Models › DeepSeek-V3.2 family

deepseek-ai MIT out 11 months ago

685B parameters (8 of 256 experts per token), text in, text out, 160K tokens of context. 11 providers serve it from $0.26 per million input tokens.

Verified 2026-10-25 Rank #65 in LLMs · score 104.7 method v1 Sources 4 Downloads 30d 5.6M · #26 of 619 tracked

Changed this week: 1 new quantized build on the Hub (20 in all).2026-10-24

Can I run it?411GB at INT4Estimateneeds a multi-GPU nodemethod
What does it cost?$0.26/1M incheapest of 9SiliconFlowfp82026-10-24
Can I use it?MITPermissivelicence file2026-10-24
Does it work?70on SWE-bench Verified#4 of 22 measuredleaderboard2026-02-17
Do people use it?5.6Mdownloads, 30 days#26 of 619 we track · top 4%+7 likes, downloads +8% this week1,587 likesHugging Face2026-10-24

Worth reading

Picked by rule from 28 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post

DeepSeek-V3.2 Release

DeepSeek launches V3.2 and V3.2-Speciale, reasoning-first models built for agents with V3.2-Speciale attaining gold-level results in IMO, CMO, ICPC World Finals & IOI 2025.

api-docs.deepseek.com 2025-12-01 14 HN pts 217 words
Most discussed · 309 HN pts

DeepSeek-v3.2-Exp

DeepSeek-V3.2-Exp introduces DeepSeek Sparse Attention to optimize long-context efficiency while maintaining performance on par with V3.1-Terminus.

github.com 2025-09-29 309 HN pts · 50 comments 537 words
Latest · 3 months ago

DeepSeek-AI/DeepSeek-v3.2

DeepSeek-V3.2 is an open-weights language model for text generation published by deepseek-ai and added to Hugging Bay Jun 29, 2026.

  • The page explicitly states that file-size fit is an estimate and not a successful run or speed promise.
  • A recorded SHA-256 checksum fingerprints only the named file bytes and does not prove safety or quality.
huggingbay.xyz 2026-07-11 2 HN pts · 3 comments 766 words

25 best of 28 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. PaperDeepSeek-V3.2: Pushing the Frontier of Open Large Language ...arxiv.org · undated
  2. ArticleDeepSeek-v3.2 on GB300: Performance Breakthroughblog.vllm.ai · 2026-02-25 · 2 HN pts
  3. ArticleDeepSeek-v3.2-Expapi-docs.deepseek.com · 2025-10-04 · 3 HN pts · official
  4. ArticleDeepSeek-v3.2simonwillison.net · 2025-12-02 · 7 HN pts
  5. ArticleDeepSeek v3.2 Is Okay and Cheap but Slowthezvi.substack.com · 2025-12-05 · 4 HN pts
  6. CodeDeepSeek-v3.2-Exp: Long-Context Efficiency with DeepSeek Sparse Attention [pdf]github.com · 2025-09-29 · 4 HN pts
  7. ArticleDeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co · 2025-12-08 · 7 HN pts
  8. ArticleGPT-5.2, Grok 4.1, and DeepSeek v3.2 compare as Santa agentsveris.ai · 2025-12-19
  9. ArticleDeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co · 2025-12-01
  10. DiscussionLaunching DeepSeek-v3.2 and DeepSeek-v3.2-Specialetwitter.com · 2025-12-01
  11. ArticleThe DeepSeek v3.2 Breakthrough Simplifiedtripplyons.com · 2025-09-30
  12. ArticleDeepSeek-V3.2 — Advanced Paper (Free) | THE D*AI*LY BRIEFberi.net · undated
  13. ArticleDeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co · 2025-12-01
  14. ArticleDeepSeek-v3.2cas-bridge.xethub.hf.co · 2025-12-01
  15. ArticleDeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co · 2025-12-01
  16. ArticleDeepSeek-v3.2cas-bridge.xethub.hf.co · 2025-12-01
  17. ArticleDeepSeek v3.2 Technical Reportcas-bridge.xethub.hf.co · 2025-12-01
  18. DiscussionDeepSeek-v3.2-Exptwitter.com · 2025-09-29
  19. ArticleDeepSeek V3.2 Review: GPT-5 Performance at a Fraction of the Cost | Awesome Agentsawesomeagents.ai · 2026-02-01
  20. ArticleDeepSeek-V3.2: A Technical Deep Dive | Abhijit Moreabhijitmore.github.io · 2026-01-07
  21. ArticleThe Algorithm That Ate The High End: A Deep Dive Into DeepSeek-V3.2nowadais.com · 2025-12-10
  22. ArticleDeepSeek V3.2: Architecture, Training, and Practical Capabilities - ML Digestml-digest.com · 2025-12-05

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 12×

9 providers with a live price, from $0.26 to $3.00 per million input tokens; the median is $0.28. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00SiliconFlow: $0.26 in / $0.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2DeepInfra: $0.26 in / $0.38 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V3.2?expand[]=inferenceProviderMappingAtlasCloud: $0.26 in / $0.38 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Venice: $0.27 in / $0.39 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Baidu: $0.28 in / $0.42 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2DigitalOcean: $0.30 in / $0.96 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Phala: $1.00 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Mara: $3.00 in / $4.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2SambaNova: $3.00 in / $4.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2cheapest $0.26median $0.28
$0.010$0.10$1.00$10.00SiliconFlow: $0.26 in / $0.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2DeepInfra: $0.26 in / $0.38 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/deepseek-ai/DeepSeek-V3.2?expand[]=inferenceProviderMappingAtlasCloud: $0.26 in / $0.38 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Venice: $0.27 in / $0.39 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Baidu: $0.28 in / $0.42 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2DigitalOcean: $0.30 in / $0.96 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Phala: $1.00 in / $1.00 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2Mara: $3.00 in / $4.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2SambaNova: $3.00 in / $4.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/deepseek/deepseek-v3.2cheapest $0.26 · median $0.28 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 SiliconFlow $0.26 $0.80 160K fp8 Oct 24
2 AtlasCloud $0.26 $0.38 160K fp8 Oct 24
3 DeepInfra $0.26 $0.38 16K fp4 Oct 25
4 Venice $0.27 $0.39 156K unknown Oct 24
5 Baidu $0.28 $0.42 128K fp8 Oct 24

All 11 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 (11 API providers); 20 quantized builds for local use.

  • As a service11AtlasCloud, Baidu, DeepInfra, DigitalOcean +7
  • On own hardware20quantized builds
  • Built on4234 fine-tunes · 7 adapters · 1 merges
  • In apps100+Hugging Face Spaces

Top Spaces running it: enzostvs/deepsite ♥ 16,604 · hadadxyz/ai ♥ 819 · embedl/hfviewer ♥ 181 · hfmlsoc/different-flops ♥ 11

Will it fit?

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

Needs an 8×80 GB node even at INT4

About 411 GB at INT4, 822 GB at FP8, 1.64 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 411 GB of weights (estimate; KV cache and activations extra)INT4411 GBFP8: about 822 GB of weights (estimate; KV cache and activations extra)FP8822 GBBF16: about 1.64 TB of weights (estimate; KV cache and activations extra)BF161.64 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 411 GB of weights (estimate; KV cache and activations extra)INT4411 GBFP8: about 822 GB of weights (estimate; KV cache and activations extra)FP8822 GBBF16: about 1.64 TB of weights (estimate; KV cache and activations extra)BF161.64 TB

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

How it scores

Third-party measurements only; author-reported numbers stay in Reference, marked as such.

#4 of 22 on SWE-bench Verified so far

70. Each tick is a model on this site with a measured SWE-bench Verified row; author-reported numbers stay in Reference. Percent resolved. agent: mini-SWE-agent, reasoning_effort=high

MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2.5: 70.8 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Thinking: 63.4 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010070 · #4 of 22
MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2.5: 70.8 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Thinking: 63.4 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010070 · #4 of 22

Source · leaderboard · 2026-02-17

The story so far

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

What people built on it

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

62 derivatives on the Hub, most of them fine-tunes

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

20 quantized (Hugging Face model tree)34 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)7 adapters (Hugging Face model tree)20 quantized34 fine-tunes1 merge7 adapters
20 quantized (Hugging Face model tree)34 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)7 adapters (Hugging Face model tree)20 quantized34 fine-tunes1 merge7 adapters

All variants →

Reference

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

Benchmarks

2 rows

Benchmarks 2 rows

measured third-party leaderboard row (2)author-reported number from the model card or eval filesNo 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
measured SWE-bench Multilingual · Write code with agents59 percent resolvedagent: mini-SWE-agentleaderboard2026-02-13
measured SWE-bench Verified · Write code with agents70 percent resolvedagent: mini-SWE-agent, reasoning_effort=highleaderboard2026-02-17

Use-case evidence

4 use cases

What it is good at 4 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

21 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesDeepseekV32ForCausalLM
Model typedeepseek_v32
Dtypebfloat16
Params by dtypeBF16: 3,946,184,704 · F32: 1,075,456 · F8_E4M3: 681,408,069,632
Size bandXXL
MoEyes
Experts256
Experts per token8
Context length163,840 (config.json max_position_embeddings)
LicenseMIT (mit)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · DeepseekV32ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: not in registry · 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-V3.2: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.64 TB · Cluster (≥640 GB)
FP8 (1 byte/param)822 GB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)411 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

Fine-tuned from deepseek-ai/DeepSeek-V3.2-Exp-Base (same organization) Family: deepseek-ai/DeepSeek-V3.2 62 derivatives on the Hub

Base model: deepseek-ai/DeepSeek-V3.2-Exp-Base

quantized: 20finetune: 34adapter: 7merge: 1

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
QuantTrio/DeepSeek-V3.2-AWQ quantized AWQ 685B XXL mit QuantTrio 11 1.5M
nvidia/DeepSeek-V3.2-NVFP4 finetune NVFP4 394B XXL mit nvidia 19 175.3k
DevQuasar/deepseek-ai.DeepSeek-V3.2-GGUF quantized GGUF — — — DevQuasar 1 161.5k
unsloth/DeepSeek-V3.2-GGUF quantized GGUF — — mit unsloth 41 101.1k
mlx-community/DeepSeek-V3.2-4bit finetune -4bit 672B XXL mit mlx-community 7 18.4k
amd/DeepSeek-V3.2-mxfp4 quantized mxfp4 377B XXL mit amd 1 9.8k
cerebras/DeepSeek-V3.2-REAP-345B-A37B quantized — 345B XXL mit cerebras 34 7.4k
lovedheart/DeepSeek-V3.2-REAP-345B-A37B-GGUF-Experimental quantized GGUF — — mit lovedheart 4 6.1k
mlx-community/DeepSeek-V3.2-8bit finetune -8bit 672B XXL mit mlx-community 0 4.8k
mlx-community/DeepSeek-V3.2-mlx-5bit quantized mlx 672B XXL mit mlx-community 2 4.3k
bdellabe/DeepSeek-V3.2-NVFP4-FP8-BLOCK quantized NVFP4 384B XXL apache-2.0 bdellabe 0 3.8k
cerebras/DeepSeek-V3.2-REAP-508B-A37B quantized — 508B XXL mit cerebras 15 3k
mlx-community/DeepSeek-V3.2_bf16 finetune — 672B XXL mit mlx-community 1 3.4k
baa-ai/DeepSeek-V3.2-RAM-350GB-MLX quantized MLX 672B XXL mit baa-ai 0 3k
sjug/DeepSeek-V3.2-MLX-4bit finetune MLX 672B XXL mit sjug 0 2.7k
exolabs/DeepSeek-V3.2_bf16 finetune — 672B XXL mit exolabs 0 2.5k
RedHatAI/DeepSeek-V3.2-NVFP4-FP8-BLOCK quantized NVFP4 384B XXL mit RedHatAI 1 2.2k
amd/DeepSeek-V3.2-mtp-ptpc quantized — 686B XXL mit amd 1 1.7k
sszymczyk/DeepSeek-V3.2-nolight-GGUF quantized GGUF — — mit sszymczyk 10 1.2k
ubicloud/DeepSeek-V3.2-Pruned-15B quantized — 15.2B L — ubicloud 1 624
sarthak2314/indicbart-hindi-gec-v2 finetune — 244M S — sarthak2314 0 629
Doctor-Shotgun/DeepSeek-V3.2-dense-attn-GGUF quantized GGUF — — — Doctor-Shotgun 3 278
lovedheart/DeepSeek-V3.2-GGUF-Experimental quantized GGUF — — mit lovedheart 1 292
kathywu95/deepseek-v3.2-small-random-fp8-64-experts quantized -fp8 5.52B M — kathywu95 1 280
CPU-Hybrid-MoE/DeepSeek-V3.2-CPU-NUMA4-AMXINT8 quantized — — — — CPU-Hybrid-MoE 1 150
Kebob/DeepSeek-V3.2-CPU-NUMA2-AMXINT4 quantized — — — mit Kebob 0 172
ckoh04/DeepSeek-V3.2-NVFP4-active-slice finetune NVFP4 25.2B L mit ckoh04 0 129
Hotblaz/Compare_Anti_9 finetune — — — mit Hotblaz 1 2
Dominic586/ugly finetune — — — apache-2.0 Dominic586 1 0
nvcnvn/DeepSeek-V3.2-4L quantized — 28.6B L mit nvcnvn 0 19
Syntrallm/syntrallm adapter — — — mit Syntrallm 0 0
massmas/ok finetune — — — — massmas 0 0
KNB86/ccr finetune — — — — KNB86 0 0
cridhdxbgct5/bb adapter — — — apache-2.0 cridhdxbgct5 0 0
Yooghrn/Goodheng adapter — — — bigcode-openrail-m Yooghrn 0 0
Adimasdn/Humaniora finetune — — — apache-2.0 Adimasdn 0 0
mengerti/my-robot-dataset finetune — — — apache-2.0 mengerti 0 0
Maxmaxjawa/max1412 finetune — — — llama4 Maxmaxjawa 0 0
wedangjahe/Greenday finetune — — — mit wedangjahe 0 0
Arief1899X/Galuh finetune — — — apache-2.0 Arief1899X 0 0

Repository changelog

7 commits
changed in the last 7 daysOther: 2026-10-24all changes

Repository changelog 7 commits

DateCommitSHA
2025-12-01 Update README.md a7e62ac
2025-12-01 Release DeepSeek-V3.2 c69397e
2025-12-01 Add files using upload-large-folder tool 205ad63
2025-12-01 Add files using upload-large-folder tool 00846f3
2025-12-01 Add files using upload-large-folder tool fba8af1
2025-12-01 Add files using upload-large-folder tool ba658fb
2025-12-01 initial commit e91970e

All providers

11 providers · 11 endpoints

Where to run 11 providers · 11 endpoints

Hosted endpoints observed for DeepSeek-V3.2, 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: $0.21/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
GMICloud
openrouter
deepseek/deepseek-v3.2 160K fp8 $0.21 $0.31 — — yes status -2 2026-10-24
SiliconFlow
openrouter
deepseek/deepseek-v3.2 160K fp8 $0.26 $0.80 — — yes live 2026-10-24
DeepInfra
hf, deepinfra, openrouter
deepseek-ai/DeepSeek-V3.2 16K fp4 $0.26 $0.38 32.5 440 ms yes live 2026-10-25
AtlasCloud
openrouter
deepseek/deepseek-v3.2 160K fp8 $0.26 $0.38 — — yes live 2026-10-24
Venice
openrouter
deepseek/deepseek-v3.2 156K unknown $0.27 $0.39 — — yes live 2026-10-24
Baidu
openrouter
deepseek/deepseek-v3.2 128K fp8 $0.28 $0.42 — — — live 2026-10-24
DigitalOcean
openrouter
deepseek/deepseek-v3.2 160K unknown $0.30 $0.96 — — — live 2026-10-24
Google
openrouter
deepseek/deepseek-v3.2 160K unknown $0.56 $1.68 — — yes status -5 2026-10-24
Phala
openrouter
deepseek/deepseek-v3.2 160K unknown $1.00 $1.00 — — yes live 2026-10-24
Mara
openrouter
deepseek/deepseek-v3.2 32K unknown $3.00 $4.50 — — — live 2026-10-24
SambaNova
openrouter
deepseek/deepseek-v3.2 32K unknown $3.00 $4.50 — — — live 2026-10-24

Articles and coverage

28 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.

Official2

api-docs.deepseek.com2025-12-01officialauthorityHN 14 pts · 0 comments217 words
DeepSeek launches V3.2 and V3.2-Speciale, reasoning-first models built for agents with V3.2-Speciale attaining gold-level results in IMO, CMO, ICPC World Finals & IOI 2025. The new models integrate thinking directly into tool-use after training on 1,800+ environments and 85k+ complex instructions. V3.2-Speciale is API-only until Dec 15th, 2025, 15:59 (UTC Time) and requires higher token usage for complex tasks.written, reviewed, cited
api-docs.deepseek.com2025-10-04officialauthorityHN 3 pts · 0 comments137 words

Analysis8

github.com2025-09-29authorityHN front pageHN 309 pts · 50 comments537 words
DeepSeek-V3.2-Exp introduces DeepSeek Sparse Attention to optimize long-context efficiency while maintaining performance on par with V3.1-Terminus. The model scores 85.0 on MMLU-Pro and 89.3 on AIME 2025, with a 2025.11.17 fix resolving a Rotary Position Embedding discrepancy in the indexer module. Weights are licensed under the MIT License and supported by vLLM and SGLang.written, reviewed, cited
huggingbay.xyz2026-07-11HN 2 pts · 3 comments766 words
DeepSeek-V3.2 is an open-weights language model for text generation published by deepseek-ai and added to Hugging Bay Jun 29, 2026. The repository totals 689.5 GB with 178 hosted files available out of 190 listed, distributed in safetensors format. The model is released under the mit license, permitting commercial use, modification, and redistribution.written, reviewed, cited
  • The page explicitly states that file-size fit is an estimate and not a successful run or speed promise.
  • A recorded SHA-256 checksum fingerprints only the named file bytes and does not prove safety or quality.
  • The system cannot verify if the model fits a specific computer because it lacks the user's machine specs.
arxiv.orgauthority8,304 words
DeepSeek-V3.2 introduces DeepSeek Sparse Attention to reduce computational complexity while preserving performance in long-context scenarios. The model allocates a post-training computational budget exceeding 10% of the pre-training cost and trains on data generated from over 1,800 distinct environments and 85,000 complex prompts. Starting from a base checkpoint with a 128K context length, the model achieves gold-medal performance in the 2025 International Mathematical Olympiad and the International Olympiad in Informatics.written, reviewed, cited
  • The model implements DSA based on the MQA mode of MLA where each latent vector is shared across all query heads.
  • The indexer warm-up stage uses a learning rate of 10^-3 and trains for only 1000 steps.
  • The sparse training stage selects 2048 key-value tokens for each query token.
blog.vllm.ai2026-02-25authorityHN 2 pts · 0 comments1,908 words
DeepSeek-V3.2 (NVFP4 + TP2) runs on GB300 with 288GB memory per GPU using vLLM v0.14.1 and the FlashInfer FP4 MoE kernel. It achieves 7360 TGS in prefill-only scenarios and 2816 TGS in mixed contexts (ISL=2k, OSL=1k), while DeepSeek R1 reaches ~22476 TGS in prefill. V3.2's lower throughput stems from the Indexer/Sparse MLA overhead, where the kernel execution time for a single DSA layer step is 2.7x that of MLA.written, reviewed, cited
simonwillison.net2025-12-02authorityHN 7 pts · 0 comments333 words
thezvi.substack.com2025-12-05authorityHN 4 pts · 0 comments2,504 words
github.com2025-09-29authorityHN 4 pts · 0 comments
cas-bridge.xethub.hf.co2025-12-08HN 7 pts · 1 comments
All coverage tracked for this model (28) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
DeepSeek-V3.2: Pushing the Frontier of Open Large Language ...arxiv.org—2026-10-07exa
DeepSeek-V3.2 — Advanced Paper (Free) | THE D*AI*LY BRIEFberi.net—2026-10-07exa
DeepSeek-AI/DeepSeek-v3.2huggingbay.xyz2026-07-112026-10-07hn
DeepSeek-v3.2 on GB300: Performance Breakthroughblog.vllm.ai2026-02-252026-10-07hn
DeepSeek V3.2 Review: GPT-5 Performance at a Fraction of the Cost | Awesome Agentsawesomeagents.ai2026-02-012026-10-07exa
DeepSeek-V3.2: A Technical Deep Dive | Abhijit Moreabhijitmore.github.io2026-01-072026-10-07exa
GPT-5.2, Grok 4.1, and DeepSeek v3.2 compare as Santa agentsveris.ai2025-12-192026-10-07hn
The Algorithm That Ate The High End: A Deep Dive Into DeepSeek-V3.2nowadais.com2025-12-102026-10-07exa
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co2025-12-082026-10-07hn
DeepSeek v3.2 Is Okay and Cheap but Slowthezvi.substack.com2025-12-052026-10-07hn
DeepSeek V3.2: Architecture, Training, and Practical Capabilities - ML Digestml-digest.com2025-12-052026-10-07exa
DeepSeek V3.2: Early Results From Atlas | by LayerLens | Mediummedium.com2025-12-032026-10-07exa
DeepSeek-v3.2simonwillison.net2025-12-022026-10-07hn
DeepSeek V3.2 Explained | Architecture, Sparse Attention & Benchmarksdataguy.in2025-12-022026-10-07exa
DeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co2025-12-012026-10-07hn
DeepSeek-v3.2cas-bridge.xethub.hf.co2025-12-012026-10-07hn
DeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co2025-12-012026-10-07hn
DeepSeek-v3.2cas-bridge.xethub.hf.co2025-12-012026-10-07hn
DeepSeek v3.2 Technical Reportcas-bridge.xethub.hf.co2025-12-012026-10-07hn
Launching DeepSeek-v3.2 and DeepSeek-v3.2-Specialetwitter.com2025-12-012026-10-07hn
DeepSeek-v3.2: Pushing the Frontier of Open Large Language Modelscas-bridge.xethub.hf.co2025-12-012026-10-07hn
DeepSeek-V3.2 Releaseapi-docs.deepseek.com official2025-12-012026-10-07hn
DeepSeek V3.2-Exp deep dive - by Jose Lopez - Disrupted AIdisruptedai.substack.com2025-10-272026-10-07exa
DeepSeek-v3.2-Expapi-docs.deepseek.com official2025-10-042026-10-07hn
The DeepSeek v3.2 Breakthrough Simplifiedtripplyons.com2025-09-302026-10-07hn
DeepSeek-v3.2-Exp: Long-Context Efficiency with DeepSeek Sparse Attention [pdf]github.com2025-09-292026-10-07hn
DeepSeek-v3.2-Exptwitter.com2025-09-292026-10-07hn
DeepSeek-v3.2-Expgithub.com2025-09-292026-10-07hn

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Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:32 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:00 UTC · Last verified 2026-10-25

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