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Alibaba · model passport

Qwen1 5 72b Chat

qwen1-5-72b-chat
  • Open weights
  • Qianwen LICENSE licence
  • 72.3B params
  • Released 2024-02-05(curated)
6/27axes covered
Overall · LMArena
1,234#310 of 396
Best percentile
22.5Coding
Cohort standing
28.9median 30.2

What the data says

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

Weakest axes

  • 9.5 pct — GPQA Diamond (28.8%)
  • 11.7 pct — LiveBench (28.9%)

Uneven: 13 percentile points between its best and worst axis — 22.5 pct on Coding versus 9.5 pct on GPQA Diamond.

  • Coding22.5 pct1,275Verify →
  • Hard prompts22.2 pct1,240Verify →
  • Overall22 pct1,234Verify →
  • Instruction following22 pct1,212Verify →
  • LiveBench11.7 pct28.9%Verify →
  • GPQA Diamond9.5 pct28.8%Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot 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
  • 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

RepositoryQwen/Qwen1.5-72B-Chat
Parameters72.3B
ArchitectureQwen2ForCausalLM
LicenceQianwen LICENSE

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen1.5-72B-Chat"
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.

  • qwen1.5-72b-chat stands at 28.9 (raw mean 18.3) — measured on 6 of 27 axes.

    standingtemplateVerify →

  • qwen1.5-72b-chat stands at 28.9 versus a cohort median of 30.2 across 94 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 →