius: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flash+5Morph: $0.45 in / $0.60 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashcheapest $0.020median $0.21
$0.010$0.10$1.00$10.00OpenInference: $0.020 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashSail Research: $0.050 in / $0.15 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashBaidu: $0.054 in / $0.21 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashRelace: $0.070 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashDecart: $0.090 in / $0.18 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashInferenceNet: $0.10 in / $0.71 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashReka: $0.10 in / $0.84 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashAtlasCloud: $0.14 in / $0.54 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashStreamLake: $0.15 in / $0.59 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashCoreWeave: $0.15 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashDigitalOcean: $0.17 in / $0.66 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashAlibaba: $0.17 in / $0.66 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashModelRun: $0.17 in / $0.67 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashGMICloud: $0.18 in / $0.72 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashNovita: $0.20 in / $0.78 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingMancer 2: $0.20 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashFireworks AI: $0.21 in / $0.84 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingPhala: $0.21 in / $0.84 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashWafer: $0.22 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashMakora: $0.26 in / $1.17 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashCrusoe: $0.29 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashTogether AI: $0.30 in / $1.20 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingBaseten: $0.30 in / $1.20 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V4.1-Flash?expand[]=inferenceProviderMappingParasail: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingGoogle: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashNebius: $0.30 in / $1.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flash+5Morph: $0.45 in / $0.60 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v4.1-flashcheapest $0.020 · median $0.21 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 OpenInference $0.020 $1.00 1M fp4 Oct 24
2 Sail Research $0.050 $0.15 1M fp4 Oct 24
3 Baidu $0.054 $0.21 1M fp8 Oct 24
4 Relace $0.070 $0.50 1M unknown Oct 24
5 Decart $0.090 $0.18 1M fp4 Oct 24

All 34 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Widely served (34 API providers); heavily run locally (137 quantized builds).

  • As a service34Alibaba, AtlasCloud, Baidu, Baseten +30
  • On own hardware137quantized builds
  • Built on2525 fine-tunes · 0 adapters · 0 merges
  • In apps62Hugging Face Spaces

Top Spaces running it: townbox/deepseek-profile-guide ♥ 6 · bagegegegw/DeepSeek-V4.1-Flash-UNCENSORED-Demo ♥ 4 · ArtShumov/Whyx-PROmpTea ♥ 3 · akhaliq/DeepSeek-V4.1-Flash ♥ 2

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 458 GB at INT4, 916 GB at FP8, 1.83 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 458 GB of weights (estimate; KV cache and activations extra)INT4458 GBFP8: about 916 GB of weights (estimate; KV cache and activations extra)FP8916 GBBF16: about 1.83 TB of weights (estimate; KV cache and activations extra)BF161.83 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 458 GB of weights (estimate; KV cache and activations extra)INT4458 GBFP8: about 916 GB of weights (estimate; KV cache and activations extra)FP8916 GBBF16: about 1.83 TB of weights (estimate; KV cache and activations extra)BF161.83 TB

Estimate params × bytes per param × 1.2 · how we estimate

Day-one receipts

Claim-matched tests, graded by code. Receipts, not a score.

Speed run● pass
{
  "providers": [
    {
      "provider": "parasail",
      "endpoint_model": "parasail-deepseek-v41-flash",
      "source_url": "https://api.parasail.io/v1/chat/completions",
      "ttft_ms_median": 2250,
      "ttft_any_ms_median": 352,
      "tokens_per_s_median": 258.9,
      "runs": [
        {
          "ttft_s": 2.397,
          "ttft_any_s": 0.352,
          "tokens_per_s": 257.3,
          "latency_s": 2.73,
          "tokens_out": 702,
          "reasoning_chars": 2440,
          "finish": "stop"
        },
        {
          "ttft_s": 2.25,
          "ttft_any_s": 0.353,
          "tokens_per_s": 258.9,
          "latency_s": 2.73,
          "tokens_out": 708,
          "reasoning_chars": 2409,
          "finish": "stop"
        },
        {
          "ttft_s": 1.901,
          "ttft_any_s": 0.346,
          "tokens_per_s": 295.3,
          "latency_s": 2.35,
          "tokens_out": 694,
          "reasoning_chars": 2423,
          "finish": "stop"
        }
      ]
    }
  ],
  "note": "Single client, wall clock; tok/s counts every output token including hidden reasoning."
}
Three-tool errand● pass
{
  "calls": [
    {
      "name": "get_weather",
      "args": {
        "city": "Lisbon",
        "date": "2026-10-14"
      }
    },
    {
      "name": "check_calendar",
      "args": {
        "date": "2026-10-14"
      }
    },
    {
      "name": "book_table",
      "args": {
        "restaurant": "Cervejaria Ramiro",
        "date": "2026-10-14",
        "time": "19:30",
        "party_size": 2
      }
    }
  ],
  "checks": {
    "get_weather": true,
    "check_calendar": true,
    "book_table": true
  },
  "final": "Done! Here's the summary:\n\n- **Weather (Lisbon, 2026-10-14):** Clear, 22°C high, only 5% rain probability — no rain, so we proceeded.\n- **Calendar:** Free evening slot 19:30–21:30.\n- **Booking:** Cervejaria Ramiro, table for 2, Wednesday 2026-10-14 at 19:30.\n\n**Confirmation code: TBL-4471**",
  "fixture": "receipts.errand.v1"
}
calls in order · args 3/3 · confirmed · 2026-10-091.9 s
Messy addresses to JSON● pass
{
  "fields_matched": 80,
  "fields_total": 80,
  "cost_per_1000_usd": 0.2766,
  "parsed": true,
  "per_item": [
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    },
    {
      "street": true,
      "city": true,
      "postcode": true,
      "country": true
    }
  ],
  "sample": [
    {
      "street": "1600 Amphitheatre Parkway",
      "unit": null,
      "city": "Mountain View",
      "region": "CA",
      "postcode": "94043",
      "country": "US"
    },
    {
      "street": "10 Downing Street",
      "unit": null,
      "city": "London",
      "region": null,
      "postcode": "SW1A 2AA",
      "country": "GB"
    },
    {
      "street": "Rua Augusta 274",
      "unit": null,
      "city": "Lisboa",
      "region": null,
      "postcode": "1100-053",
      "country": "PT"
    }
  ],
  "fixture": "receipts.addresses.v1"
}
80/80 fields · $0.2766 per 1,000 · 2026-10-0918.4 s
Needle heatmap● pass
{
  "lengths_k": [
    8,
    32,
    64
  ],
  "depths": [
    0.1,
    0.5,
    0.9
  ],
  "cells": [
    [
      1,
      1,
      1
    ],
    [
      1,
      1,
      1
    ],
    [
      1,
      1,
      1
    ]
  ],
  "detail": [
    {
      "k": 8,
      "depth": 0.1,
      "recalled": true,
      "reply": "amber-falcon-7731",
      "latency_s": 1.54,
      "finish": "stop"
    },
    {
      "k": 8,
      "depth": 0.5,
      "recalled": true,
      "reply": "cobalt-marten-7828",
      "latency_s": 0.6,
      "finish": "stop"
    },
    {
      "k": 8,
      "depth": 0.9,
      "recalled": true,
      "reply": "saffron-newt-7925",
      "latency_s": 0.64,
      "finish": "stop"
    },
    {
      "k": 32,
      "depth": 0.1,
      "recalled": true,
      "reply": "jade-lynx-8022",
      "latency_s": 1.57,
      "finish": "stop"
    },
    {
      "k": 32,
      "depth": 0.5,
      "recalled": true,
      "reply": "umber-kestrel-8119",
      "latency_s": 1.38,
      "finish": "stop"
    },
    {
      "k": 32,
      "depth": 0.9,
      "recalled": true,
      "reply": "coral-heron-8216",
      "latency_s": 0.82,
      "finish": "stop"
    },
    {
      "k": 64,
      "depth": 0.1,
      "recalled": true,
      "reply": "slate-badger-8313",
      "latency_s": 2.53,
      "finish": "stop"
    },
    {
      "k": 64,
      "depth": 0.5,
      "recalled": true,
      "reply": "ochre-otter-8410",
      "latency_s": 2.27,
      "finish": "stop"
    },
    {
      "k": 64,
      "depth": 0.9,
      "recalled": true,
      "reply": "teal-ibis-8507",
      "latency_s": 1.47,
      "finish": "stop"
    }
  ]
}

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2026-09-10), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

162 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

162 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

137 quantized (Hugging Face model tree)25 fine-tunes (Hugging Face model tree)137 quantized25 fine-tunes
137 quantized (Hugging Face model tree)25 fine-tunes (Hugging Face model tree)137 quantized25 fine-tunes

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

27 rows

Benchmarks 27 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (27)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AGIEval (EM)83.4DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported BBEH · Reason through hard problems27.2DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported BBH (EM)86.1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported BigCodeBench · Write code with agents60.6 pass@1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported C-Eval · Work across languages92.1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported CVBench (EM)77.9DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported Codeforces · Write code with agents3471 ratingDeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
author-reported DROP (F1)87.9DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported DocVQA · Extract text from documents95.6DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported GPQA Diamond · Reason through hard problems90.9 pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
author-reported GSM8K · Reason through hard problems93DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported HLE (Pass@1)36.8 (39.1†) pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
author-reported HellaSwag · Chat and follow instructions87.2DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported HumanEval · Write code with agents79.4 pass@1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported LongBench v2 · Read long documents45.2DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported MATH-500 · Reason through hard problems61.1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported MATH-500 · Reason through hard problems65.6 pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
author-reported MGSM · Work across languages80.2DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported MMLU-Pro · Reason through hard problems74.1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported MMMU-Pro · Understand images and video56.5DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported MultiLoKo (LLM-Judge)45.5DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported RefCOCO · Understand images and video86DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported SimpleQA · Reason through hard problems42.3DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported SuperGPQA · Reason through hard problems53.1DeepSeek-V4.1-Flash-Basemodel author2026-10-07
author-reported Terminal-Bench · Write code with agents90.6 pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
Show all 27 rows (2 more)
BenchmarkValueSetting / notesReported byDate
author-reported Terminal-Bench · Write code with agents30 pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07
author-reported Terminal-Bench · Write code with agents31.2 pass@1DeepSeek-V4.1-Flash-Instruct (Max reasoning effort)model author2026-10-07

What the card says

1 cited sentence

Summary generated, cited

The model uses a Causal Encoder-Decoder architecture and is released under the MIT license.[1][2]

  1. “Causal Encoder-Decoder (CED) architecture”
  2. license

Generated 2026-10-07 14:59 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

8 use cases

Facts

22 fields

Listed in LLMs

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesDeepseekV41ForCausalLM
Model typedeepseek_v41
Dtypebfloat16
Params by dtypeBF16: 1,976,441,856 · F32: 42,307,282 · F8_E4M3: 204,015,223,296 · I8: 557,171,343,360
Size bandXXL
MoEyes
Experts384
Experts per token6
Context length1,048,576 (config.json max_position_embeddings)
LicenseMIT (mit)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DeepseekV41ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · DeepseekV41ForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for DeepSeek-V4.1-Flash: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.83 TB · Cluster (≥640 GB)
FP8 (1 byte/param)916 GB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)458 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

40 variants

Lineage

Original release (shipped quantized by the lab) Family: deepseek-ai/DeepSeek-V4.1 162 derivatives on the Hub

quantized: 137finetune: 25

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
antirez/deepseek-v4.1-flash-gguf quantized gguf — — mit antirez 79 2.3M
dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8 quantized -FP8 763B XXL mit dealignai 758 88.9k
pfeifferj/DeepSeek-V4.1-Flash-GSQ-RCO-GGUF quantized GGUF — — mit pfeifferj 22 76.4k
vcruz305/DeepSeek-V4.1-Flash-GGUF quantized GGUF — — mit vcruz305 44 64.4k
kyuz0/deepseek-v4.1-flash-gguf quantized gguf — — mit kyuz0 4 60.5k
Lucebox/DeepSeek-V4.1-Flash-ROCMFP23-GGUF quantized GGUF — — mit Lucebox 10 47.2k
nvidia/DeepSeek-V4.1-Flash-NVFP4 quantized NVFP4 763B XXL mit nvidia 119 37.5k
smalinin/DeepSeek-V4.1-Flash-GGUF quantized GGUF — — mit smalinin 3 28.5k
apetersson/DeepSeek-V4.1-Flash-MixedQ2-GGUF quantized GGUF — — mit apetersson 5 25.4k
Jundot/DeepSeek-V4.1-Flash-oQ4e-mtp quantized — 129B XXL mit Jundot 8 14.3k
drowzeys/DeepSeek-V4.1-Flash-Abliterated-Cybersecurity-Unleashed finetune — — — mit drowzeys 212 0
TensorFold/DeepSeek-V4.1-Flash-MLX-2bit-MTP quantized MLX 763B XXL mit TensorFold 5 9k
pipenetwork/DeepSeek-V4.1-Flash-MLX-mixed-4_8bit quantized MLX 379B XXL mit pipenetwork 4 7.8k
mxxm-t/DeepSeek-V4.1-Flash-GGUF quantized GGUF — — mit mxxm-t 2 7.1k
inferencerlabs/DeepSeek-V4.1-Flash-MLX-Q4i quantized MLX — — — inferencerlabs 4 6.6k
Mia-AiLab/DeepSeek-V4.1-Flash-EXL3-2.9bpw quantized EXL3 105B XXL mit Mia-AiLab 22 4.9k
GreenBitAI/DeepSeek-V4.1-Flash-4bit-paged quantized -4bit 8.78B M mit GreenBitAI 1 5.9k
JigSawPT/DeepSeek-V4.1-Flash-DSpark-GGUF quantized GGUF — — mit JigSawPT 5 5.1k
Jundot/DeepSeek-V4.1-Flash-oQ3e-mtp quantized — 112B XXL mit Jundot 5 4.8k
rapid-mlx/DeepSeek-V4.1-Flash-REAP-2bit-MLX quantized MLX 681B XXL mit rapid-mlx 1 4.8k
dealignai/DeepSeek-V4.1-Flash-UNCENSORED-EXL3-2.9bpw quantized EXL3 105B XXL mit dealignai 47 2.3k
OpensourceWTF/DeepSeek-V4.1-Flash-MTPLX-streaming-q2 quantized — 22.7B L mit OpensourceWTF 4 4.1k
pipenetwork/DeepSeek-V4.1-Flash-MLX-mixed-4_8bit-engram6 quantized MLX 428B XXL mit pipenetwork 2 4.1k
rapid-mlx/DeepSeek-V4.1-Flash-DSpark-4d2e-MLX quantized MLX 7.43B M mit rapid-mlx 2 3.9k
Solstice-AI/DeepSeek-V4.1-Flash-GGUF quantized GGUF — — mit Solstice-AI 2 3.7k
nanguoyu/DeepSeek-V4.1-Flash-minirun quantized — — — mit nanguoyu 0 3.7k
kernelpool/DeepSeek-V4.1-Flash-MXFP4-GGUF quantized MXFP4 — — mit kernelpool 0 3.3k
OpensourceWTF/DeepSeek-V4.1-Flash-streaming-repack-exl3-3.0bpw quantized exl3 22.7B L mit OpensourceWTF 5 2.9k
JigSawPT/DeepSeek-V4.1-Flash-GGUF quantized GGUF — — mit JigSawPT 8 2.7k
AMAImedia/DeepSeek-V4.1-Flash-1017-FP8-GGUF quantized -FP8 763B XXL mit AMAImedia 1 2.8k
0xSojalSec/DeepSeek-V4.1-Flash-MLX-MAC quantized MLX 763B XXL mit 0xSojalSec 0 2.7k
pokey2025/DeepSeek-V4.1-Flash-ik-llama-GGUF quantized GGUF — — mit pokey2025 1 2.5k
s-zaizen/DeepSeek-V4.1-Flash-Abliterated finetune — 763B XXL mit s-zaizen 16 1.7k
orcarouter/DeepSeek-V4.1-Flash-Uncensored-MLX quantized MLX — — mit orcarouter 48 0
pipenetwork/DeepSeek-V4.1-Flash-REAP25-MLX-mixed-4_8bit quantized MLX 311B XXL mit pipenetwork 0 2.3k
pipenetwork/DeepSeek-V4.1-Flash-REAP50-MLX-mixed-4_8bit quantized MLX 243B XXL mit pipenetwork 0 2.3k
LibertAIDAI/DeepSeek-V4.1-Flash-NVFP4 quantized NVFP4 386B XXL mit LibertAIDAI 13 1.4k
Lucebox/DeepSeek-V4.1-Flash-DSpark-GGUF quantized GGUF — — mit Lucebox 1 1.7k
bot-lab-21/DeepSeek-V4.1-Flash-EXL3-3.5bpw-Pollard quantized EXL3 332B XXL mit bot-lab-21 5 1.5k
amd/DeepSeek-V4.1-Flash-Quark-MXFP4 quantized MXFP4 484B XXL mit amd 1 1.6k

Repository changelog

17 commits
changed in the last 7 daysNew providers: 2026-10-23Price moves: 2026-10-24Other: 2026-10-24all changes

Repository changelog 17 commits

DateCommitSHA
2026-10-16 fix(encoding): preserve tool image order and current-turn thinking 314113f
2026-10-12 gitignore 5254448
2026-10-12 Delete files inference/__pycache__/ with huggingface_hub 12b5c3a
2026-10-12 update 4dd75a8
2026-10-12 Restore each indexer's K cache on incomplete compression steps (#12) 8b3cec7
2026-10-01 Adding chat template (#68) 2cba9e4
2026-09-10 fix(encoding): preserve tool namespaces in prompts and completions dba1be0
2026-09-10 Update README.md & encoding/README.md df42c10
2026-09-10 Update README.md & encoding/README.md fb2764a
2026-09-10 Update inference/README.md 2bc89ac
2026-09-10 Update inference/README.md cae409b
2026-09-10 Upload DeepSeek_V41_Tech_Report.pdf 53e70b1
2026-09-10 Upload DeepSeek_V41_Tech_Report.pdf 9574840
2026-09-10 Upload DeepSeek_V41_Tech_Report.pdf 97fcff4
2026-09-10 init 517ef62
2026-09-10 Add files using upload-large-folder tool 678ae2a
2026-09-10 initial commit 3fb9e06

All providers

34 providers · 34 endpoints

Where to run 34 providers · 34 endpoints

Hosted endpoints observed for DeepSeek-V4.1-Flash, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.02/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
OpenInference
openrouter
deepseek/deepseek-v4.1-flash 1M fp4 $0.02 $1.00 — — yes live 2026-10-24
Sail Research
openrouter
deepseek/deepseek-v4.1-flash 1M fp4 $0.05 $0.15 — — yes live 2026-10-24
Baidu
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.05 $0.21 — — yes live 2026-10-24
Relace
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.07 $0.50 — — yes live 2026-10-24
Decart
openrouter
deepseek/deepseek-v4.1-flash 1M fp4 $0.09 $0.18 — — yes live 2026-10-24
InferenceNet
openrouter
deepseek/deepseek-v4.1-flash 1016K fp8 $0.10 $0.71 — — yes live 2026-10-24
Reka
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.10 $0.84 — — yes live 2026-10-24
DeepInfra
hf, deepinfra, openrouter
deepseek-ai/DeepSeek-V4.1-Flash 1M fp8 $0.11 $0.34 87.3 438 ms yes status -5 2026-10-25
AtlasCloud
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.14 $0.54 — — yes live 2026-10-24
StreamLake
openrouter
deepseek/deepseek-v4.1-flash 1000K fp8 $0.15 $0.59 — — yes live 2026-10-24
CoreWeave
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.15 $0.65 — — yes live 2026-10-24
DigitalOcean
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.17 $0.66 — — yes live 2026-10-24
Alibaba
openrouter
deepseek/deepseek-v4.1-flash 977K unknown $0.17 $0.66 — — yes live 2026-10-24
ModelRun
openrouter
deepseek/deepseek-v4.1-flash 1M fp4 $0.17 $0.67 — — yes live 2026-10-24
GMICloud
openrouter
deepseek/deepseek-v4.1-flash 1024K fp8 $0.18 $0.72 — — yes live 2026-10-24
Novita
hf, openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.20 $0.78 79.4 1093 ms yes live 2026-10-25
Mancer 2
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.20 $1.00 — — yes live 2026-10-24
Fireworks AI
hf, openrouter
accounts/fireworks/models/deepseek-v4p1-flash 1M unknown $0.21 $0.84 95.8 589 ms yes live 2026-10-25
Phala
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.21 $0.84 — — yes live 2026-10-24
Wafer
openrouter
deepseek/deepseek-v4.1-flash 1M mxfp4 $0.22 $0.65 — — yes live 2026-10-24
Makora
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.26 $1.17 — — yes live 2026-10-24
Crusoe
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.29 $1.20 — — yes live 2026-10-24
Together AI
hf, openrouter
deepseek-ai/DeepSeek-V4.1-Flash 1M unknown $0.30 $1.20 272.6 112 ms yes live 2026-10-25
Baseten
hf, openrouter
deepseek-ai/DeepSeek-V4.1-Flash 1M unknown $0.30 $1.20 308.9 173 ms yes live 2026-10-25
Parasail
parasail, openrouter
parasail-deepseek-v41-flash 1M fp8 $0.30 $1.20 — — yes serverless 2026-10-24
Google
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.30 $1.20 — — yes live 2026-10-24
Nebius
openrouter
deepseek/deepseek-v4.1-flash 1023K fp8 $0.30 $1.20 — — yes live 2026-10-24
PrimeIntellect
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.30 $1.20 — — yes live 2026-10-24
Cohere
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.30 $1.20 — — yes live 2026-10-24
SiliconFlow
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.30 $1.20 — — yes live 2026-10-24
Modal
openrouter
deepseek/deepseek-v4.1-flash 1M unknown $0.30 $1.20 — — yes live 2026-10-24
Venice
openrouter
deepseek/deepseek-v4.1-flash 977K fp8 $0.30 $1.20 — — yes live 2026-10-24
Morph
openrouter
deepseek/deepseek-v4.1-flash 1M fp8 $0.45 $0.60 — — yes live 2026-10-24
Featherless
hf
deepseek-ai/DeepSeek-V4.1-Flash — — — — — — — live 2026-10-25

Articles and coverage

26 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

deepseek.com2026-09-10officialauthorityHN 1 pts · 1 comments293 words
DeepSeek launches V4.1-Flash, the smallest model in its new architecture family with native visual understanding and a 552B-parameter MoE design. The model utilizes a Causal Encoder–Decoder structure with 8B active parameters for input and 16B for output, requiring just 1/4 the HBM and 1/8 the SSD storage compared to the previous generation. New pricing sets off-peak rates at 50% of peak rates to balance demand.written, reviewed, cited

Analysis6

enclave.ai2026-09-16HN front pageHN 177 pts · 67 comments1,018 words
DeepSeek V4.1 Flash achieved a perfect score on an AI hacking benchmark by gaining code execution on all 11 vulnerable targets while keeping four fixed targets secure. The accepted runs cost $4.65 after using 268.3 million input tokens, where 266.2 million were cached, and executing 2,349 Bash commands across targets like Grafana. A review found six planned solutions and five unexpected routes, prompting the provider to close extra paths and add stricter checks for future leaderboard comparisons.written, reviewed, cited
  • The complete cost including failed attempts and replacement runs increased to $5.14.
  • Models tested on the earlier benchmark version will need new runs before entering the updated ranking.
  • The five unexpected routes belong to the private benchmark environment and carry no claim about new security holes in upstream products.
zartbot.github.io2026-09-17HN front pageHN 131 pts · 10 comments12,565 words
DeepSeek-V4.1 Flash is a 552B multimodal model that pushes KV cache compression to handle long-horizon agent workflows with contexts up to 1 million tokens. It achieves nearly 420 tokens/s speed by using FP4 KVCache quantization and CSA2 cross-layer compression, reducing runtime storage to 1/4 and persistent storage to 1/8 of prior versions. The architecture activates only 8B parameters during prefill and 16B during decoding, resulting in a storage growth of about 890 bytes per token for the global main KV and indexer.written, reviewed, cited
  • The model abandons block-based high-compression structures in favor of optimized CSA2 for KV Cache reduction.
  • The Encoder merges 2 adjacent positions into 1 cache entry using channel-wise learned weights while the Decoder retains per-position entries.
  • The whole network retains only 3 copies of Encoder cache and 1 copy of Decoder cache across multiple layers.
github.com2026-09-15authorityHN 16 pts · 6 comments5,646 words
WARP is an embeddable C inference engine that runs the full 2.78-trillion-parameter Kimi K3 on a 64 GB MacBook Pro at about 0.6 tokens per second by streaming selected experts directly from disk. The engine keeps the shared model trunk in RAM while reading only the active experts, which use 3-bit residual vector quantization, from fast storage. Unused RAM acts as a bounded expert cache, and a lookahead router predicts needed experts to overlap reads with computation.written, reviewed, cited
  • A 32 GB machine can open the model but will page heavily due to insufficient RAM.
  • Enlarging the expert cache past the default budget causes throughput to drop to near zero.
  • Since version 0.7.2, expert banks can be spread across multiple drives to prevent queueing.
github.com2026-09-15authorityHN 14 pts · 2 comments2,071 words
artificialanalysis.ai2026-09-10authorityHN 4 pts · 0 comments1,108 words
lmsys.org2026-09-16authorityabout the DeepSeek V4.1 familyHN 1 pts · 0 comments1,728 words

Community3

twitter.com2026-09-10HN front pageHN 1,016 pts · 574 comments
news.ycombinator.com2026-09-08HN 22 pts · 8 comments238 words
twitter.com2026-09-12HN 22 pts · 6 comments
All coverage tracked for this model (26) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
DeepSeek V4.1 Flash: Complete Technical Architecture ...local-ai-zone.github.io—2026-10-07exa
Fast single-box DeepSeek v4.1 Flash runtimetangled.org2026-10-072026-10-07hn
DeepSeek v4.1 Flash at 378 tok/s 99.7% Cache hit rateruninfra.ai2026-10-072026-10-07hn
OpenCode: $60 of usage for DeepSeek v4.1 Flash is now permanenttwitter.com2026-09-252026-10-07hn
Ask HN: DeepSeek v4.1 Flash on Local hardware, what tok/s do you see?news.ycombinator.com2026-09-192026-10-07hn
DeepSeek v4.1 Flash avg 102 tps on 4x RTX6000 pro max-q, 2.1x up from v4-flashforum.level1techs.com2026-09-182026-10-07hn
DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compressionzartbot.github.io2026-09-172026-10-07hn
Setting Up Pi with DeepSeek v4.1 Flash on OpenRoutervincentschmalbach.com2026-09-162026-10-07hn
DeepSeek v4.1 Flash Is Now Our Best Hacking Modelenclave.ai2026-09-162026-10-07hn
SGLang and Miles Add Day-0 Support for DeepSeek-v4.1lmsys.org2026-09-162026-10-07hn
Show HN: Warp – Run DeepSeek v4.1 Flash with 5 GB of RAM at 3.77 tok/sgithub.com2026-09-152026-10-07hn
DeepSeek v4.1 Flash (518GB, 4-bit) on a 128GB MacBook: 2.7x prefill, 17 tok/sgithub.com2026-09-152026-10-07hn
DeepSeek V4.1 Flash: Benchmarks, Capabilities, and Architecturegingerlabs.ai2026-09-152026-10-07exa
DeepSeek V4.1 Flash Review — Frontier Agentic Scores at Flash Pricing | AI Agents & Agentic Workflows Guideagentguides.dev2026-09-132026-10-07exa
DeepSeek v4.1 flash runs 23 seconds/token on a 2020 16gb M1 Mac Minitwitter.com2026-09-122026-10-07hn
DeepSeek-V4.1-Flash: more efficient prefill for coding agentsbaseten.co2026-09-112026-10-07exa
DeepSeek v4.1 Flash vs. ChatGPT Pro Subscriptionaicharts.io2026-09-102026-10-07hn
DeepSeek v4.1 Flash – Artificial Analysisartificialanalysis.ai2026-09-102026-10-07hn, exa
DeepSeek-v4.1-Flash: smarter, faster, more efficientdeepseek.com official2026-09-102026-10-07hn, exa
DeepSeek v4.1-Flash on Openrouteropenrouter.ai2026-09-102026-10-07hn
DeepSeek v4.1 Flashtwitter.com2026-09-102026-10-07hn
DeepSeek V4.1 Flash: New Base Model, Not a Point Releaseorcarouter.ai2026-09-102026-10-07exa
DeepSeek V4.1 Flash Review: Price & Benchmarks · OmniaKeyomniakey.com2026-09-102026-10-07exa
DeepSeek-V4.1-Flash vs V4-Pro vs V4-Flash: Benchmarks, Cost, and Speed Comparedapidog.com2026-09-102026-10-07exa
DeepSeek v4.1 Flash Beta on Vercelvercel.com2026-09-092026-10-07hn
DeepSeek v4.1 Flash is now available for internal beta testingnews.ycombinator.com2026-09-082026-10-07hn

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    same size band (XXL), also MoE, same task (image-text-to-text), fewer providers
  • Qwen3.5-397B-A17B · 403B
    same size band (XXL), also MoE, same task (image-text-to-text), fewer providers, permissive license

Sources

5 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:31 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 14:59 UTC · Last verified 2026-10-25

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