BenchLeader
Menu
Best LLMsChoose a modelCoding plansLeaderboardsModelsBenchmarksProvidersReleases12

Claude Opus 4.6 vs Kimi K3.1 Pro Together

Full profiles: Claude Opus 4.6 (Anthropic) and Kimi K3.1 Pro Together (Moonshot AI): every benchmark, price and rank.

Updated · Data from 8 sources

As of 9 Nov 2026, Claude Opus 4.6 and Kimi K3.1 Pro Together are neck and neck on quality (62.7 vs 60.5 on the BenchLeader Index, inside the ±3.3 margin). Kimi K3.1 Pro Together leads in every category we measure both in.

Key facts (as of 9 Nov 2026)

  • Claude Opus 4.6 scores 62.7 on the BenchLeader Index and Kimi K3.1 Pro Together scores 60.5 (BenchLeader, 9 Nov 2026).
  • Claude Opus 4.6 costs $10.00 per 1M tokens blended (list price, 9 Nov 2026).
Claude Opus 4.6 benchmarks high Anthropic Kimi K3.1 Pro Together benchmarks max Moonshot AIAdd a third model

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
MathsKimi K3.1 Pro Together66 vs 64 category scoreMedium
ReasoningKimi K3.1 Pro Together67 vs 65 category scoreMedium
Chat quality (human preference)–Only Claude Opus 4.6 has an Arena rating (70 category score)Not comparable
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • Claude Opus 4.6 (high) and Kimi K3.1 Pro Together (max) are neck and neck on quality (62.7 vs 60.5, within the ±3.3 margin).
  • Claude Opus 4.6 (high) is stronger in coding, human preference, multimodal.
  • Kimi K3.1 Pro Together (max) is stronger in maths, reasoning, knowledge.

At a glance

SpecClaude Opus 4.6Kimi K3.1 Pro Together
DeveloperAnthropicMoonshot AI
Released5 Feb 202623 Oct 2026
Context window (tokens)1,000,000–
Compared atClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)
Open weightsNoNo
Price in / out / cached input per 1M$5.00 / $25.00 / $0.500–

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.

CategoryClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)Leader
Reasoning65.167.1Kimi K3.1 Pro Together
Coding62.6–not comparable
Maths63.765.8Kimi K3.1 Pro Together
Knowledge–67.6not comparable
Human preference69.9–not comparable
Multimodal66.7–not comparable

On one chart

Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.

2550 avg75ReasoningCodingMathsKnowledgeMultimodalHumanpreferenceClaude Opus 4.6, Reasoning: 65.1Claude Opus 4.6, Coding: 62.6Claude Opus 4.6, Maths: 63.7Claude Opus 4.6, Multimodal: 66.7Claude Opus 4.6, Human preference: 69.9Kimi K3.1 Pro Together, Reasoning: 67.1Kimi K3.1 Pro Together, Maths: 65.8Kimi K3.1 Pro Together, Knowledge: 67.6

Dashed: that model has no result on the axis it crosses; the line only joins its neighbours.

Not drawn, no result for these models: Agents & tools, Instruction following, Long context.

ModelIndex

Where sources disagree

Across the 0 benchmarks both have, Claude Opus 4.6 leads 0, Kimi K3.1 Pro Together leads 0.

Overall indexClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)Leader
BenchLeader Index62.760.5neck and neck

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. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.

MeasureClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)
Blended price per 1M tokens$10.00–
Input / output per 1M$5.00 / $25.00– / –
Cached input per 1M$0.500–
Price per task: chat reply$0.0095–
Price per task: summarize a 30-page report$0.075–
Price per task: code edit$0.068–
Price per task: agentic coding session$0.400–
Price per task: structured extraction$0.015–
Agentic coding session, input mostly cached$0.198–
Output speed39 tok/s–
Time to first answer1.1 s–

Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 4.6 (high) scores 62.7 and takes 1 s to its first answer token; Claude Opus 4.6 (thinking) scores 62.6 in 1 s, Claude Opus 4.6 scores 62.3 in 1 s. Kimi K3.1 Pro Together (max) scores 60.5. 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

110 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.

MetricPublisherClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)
BenchLeader IndexBenchLeader62.760.5
Coding scoreBenchLeader62.6–
Human preference scoreBenchLeader69.9–
Maths scoreBenchLeader63.765.8
Multimodal scoreBenchLeader66.7–
Reasoning scoreBenchLeader65.167.1
Knowledge scoreBenchLeader–67.6
Blended price $/MBenchLeader$10.00–
Output speedBenchLeader39 tok/s–
Time to first answerBenchLeader1.1 s–
Context windowBenchLeader1,000,000 tokens–
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub90.5%†94.2%
Humanity's Last ExamScale AI / CAIS19.0%†–
SimpleBenchSimpleBench67.6%†–
Arena Hard PromptsArena (LMArena)1530–
LiveBench 1.0 ReasoningLiveBench88.7%–
GPQA Diamond (AA)Artificial Analysis84.0%†–
Humanity's Last Exam (AA)Artificial Analysis19.1%†–
GPQA Diamond (Vals)Vals AI89.7%†–
Kagi LLM BenchmarkKagi LLM Benchmark72.4%†–
ARC-AGI-1ARC Prize94.0%–
ARC-AGI-2ARC Prize69.2%–
ARC-AGI-3ARC Prize0.5%†–
CritPtArtificial Analysis2.8%†–
Chess PuzzlesEpoch AI Benchmarking Hub21.0%†45.0%
Mystery Game PuzzlesEpoch AI Benchmarking Hub15.0%†42.0%
CL-benchEpoch AI Benchmarking Hub20.7%†–
CL-bench LifeEpoch AI Benchmarking Hub17.0%–
LMCAEpoch AI Benchmarking Hub55.8%†–
DTBenchEpoch AI Benchmarking Hub91.2%†–
ForecastBenchForecastBench63.4%†–
Coding
SWE-bench Verified (Epoch)Epoch AI Benchmarking Hub78.7%†–
WeirdMLWeirdML78.0%–
FrontierCodeCognition26.6%†–
GSO-BenchGSO-Bench41.2%–
Arena CodingArena (LMArena)1547–
Show all 110 benchmarks
MetricPublisherClaude Opus 4.6 (high)Kimi K3.1 Pro Together (max)
Coding
Arena WebDevArena (LMArena)1547–
LiveBench 1.0 CodingLiveBench78.2%–
LiveCodeBenchVals AI84.7%†–
SWE-bench (Vals)Vals AI78.2%†–
SWE-Bench ProScale AI SEAL51.9%†–
SWE-bench Verified (bash only)SWE-bench75.6%†–
SWE-bench Verified (any scaffold)SWE-bench75.6%†–
Vibe Code Bench v1.1Vals AI57.6%†–
SWE-Bench Pro (private)Scale AI SEAL51.9%†–
SWE Atlas: Codebase QnAScale AI SEAL33.3%†–
SWE Atlas: RefactoringScale AI SEAL35.6%†–
SWE Atlas: Test WritingScale AI SEAL36.7%†–
ALE-BenchEpoch AI Benchmarking Hub996.5†–
AlgoTuneAlgoTune1.5–
Design Arena WebsiteDesign Arena1287†–
Agents & tools
Terminal-BenchTerminal-Bench79.8%†–
CybenchCybench93.0%†–
Remote Labor IndexScale AI / CAIS4.2%†–
APEX-AgentsMercor46.3%†–
LiveBench Agentic CodingLiveBench49.0%–
Terminal-Bench HardArtificial Analysis48.5%†–
τ²-Bench Telecom (AA)Artificial Analysis84.8%†–
MCP AtlasScale AI SEAL76.8%†–
HiL-BenchScale AI SEAL38.3%†–
τ³-bench Bankingτ²-bench27.3%†–
APEX-Agents (AA)Artificial Analysis33.0%†–
Terminal-Bench 2.0 (Vals)Vals AI58.4%†–
Arena SearchArena (LMArena)1222†–
DeepResearch BenchDeepResearch Bench55.3%–
METR Time HorizonsMETR78.9%†–
GBAEvalEpoch AI Benchmarking Hub44.1%†–
Vending-Bench 2Andon Labs8017.6†–
Design Arena AgenticDesign Arena1192†–
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub66.0%†78.3%
FrontierMath Tier 4Epoch AI Benchmarking Hub26.8%†65.8%
OTIS Mock AIMEEpoch AI Benchmarking Hub94.4%†100.0%
ProofBenchVals AI50.0%†–
LiveBench MathematicsLiveBench89.3%–
AIME (Vals)Vals AI95.6%†–
AIME 2026MathArena96.7%–
HMMT February 2026MathArena96.2%–
USAMO 2026MathArena47.0%–
MathArena ApexMathArena34.5%–
Arena MathsArena (LMArena)1516–
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub47.0%†62.2%
LiveBench 1.0 Data AnalysisLiveBench69.9%–
MMLU-ProVals AI89.1%†–
LegalBenchVals AI85.3%†–
CorpFinVals AI67.0%†–
TaxEvalVals AI76.0%†–
MedQAVals AI95.4%†–
PRBench FinanceScale AI SEAL53.3%†–
PRBench LegalScale AI SEAL52.3%†–
MultiNRCScale AI SEAL48.3%†–
CaseLaw v2Vals AI62.1%†–
MedCodeVals AI48.2%†–
MedScribeVals AI86.7%†–
DrugDiscoveryBenchScale AI SEAL27.7%†–
EBR-benchEpoch AI Benchmarking Hub12.7%†–
Global-MMLU-Lite (AA)Artificial Analysis92.2%†–
Instruction following
LiveBench LanguageLiveBench83.3%–
LiveBench Instruction FollowingLiveBench63.3%–
IFBenchArtificial Analysis44.6%†–
MultiChallengeScale AI SEAL56.0%†–
TutorBenchScale AI SEAL53.5%†–
Arena Instruction FollowingArena (LMArena)1511–
Human preference
Arena TextArena (LMArena)1503–
EQ-Bench 4EQ-Bench1210†–
Arena Creative WritingArena (LMArena)1498–
Arena Multi-turnArena (LMArena)1517–
Arena Longer QueriesArena (LMArena)1522–
Multimodal
Arena VisionArena (LMArena)1316–
MMMU-ProArtificial Analysis72.5%†–
VISTAScale AI SEAL45.5%†–
MMMU-Pro (Vals)Vals AI83.9%†–
MortgageTaxVals AI68.5%†–
VTBScale AI SEAL27.5%†–
Long context
AA-LCR v1.1Artificial Analysis67.0%†–
Arena DocumentArena (LMArena)1497–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub156.3†–
LiveBench 1.0LiveBench74.5%–
AA Intelligence Index v4.3.2Artificial Analysis26.4†–
AA Progress Index v1Artificial Analysis274.5†–
Safety & honesty
FORTRESSScale AI SEAL20.5%†–
MASKScale AI SEAL96.3%†–

Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.

What changed

  • Kimi K3.1 Pro Together (max): first results.
  • Claude Opus 4.6 (high): 1 new result (USAMO 2026).

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

Claude Opus 4.6 vs Kimi K3.1 Pro Together: questions

Is Claude Opus 4.6 better than Kimi K3.1 Pro Together?
Claude Opus 4.6 (high) and Kimi K3.1 Pro Together (max) are neck and neck on quality (62.7 vs 60.5, within the ±3.3 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 9 Nov 2026, so check the category scores for your use.
When was Kimi K3.1 Pro Together released?
Kimi K3.1 Pro Together was released on 23 Oct 2026.