Skip to content
aiqbench

Alibaba · model passport

Qwen: Qwen3 Next 80B A3B Thinking

qwen3-next-80b-a3b-thinking
  • Open weights
  • Apache 2.0 licence
  • 81.3B params
  • 262,144 context
  • Released 2025-09-09(HF repo created)
6/27axes covered
Overall · LMArena
1,369#182 of 396
Best percentile
56.2HHEM Hallucination (Vectara)
Cohort standing
53.0median 52.1

What the data says

Benchmarked on 6 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 2.9 percentile points between its best and worst axis — 56.2 pct on HHEM Hallucination (Vectara) versus 53.3 pct on LiveBench.

  • HHEM Hallucination (Vectara)56.2 pct90.7%Verify →
  • Coding54.7 pct1,421Verify →
  • Hard prompts54.5 pct1,385Verify →
  • Overall54.3 pct1,369Verify →
  • Instruction following54.3 pct1,359Verify →
  • LiveBench53.3 pct54.9%Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality 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
  • 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
  • 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

RepositoryQwen/Qwen3-Next-80B-A3B-Thinking
Parameters81.3B
ArchitectureQwen3NextForCausalLM
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen3-Next-80B-A3B-Thinking"
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 IDqwen/qwen3-next-80b-a3b-thinking
Context length262,144 tokens
Max output235,929 tokens
Input price$0.15 per 1M tokens
Output price$1.20 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogprobsMax tokensPresence 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":"qwen/qwen3-next-80b-a3b-thinking","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.

  • qwen3-next-80b-a3b-thinking stands at 53 (raw mean 54.6) — measured on 6 of 27 axes.

    standingtemplateVerify →

  • qwen3-next-80b-a3b-thinking stands at 53 versus a cohort median of 52.1 across 142 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 →