DeepSeek V3.2
DeepSeekfamily · DeepSeek-V3huggingface.co/deepseek-ai/DeepSeek-V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Updated 7 h ago · first seen 11 Sept 2026
model_01M294WW8H2JQJXMSPY7QTDVWF
Overview
Identity
Identity block not returned by the API for this entity.
Openness
Openness not classified yet — no sourced evidence to place this model in the ontology.
Key facts
- Release date
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Status
Source:DeepSeek — site & API docsT2observed 14 h agomediumLLM-extracted
- Version
Source:DeepSeek — site & API docsT2observed 14 h agomediumLLM-extracted
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Paper
Source:DeepSeek — site & API docsT2observed 14 h agomediumLLM-extracted
- Repository
Source:DeepSeek — site & API docsT2observed 14 h agomediumLLM-extracted
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Architecture
- Architecture
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Model type
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Parameters
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 14 h agomedium
- Hugging Face repo
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Capabilities
Modalities
- Modalities
- text
- Input
- text
- Output
- text
Capabilities
Tool calling
Yes
OpenRouter public model & pricing listing · T2
Structured output
Yes
OpenRouter public model & pricing listing · T2
Reasoning
Yes
OpenRouter public model & pricing listing · T2
Vision
Unavailable
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 11 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Benchmarks9
Compare with another model →Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric
No benchmark results recorded
Providers & Pricing1
All offers in the price terminal →USD per 1M tokens as published by each provider (USD). Rows are append-only: every change is kept in the history below.
Price history
Output price · USD / 1M tokens 1 provider
- DeepSeek API
- DeepSeek APIfirst observed $0.4011 Sept 2026
Input price · USD / 1M tokens 1 provider
- DeepSeek API
- DeepSeek APIfirst observed $0.26911 Sept 2026
Hardware fit37
Assumptions (6)
- Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead.
- bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
- KV cache approximated at 0.5 GB per 8 192 tokens of context, independent of architecture (GQA/MLA models need less).
- A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
- Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
- Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
Lineage
Open in Graph →- ancestor: DeepSeek-V3.2-Exp-Base
Timeline14
Full timeline →DeepSeek V3.2: context length changed from 128000 to 163840
Context window128K tokens→163.8K tokensopenrouterDeepSeek V3.2 scores 70% on SWE-bench Verified
swebench_leaderboardDeepSeek V3.2 scores 59% on SWE-bench Multilingual
swebench_leaderboardDeepSeek V3.2 scores 35.61% on Terminal-Bench
artificial_analysisDeepSeek V3.2 scores 46.82% on Terminal-Bench
artificial_analysisDeepSeek V3.2 scores 90.64% on τ²-bench
artificial_analysisDeepSeek V3.2 scores 24.56% on Humanity's Last Exam
artificial_analysisDeepSeek V3.2 scores 21.49 on Artificial Analysis Intelligence Index
artificial_analysisDeepSeek V3.2: context length changed from 163840 to 128000
Context window163.8K tokens→128K tokensartificial_analysisDeepSeek API lists DeepSeek V3.2 at $0.269 in / $0.4 out per 1M tokens
openrouterDeepSeek V3.2: status changed from deprecated to active
Statusdeprecated→activedeepseek
Change history34
Viewing AI Atlas as of 1 Jul 2026 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →DeepSeek V3.2 was not yet in AI Atlas on 1 Jul 2026
Release daterelease_date1
Opennessopenness1
Licenselicense1
Architecturearchitecture1
Parametersparameter_count1
Context windowcontext_length1
Max outputmax_output_tokens1
Modalitiesmodalities1
Input modalitiesmodalities_input1
Output modalitiesmodalities_output1
Tokenizertokenizer1
Base modelbase_model1
Quantization formatquant_format1
File sizefile_size_gb1
Hugging Face repohf_repo1
Pipeline tagpipeline_tag1
Model cardmodel_card_url1
Downloadsmetric.downloads1
Likesmetric.likes1
Descriptiondescription1
Gatedgated1
Hf inference providershf_inference_providers1
Is quantizedis_quantized1
Last modifiedlast_modified1
Library namelibrary_name1
Downloads all timemetric.downloads_all_time1
Model typemodel_type1
Openrouter idopenrouter_id1
Reasoningreasoning1
Structured outputstructured_output1
Supported parameterssupported_parameters1
Tool callingtool_calling1
Weights dtypeweights_dtype1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
41
Source tiers
T241
Freshest observation
7 h ago
Conflicts
None
Source documents 9
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (68/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →