BenchLeader
Menu
Best LLMsChoose a modelLeaderboardsModelsBenchmarksProvidersReleases9

GPT-5.5 vs Qwen3.6 Max

Full profiles: GPT-5.5 (OpenAI) and Qwen3.6 Max (Alibaba): every benchmark, price and rank.

Updated · Data from 11 sources

As of 27 Oct 2026, GPT-5.5 (xhigh) and Qwen3.6 Max (max) are neck and neck on quality (66.0 vs 62.2 on the BenchLeader Index, inside the ±4.8 margin of error), and Qwen3.6 Max (max) costs 3.8× less, which makes it the better buy for most workloads. Pick GPT-5.5 for reasoning (75 vs 58); pick Qwen3.6 Max for agents & tools (72 vs 62).

GPT-5.5 benchmarks xhigh OpenAI Qwen3.6 Max benchmarks max AlibabaOpen in the comparison tool

Which to pick for…

The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.

Use casePickWhyConfidence
CodingGPT-5.561 vs 57 category score; Arena WebDev 1513 vs 1482Medium
Agents & tool useQwen3.6 Max72 vs 62 category score; Terminal-Bench Hard 43.9% vs 60.6%High
MathsGPT-5.570 vs 65 category scoreMedium
ReasoningGPT-5.575 vs 58 category score; CritPt 27.1% vs 3.7%High
KnowledgeGPT-5.565 vs 62 category score; SimpleQA Verified 63.0% vs 52.0%Medium
Long documentsGPT-5.567 vs 65 category score; AA-LCR 84.3% vs 80.7%Medium
Following instructionsEither74 vs 75 category score; IFBench 75.8% vs 76.6%Either
Chat quality (human preference)–Only Qwen3.6 Max has an Arena rating (65 category score)Not comparable
Lowest costQwen3.6 Max$2.92 vs $11.25 per 1M blended; agentic coding session $0.109 vs $0.420–
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • GPT-5.5 (xhigh) and Qwen3.6 Max (max) are neck and neck on quality (66.0 vs 62.2, within the ±4.8 margin).
  • GPT-5.5 (xhigh) is stronger in coding, composite, knowledge, long context, maths, multimodal, reasoning.
  • Qwen3.6 Max (max) is stronger in agents & tools, instruction following, human preference.
  • Qwen3.6 Max (max) is 3.8× cheaper ($2.92 vs $11.25 per 1M blended).

At a glance

SpecGPT-5.5Qwen3.6 Max
DeveloperOpenAIAlibaba
Released23 Apr 202620 Apr 2026
Context window (tokens)1,050,000262,144
Compared atGPT-5.5 (xhigh)Qwen3.6 Max (max)
Open weightsNoNo
Price in / out / cached input per 1M$5.00 / $30.00 / $0.500$1.30 / $7.80 / $0.130

Category by category

Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.

CategoryGPT-5.5 (xhigh)Qwen3.6 Max (max)Leader
Reasoning75.258.1GPT-5.5
Coding61.156.6GPT-5.5
Agents & tools62.372.1Qwen3.6 Max
Maths69.764.9GPT-5.5
Knowledge65.262.1GPT-5.5
Instruction following73.974.6level
Human preference–64.8not comparable
Multimodal62.6–not comparable
Long context67.165.2GPT-5.5
Composite71.960.9GPT-5.5

Where sources disagree

Across the 17 benchmarks both have, GPT-5.5 leads 14, Qwen3.6 Max leads 3. Every overall index that rates both puts GPT-5.5 ahead.

Overall indexGPT-5.5 (xhigh)Qwen3.6 Max (max)Leader
BenchLeader Index66.062.2neck and neck
AA Intelligence Index v4.3.238.428.4GPT-5.5
Arena Text1482†1461GPT-5.5

Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).

Price, speed and latency

List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.

MeasureGPT-5.5 (xhigh)Qwen3.6 Max (max)
Blended price per 1M tokens$11.25$2.92
Input / output per 1M$5.00 / $30.00$1.30 / $7.80
Cached input per 1M$0.500$0.130
Price per task: chat reply$0.011$0.0029
Price per task: summarize a 30-page report$0.078$0.020
Price per task: code edit$0.075$0.019
Price per task: agentic coding session$0.420$0.109
Price per task: structured extraction$0.016$0.0042
Cost to run a full benchmark suite$2.10–
Output speed90 tok/s–
Time to first answer76.5 s–
Time to a complete answer82.0 s–

Each model is compared at its best-ranked reasoning-effort setting. GPT-5.5 (xhigh) scores 66.0 and takes 76 s to its first answer token; GPT-5.5 (high) scores 65.2 in 30 s, GPT-5.5 (medium) scores 62.8 in 6 s. Qwen3.6 Max (max) scores 62.2. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.

Every benchmark

124 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.

MetricPublisherGPT-5.5 (xhigh)Qwen3.6 Max (max)
BenchLeader IndexBenchLeader66.062.2
Agents & tools scoreBenchLeader62.372.1
Coding scoreBenchLeader61.156.6
Composite scoreBenchLeader71.960.9
Instruction following scoreBenchLeader73.974.6
Knowledge scoreBenchLeader65.262.1
Long context scoreBenchLeader67.165.2
Maths scoreBenchLeader69.764.9
Multimodal scoreBenchLeader62.6–
Reasoning scoreBenchLeader75.258.1
Human preference scoreBenchLeader–64.8
Blended price $/MBenchLeader$11.25$2.92
Output speedBenchLeader90 tok/s–
Time to first answerBenchLeader76.5 s–
Context windowBenchLeader1,050,000 tokens262,144 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub90.7%†87.4%
SimpleBenchSimpleBench69.0%†63.0%
Arena Hard PromptsArena (LMArena)1499†1483
LiveBench 1.0 ReasoningLiveBench89.7%–
GPQA Diamond (AA)Artificial Analysis93.5%88.8%
Humanity's Last Exam (AA)Artificial Analysis45.8%30.8%
GPQA Diamond (Vals)Vals AI93.2%–
Kagi LLM BenchmarkKagi LLM Benchmark88.8%†–
ARC-AGI-1ARC Prize95.0%–
ARC-AGI-2ARC Prize85.0%–
ARC-AGI-3ARC Prize0.4%†–
CritPtArtificial Analysis27.1%3.7%
Chess PuzzlesEpoch AI Benchmarking Hub63.0%–
Mystery Game PuzzlesEpoch AI Benchmarking Hub56.0%19.0%†
CL-bench LifeEpoch AI Benchmarking Hub22.2%†–
LMCAEpoch AI Benchmarking Hub54.3%42.5%
DTBenchEpoch AI Benchmarking Hub96.0%87.2%
ForecastBenchForecastBench64.4%†–
BTF-3Epoch AI Benchmarking Hub14.3%†–
Coding
SWE-bench Verified (Epoch)Epoch AI Benchmarking Hub–76.7%
SciCodeSciCode55.8%–
WeirdMLWeirdML84.9%–
FrontierCodeCognition43.0%†–
GSO-BenchGSO-Bench40.2%–
Arena CodingArena (LMArena)1511†1512
Show all 124 benchmarks
MetricPublisherGPT-5.5 (xhigh)Qwen3.6 Max (max)
Coding
Arena WebDevArena (LMArena)15131482
LiveBench 1.0 CodingLiveBench82.2%–
LiveCodeBenchVals AI85.3%–
SWE-bench (Vals)Vals AI82.6%72.8%
Code MigrationVals AI45.2%–
ProgramBenchVals AI0.5%–
Vibe Code Bench v1.1Vals AI69.8%–
SWE Atlas: Codebase QnAScale AI SEAL45.4%–
SWE Atlas: RefactoringScale AI SEAL44.8%–
SWE Atlas: Test WritingScale AI SEAL42.6%–
MirrorCodeEpoch AI Benchmarking Hub10.0%†–
Surface Evolver BenchEpoch AI Benchmarking Hub88.1%†–
DeepSWE v1.1Epoch AI Benchmarking Hub67.0%–
ALE-BenchEpoch AI Benchmarking Hub1943.0–
Terminal-Bench 4.0 (AA)Artificial Analysis14.7%–
Terminal-Bench 2.1 (AA)Artificial Analysis84.3%–
Agents & tools
Terminal-BenchTerminal-Bench84.7%†–
OSWorld-Verified 2.0OSWorld13.0%–
Remote Labor IndexScale AI / CAIS6.3%†–
APEX-AgentsMercor55.1%†–
LiveBench Agentic CodingLiveBench54.0%–
Terminal-Bench HardArtificial Analysis60.6%43.9%
τ²-Bench Telecom (AA)Artificial Analysis93.9%95.9%
Terminal-Bench 2.1 (Vals)Vals AI76.4%†–
MCP AtlasScale AI SEAL75.3%–
HiL-BenchScale AI SEAL39.7%†–
τ³-bench Bankingτ²-bench44.6%–
τ³-Banking (AA)Artificial Analysis39.0%–
ITBench SRE (AA)Artificial Analysis45.8%–
Analyst Agent (AA)Artificial Analysis50.0%–
APEX-Agents (AA)Artificial Analysis37.7%–
Finance Agent v2Vals AI51.8%–
Harvey's Legal Agent Benchmark v1Vals AI3.8%–
Legal Research BenchVals AI40.4%–
SkillsBenchVals AI62.2%–
SREBenchVals AI3.8%–
Tax Agent BenchVals AI21.8%–
Terminal-Bench 2.0 (Vals)Vals AI73.2%†51.7%
Time Horizon Index: KSPVals AI8.3%–
DeepResearch BenchDeepResearch Bench54.0%†–
PostTrainBenchEpoch AI Benchmarking Hub27.2%–
ExploitBenchEpoch AI Benchmarking Hub47.4%†–
GBAEvalEpoch AI Benchmarking Hub53.2%†–
Vending-Bench 2Andon Labs7523.8†4254.2
AutomationBenchArtificial Analysis47.3%–
GDP.pdf (AA)Artificial Analysis21.2%–
EnterpriseOps-GymArtificial Analysis46.6%–
AA-Briefcase v1.1Artificial Analysis1136–
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub85.3%–
FrontierMath Tier 4Epoch AI Benchmarking Hub72.5%–
OTIS Mock AIMEEpoch AI Benchmarking Hub84.4%†91.1%
ProofBenchVals AI50.0%–
LiveBench MathematicsLiveBench95.9%–
AIME 2026MathArena100.0%–
HMMT February 2026MathArena98.5%–
USAMO 2026MathArena98.2%–
MathArena ApexMathArena80.2%–
Arena MathsArena (LMArena)1501†1476
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub63.0%52.0%
LiveBench 1.0 Data AnalysisLiveBench81.6%–
AA-OmniscienceArtificial Analysis20.59.2
MMLU-ProVals AI88.1%–
LegalBenchVals AI86.5%–
CorpFinVals AI68.4%66.5%
TaxEvalVals AI75.0%–
CaseLaw v2Vals AI66.2%47.9%
Excel Modeling BenchmarkVals AI64.5%–
MedCodeVals AI49.1%–
MedScribeVals AI86.9%–
Public Benefits Bench v1.1Vals AI60.9%–
DrugDiscoveryBenchScale AI SEAL51.6%–
EBR-benchEpoch AI Benchmarking Hub34.3%–
GDP.pdfEpoch AI Benchmarking Hub26.0%–
AA-Omniscience: accuracyArtificial Analysis58.0%37.9%
AA-Omniscience: non-hallucinationArtificial Analysis11.0%53.8%
Instruction following
LiveBench LanguageLiveBench87.4%–
LiveBench Instruction FollowingLiveBench70.7%–
IFBenchArtificial Analysis75.8%76.6%
Arena Instruction FollowingArena (LMArena)1475†1452
Human preference
Arena TextArena (LMArena)1477†1461
EQ-Bench 4EQ-Bench1294†–
Arena Creative WritingArena (LMArena)1448†1438
Arena Multi-turnArena (LMArena)1484†1468
Arena Longer QueriesArena (LMArena)1487†1474
Multimodal
Arena VisionArena (LMArena)1297†–
MMMU-ProArtificial Analysis79.9%–
MMMU-Pro (Vals)Vals AI88.3%–
MortgageTaxVals AI68.8%–
SAGEVals AI51.5%–
Blueprint-Bench 2Epoch AI Benchmarking Hub36.2%†–
Long context
AA-LCRArtificial Analysis84.3%80.7%
Arena DocumentArena (LMArena)1483†–
MLCRArtificial Analysis28.9%–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub159.7†149.4
LiveBench 1.0LiveBench80.2%–
AA Intelligence Index v4.3.2Artificial Analysis38.428.4
Vals RSI IndexVals AI16.5%–
Vals Multimodal IndexVals AI68.1%–
Safety & honesty
FORTRESSScale AI SEAL16.3%–

Data as of 2026-10-27. Best configuration of each model; every score links to its source on the model pages.

What changed

  • GPT-5.5 (xhigh): cost per task moved from $2.63/M to $2.10/M (20% down).
  • GPT-5.5 (xhigh): 1 new result (AA-Briefcase v1.1).
  • GPT-5.5 (xhigh): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
  • GPT-5.5 (xhigh): 1 score revision.
  • GPT-5.5 (xhigh): 6 new results (USAMO 2026, AutomationBench, GDP.pdf (AA)…).
  • GPT-5.5 (xhigh): BenchLeader Index 66.2 → 65.7.
  • Qwen3.6 Max (max): BenchLeader Index 62.8 → 62.3.
  • GPT-5.5 (xhigh): 1 score revision.
  • GPT-5.5 (xhigh): 1 new result (Chess Puzzles); 1 score revision.
  • GPT-5.5 (xhigh): 1 new result (SWE Atlas: Codebase QnA).

Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs

GPT-5.5 vs Qwen3.6 Max: questions

Is GPT-5.5 better than Qwen3.6 Max?
GPT-5.5 (xhigh) and Qwen3.6 Max (max) are neck and neck on quality (66.0 vs 62.2, within the ±4.8 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-27, so check the category scores for your use.
Which is cheaper, GPT-5.5 or Qwen3.6 Max, and will that change?
Qwen3.6 Max is cheaper today: $2.92 against $11.25 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
Is GPT-5.5 better than Qwen3.6 Max for coding?
GPT-5.5 scores higher in coding (61 vs 57 on the category index, where 50 is average). On Arena WebDev, the top shared coding benchmark, it is 1513 for GPT-5.5 against 1482 for Qwen3.6 Max.
Is GPT-5.5 better than Qwen3.6 Max for agents and tool use?
Qwen3.6 Max scores higher in agentic tasks (72 vs 62 on the category index, where 50 is average). On Terminal-Bench Hard, the top shared agents & tools benchmark, it is 60.6% for GPT-5.5 against 43.9% for Qwen3.6 Max.
Which is better for long documents?
GPT-5.5 scores higher on long-context benchmarks (67 vs 65). GPT-5.5 accepts more context: 1,050,000 against 262,144 tokens for Qwen3.6 Max.
Do independent benchmarks agree on GPT-5.5 vs Qwen3.6 Max?
Across the 17 benchmarks both have, GPT-5.5 leads 14, Qwen3.6 Max leads 3. Every overall index that rates both puts GPT-5.5 ahead.
When was GPT-5.5 released?
GPT-5.5 was released on 23 Apr 2026.