DeepSeek v4 の発表と AI ニュースまとめ

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Top Story: DeepSeek V4

What happened

DeepSeek released DeepSeek-V4 Pro and DeepSeek-V4 Flash, its first major architecture refresh since V3 and first clear two-tier lineup, with 1M-token context, hybrid reasoning/non-reasoning modes, an MIT license, and a technical report detailed enough that multiple researchers called it one of the most important or best-written model papers of the year. Across the reactions, the factual consensus is that V4 materially advances open-weight long-context and agentic coding performance while remaining somewhat behind the top closed frontier models overall. Independent benchmarkers place V4 Pro around the #2 open-weights tier, roughly near Kimi K2.6 / GLM-5.1 / strong Claude Sonnet-class to Opus-ish depending on benchmark and mode, with especially strong long-context and agentic performance; opinions diverge on how close it is to GPT-5.x / Opus 4.7 and on whether this is “democratizing” progress or an architecture so complex that few open labs can realistically reproduce it. Key sources include deep-dive commentary from @ArtificialAnlys, @scaling01, @nrehiew_, @ben_burtenshaw, @TheZachMueller, @ZhihuFrontier, and infra/vendor posts from @vllm_project, @NVIDIAAI, and @Togethercompute.

Core facts and technical details

The most concrete technical claims repeated across the discussion:

• Two models

V4 Pro: 1.6T total parameters / 49B active

• V4 Flash: 284B total / 13B active

• Reported by @ArtificialAnlys, @teortaxesTex, @baseten, @NVIDIAAI

• Context

1M tokens, up from 128K in V3.2 per @ArtificialAnlys

• Multiple posters frame this as the headline achievement: “solid ultra-long context” @teortaxesTex

• Training scale

32T–33T tokens cited repeatedly

• @nrehiew_ notes 32T tokens over 1.6T parameters, i.e. roughly 20 tokens/parameter

• @teortaxesTex cites 33T

• @nrehiew_ estimates pretraining compute at ~1e25 FLOPs

• Reasoning / modes

DeepSeek exposes three reasoning modes per @Togethercompute

• Hybrid “thinking/non-thinking” positioning noted by @ArtificialAnlys

• Long-context architecture

Several threads summarize a new hybrid attention system:

shared KV vectors

• compressed KV streams

• sparse attention over compressed tokens

• local/sliding-window attention for nearby context

• @ZhihuFrontier gives the most compact public summary:

2× KV reduction via shared key-value vectors

• c4a ≈ 4× compression

• c128a ≈ 128× compression

• top-k sparse attention on compressed tokens

• 128-token sliding window

• 1M context KV cache = 9.62 GiB/sequence (bf16)

• 8.7× smaller than DeepSeek V3.2’s 83.9 GiB

• FP4 index cache + FP8 attention cache gives another ~2× reduction

• @ben_burtenshaw condenses this to “10× smaller KV cache”

• @TheZachMueller and @TheZachMueller describe CSA + HCA layer patterns, with alternating layers and V4 Flash using sliding-window layers instead of HCA in some places

• Quantization / checkpoint format

@LambdaAPI: checkpoint is mixed FP4 + FP8

MoE expert weights in FP4

• attention / norm / router in FP8

• claim: the full model fits on a single 8×B200 node

• Inference hardware / serving

@NVIDIAAI: on Blackwell Ultra, V4 Pro can deliver 150+ TPS/user interactivity for agentic workflows

• @NVIDIAAI: published day-0 V4 Pro performance pareto using vLLM

• @SemiAnalysis_: day-0 support and benchmarking across H200, MI355, B200, B300, GB200/300

• @Prince_Canuma: DeepSeek4-Flash on 256GB Mac

• @Prince_Canuma: MLX quants published

• @simonw asks about smaller-RAM Mac viability, implying community interest but incomplete support story

• @QuixiAI reminds users that many local stacks still lack tensor parallel, relevant because V4-class models strongly stress inference infra

• License / availability / pricing

MIT license per @ArtificialAnlys

• first-party API plus rapid third-party availability via @Togethercompute, @baseten, @NousResearch, @Teknium

• V4 Pro pricing: $1.74 / $3.48 per 1M input/output tokens

• V4 Flash pricing: $0.14 / $0.28

• cache-hit pricing also given by @ArtificialAnlys

• @scaling01 views the pricing as a glimpse of future “Mythos-level” cheap coding models

• Reuters-via-posted quote from @scaling01: DeepSeek said Pro pricing could fall sharply once Huawei Ascend 950 supernodes are deployed at scale in H2

Independent evaluations and where V4 lands

The most useful independent benchmark synthesis came from @ArtificialAnlys:

• V4 Pro Max: 52 on Artificial Analysis Intelligence Index

up 10 points from V3.2 at 42

• becomes #2 open weights reasoning model, behind Kimi K2.6 (54)

• V4 Flash Max: 47

positioned around strong mid/high open models, “Claude Sonnet 4.6 max level intelligence”

• GDPval-AA (agentic real-world work):

V4 Pro: 1554, leading open-weight models

• ahead of Kimi K2.6 (1484), GLM-5.1 (1535), MiniMax-M2.7 (1514)

• AA-Omniscience

V4 Pro: -10, an 11-point improvement over V3.2

• but still paired with 94% hallucination rate

• V4 Flash: 96% hallucination rate

• Cost to run AA Index

V4 Pro: $1,071

• V4 Flash: $113

• Output tokens used on AA Index

V4 Pro: 190M

• V4 Flash: 240M

• This is a major caveat: cheap per-token pricing does not imply cheap total task cost if the model spills huge token volumes

Additional eval perspectives:

• @arena:

#2 open in Text Arena overall at debut

• category wins/placements:

#1 Medical & Healthcare

• #15 Creative Writing

• #18 Multi-Turn

• thinking variant:

#8 Math

• #9 Life/Physical/Social Science

• @arena emphasizes the Pro vs Flash tradeoff:

Pro ranks ~30 places higher

• costs 12× more

• Flash is still competitive in Chinese, medicine, math

• @scaling01:

“~Opus 4.5 estimate holds for now, at least on SimpleBench”

• @scaling01:

V4 is “definitely better than GLM-5.1 but not quite Opus 4.7, GPT-5.4 or Gemini 3.1 Pro”

• @scaling01 lists what scores would confirm <6 month gap:

ARC-AGI-1 ~75%

• ARC-AGI-2 ~35%

• GSO ~26%

• METR 4.5–5 hours

• WeirdML ~63%

• @TheZachMueller:

on his evals, Flash@max ≈ Pro@high on reasoning

• Pro focuses more on knowledge (SimpleQA)

• @VictorTaelin:

after fixing benchmark bugs and letting long-running models run longer, DeepSeek and Kimi improved materially

• @mbusigin:

a simple negative early impression with no detail

• @petergostev:

on BullshitBench, not about capability but refusal/pushback behavior, GPT-5.5 underperformed; included here because many readers compare V4 in an eval-skeptical environment

Facts vs opinions

Facts / relatively well-supported claims

• V4 Pro / Flash were released with the specs above, MIT-licensed, 1M context, and open technical documentation: @ArtificialAnlys, @TheZachMueller

• The architecture introduces a new long-context attention system with dramatic KV-cache reduction: @ZhihuFrontier, @ben_burtenshaw

• Independent benchmarkers broadly place V4 Pro near the very top of open weights but below the best proprietary models overall: @ArtificialAnlys, @arena, @scaling01

• DeepSeek V4 is heavily token-intensive in some evaluations: @ArtificialAnlys

• The checkpoint uses FP4/FP8 mixed precision and can fit on an 8×B200 node: @LambdaAPI

• Rapid ecosystem support arrived via vLLM and other providers day 0: @vllm_project, @SemiAnalysis_

Opinions / interpretation

• “V4 is ~4–5 months behind the frontier” from @scaling01, @scaling01, @scaling01 is an informed estimate, not a measured fact

• “Top three open” vs “only open model close to frontier” debate from @teortaxesTex is partly about benchmark trust and framing

• “Strongest pretrained model we have” from @teortaxesTex is an opinion hinging on scale + architecture, not direct benchmark supremacy

• “Most significant AI paper of the year” from @Dorialexander is enthusiasm, not consensus

• “This is what research should look like” from @scaling01 speaks to transparency/style rather than only capability

• “Not exactly a democratizing technology” from @teortaxesTex is a strong architectural/political interpretation

Different opinions and fault lines

1) Is V4 near frontier, or clearly behind?

More favorable

• @scaling01: puts it at roughly GPT-5.2 / Opus 4.5+ tier

• @scaling01: SimpleBench supports ~Opus 4.5

• @teortaxesTex: argues it is the strongest pretraining base among opens and implies people are underestimating what post-training can do

More skeptical

• @scaling01: below Opus 4.7 / GPT-5.4 / Gemini 3.1 Pro

• @scaling01: the gap may widen again because closed labs have bigger models, better science/law/medicine coverage, faster inference with GB200s

• @mbusigin: early impressions “not great”

• @teortaxesTex: says polished models like K2.6 and GLM 5.1 may still feel better in coding despite lower intrinsic capacity

2) Is V4’s real contribution model quality, or long-context systems design?

A big split in reactions is that many technical readers think the long-context architecture matters more than the raw benchmark position.

• @teortaxesTex: “They've completed their quest: Solid Ultra-Long Context”

• @ben_burtenshaw: first open model where long context and agentic post-training “meet”

• @scaling01: expects other open labs to adopt pieces of the architecture

• @Dorialexander: frames Huawei/sovereignty constraints as an opportunity to reshape hardware and memory/interconnect design

• @jukan05: reads the paper as evidence that NVIDIA’s hardware roadmap is unusually well aligned to where MoE/long-context models are going

3) Is V4 “open democratization,” or too hard to copy?

This was one of the sharpest strategic disagreements.

• @teortaxesTex: says V4 is “not exactly a democratizing technology” because the architecture is too difficult for most labs to replicate

• @teortaxesTex: suggests even DeepSeek may not want to do this exact architecture again without refactoring

• @stochasticchasm: notes the sheer hyperparameter complexity is daunting

• Against that, @Prince_Canuma and @Prince_Canuma show that the ecosystem is already compressing and adapting Flash for localish Apple Silicon use, softening the “not democratizing” claim on the inference side if not the training side

4) Are people underrating Flash?

Several reactions suggest Flash may be more important than Pro for practical adoption.

@arena: Flash shifts the price/performance front