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aiqbench

Alibaba · model passport

Qwen: Qwen3.6 Plus

qwen3-6-plus
  • Cloud only
  • Proprietary licence
  • 1,000,000 context
  • Released 2026-10-09(first seen on LMArena)
13/27axes covered
Overall · LMArena
1,443#80 of 396
Best percentile
90.4IFBench (Artificial Analysis)
Cohort standing
64.5median 60.6

What the data says

Benchmarked on 13 of 27 tracked axes. Every figure below is the model's own published result on that board.

Strongest axes

  • 90.4 pct — IFBench (Artificial Analysis) (75.2%)

Weakest axes

  • 12.5 pct — SWE-bench Verified (57.9%)

Uneven: 77.9 percentile points between its best and worst axis — 90.4 pct on IFBench (Artificial Analysis) versus 12.5 pct on SWE-bench Verified.

  • IFBench (Artificial Analysis)90.4 pct75.2%Verify →
  • Hard prompts81.3 pct1,467Verify →
  • Instruction following80.8 pct1,435Verify →
  • Overall80.1 pct1,443Verify →
  • Coding80.1 pct1,496Verify →
  • LiveBench79 pct68.9%Verify →
  • AIME (math)77 pct93.3%Verify →
  • GPQA Diamond76.9 pct88.4%Verify →
  • FrontierMath68.1 pct26.2%Verify →
  • WebDev Arena54.7 pct1,462Verify →
  • SimpleQA Verified51.2 pct44.1%Verify →
  • Factuality Arena49.4 pct1,438Verify →
  • SWE-bench Verified12.5 pct57.9%Verify →
  • Vision ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot 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
  • HHEM Hallucination (Vectara)Not 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

Use it hosted

API model IDqwen/qwen3.6-plus
Context length1,000,000 tokens
Max output65,536 tokens
Acceptstext, image, video
Input price$0.33 per 1M tokens
Output price$1.95 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogprobsMax tokensPresence penaltyReasoningResponse 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/qwen3.6-plus","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.

  • qwen3.6-plus stands at 64.5 (raw mean 67.8) — measured on 13 of 27 axes.

    standingtemplateVerify →

  • qwen3.6-plus stands at 64.5 versus a cohort median of 60.6 across 171 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 →