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DeepSeek · model passport

DeepSeek: DeepSeek V4 Flash 0731

deepseek-v4-flash
  • Open weights
  • MIT licence
  • 304.2B params
  • 1,048,576 context
  • Released 2026-07-31(HF repo created)
9/27axes covered
Overall · LMArena
1,436#93 of 396
Best percentile
78.3Instruction following
Cohort standing
63.2median 59.8

What the data says

Benchmarked on 9 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 35.8 percentile points between its best and worst axis — 78.3 pct on Instruction following versus 42.5 pct on SimpleBench.

  • Instruction following78.3 pct1,428Verify →
  • WebDev Arena77.7 pct1,582Verify →
  • Hard prompts77.5 pct1,459Verify →
  • Overall76.8 pct1,436Verify →
  • Coding76 pct1,484Verify →
  • LiveBench73.9 pct65.5%Verify →
  • IFBench (Artificial Analysis)54 pct47.2%Verify →
  • Factuality Arena51.2 pct1,439Verify →
  • SimpleBench42.5 pct46.3%Verify →
  • 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
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not 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-V4-Flash-0731
Parameters304.2B
ArchitectureDeepseekV4ForCausalLM
LicenceMIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "deepseek-ai/DeepSeek-V4-Flash-0731"
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-v4-flash-0731
Context length1,048,576 tokens
Max output943,718 tokens
Input price$0.02 per 1M tokens
Cached input price$0.02 per 1M tokens
Output price$0.45 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pParallel Tool CallsPresence penaltyReasoningReasoning effortRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-aTop-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-v4-flash-0731","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-v4-flash stands at 63.2 (raw mean 67.5) — measured on 9 of 27 axes.

    standingtemplateVerify →

  • deepseek-v4-flash stands at 63.2 versus a cohort median of 59.8 across 193 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 →