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aiqbench

Alibaba · model passport

Qwen: Qwen3.5-27B

qwen3-5-27b
  • Open weights
  • Apache 2.0 licence
  • 27.8B params
  • 262,144 context
  • Released 2026-02-24(HF repo created)
9/27axes covered
Overall · LMArena
1,409#140 of 396
Best percentile
92.0IFBench (Artificial Analysis)
Cohort standing
53.2median 57.4

What the data says

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

Strongest axes

  • 92 pct — IFBench (Artificial Analysis) (75.6%)

Uneven: 67.2 percentile points between its best and worst axis — 92 pct on IFBench (Artificial Analysis) versus 24.8 pct on HHEM Hallucination (Vectara).

  • IFBench (Artificial Analysis)92 pct75.6%Verify →
  • Instruction following66.7 pct1,401Verify →
  • Hard prompts65.9 pct1,428Verify →
  • Overall64.9 pct1,409Verify →
  • Coding63.7 pct1,451Verify →
  • Vision Arena54.7 pct1,217Verify →
  • Factuality Arena29.7 pct1,420Verify →
  • WebDev Arena26.6 pct1,358Verify →
  • HHEM Hallucination (Vectara)24.8 pct87.9%Verify →
  • 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
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (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/Qwen3.5-27B
Parameters27.8B
ArchitectureQwen3_5ForConditionalGeneration
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Qwen/Qwen3.5-27B"
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/qwen3.5-27b
Context length262,144 tokens
Max output81,920 tokens
Acceptstext, image, video
Input price$0.26 per 1M tokens
Output price$2.60 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":"qwen/qwen3.5-27b","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.

  • qwen3.5-27b stands at 53.2 (raw mean 54.3) — measured on 9 of 27 axes.

    standingtemplateVerify →

  • qwen3.5-27b stands at 53.2 versus a cohort median of 57.4 across 106 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 →