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DeepSeek · model passport

DeepSeek: DeepSeek V3.2

deepseek-v3-2
  • Open weights
  • MIT licence
  • 685.4B params
  • 163,840 context
  • Released 2025-12-01(HF repo created)
10/27axes covered
Overall · LMArena
1,425#111 of 396
Best percentile
96.6LiveCodeBench (Artificial Analysis)
Cohort standing
65.9median 53.9

What the data says

Benchmarked on 10 of 27 tracked axes. Every figure below is the model's own published result on that board.

Strongest axes

  • 96.6 pct — LiveCodeBench (Artificial Analysis) (86.2%)
  • 95.1 pct — MMLU-Pro (Artificial Analysis) (86.2%)

Uneven: 67.1 percentile points between its best and worst axis — 96.6 pct on LiveCodeBench (Artificial Analysis) versus 29.5 pct on WebDev Arena.

  • LiveCodeBench (Artificial Analysis)96.6 pct86.2%Verify →
  • MMLU-Pro (Artificial Analysis)95.1 pct86.2%Verify →
  • Instruction following75.3 pct1,420Verify →
  • HHEM Hallucination (Vectara)75.2 pct93.7%Verify →
  • Hard prompts73.7 pct1,447Verify →
  • Overall72.2 pct1,425Verify →
  • Coding70.8 pct1,469Verify →
  • IFBench (Artificial Analysis)69.8 pct60.7%Verify →
  • Factuality Arena48.3 pct1,437Verify →
  • WebDev Arena29.5 pct1,362Verify →
  • Vision ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorydeepseek-ai/DeepSeek-V3.2
Parameters685.4B
ArchitectureDeepseekV32ForCausalLM
LicenceMIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "deepseek-ai/DeepSeek-V3.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Use it hosted

API model IDdeepseek/deepseek-v3.2
Context length163,840 tokens
Max output65,536 tokens
Input price$0.28 per 1M tokens
Cached input price$0.03 per 1M tokens
Output price$0.42 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-kTop logprobsTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek/deepseek-v3.2","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • deepseek-v3.2 stands at 65.9 (raw mean 70.6) — measured on 10 of 27 axes.

    standingtemplateVerify →

  • deepseek-v3.2 stands at 65.9 versus a cohort median of 53.9 across 126 models released within 180 days — above the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →