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Alibaba · model passport

Qwen3 8 Flash Next

qwen3-8-flash-next
  • Open weights
  • qwen-community-1.0 licence
  • 180.0B params
  • Released 2026-08-24(HF repo created)
2/27axes covered
Best percentile
89.2WebDev Arena
Cohort standing
54.8median 60.5

Thin coverage — read with caution

This model appears on only 2 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.

What the data says

Benchmarked on 2 of 27 tracked axes. Every figure below is the model's own published result on that board.

On only 2 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.

Uneven: 54.7 percentile points between its best and worst axis — 89.2 pct on WebDev Arena versus 34.5 pct on Agent Arena.

  • WebDev Arena89.2 pct1,633Verify →
  • Agent Arena34.5 pct-0.026Verify →
  • OverallNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document 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
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked
  • CodingNot benchmarked
  • Instruction followingNot benchmarked
  • Hard promptsNot 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

RepositoryQwen/Qwen3.8-Flash-Next
Parameters180.0B
ArchitectureQwen4ExpForConditionalGeneration
Licenceqwen-community-1.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen3.8-Flash-Next"
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 →

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.

  • qwen3.8-flash-next stands at 54.8 (raw mean 61.9) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.

    standingtemplateVerify →

  • qwen3.8-flash-next stands at 54.8 versus a cohort median of 60.5 across 182 models released within 180 days — below 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 →