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Claude Opus 4.6 vs Kimi K3.1 Pro

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

Updated · Data from 8 sources

As of 24 Oct 2026, Claude Opus 4.6 (high) and Kimi K3.1 Pro are neck and neck on quality (62.7 vs 66.8 on the BenchLeader Index, inside the ±6.6 margin of error), and Kimi K3.1 Pro costs 2.5× less, which makes it the better buy for most workloads. Kimi K3.1 Pro leads in every category we measure both in.

Claude Opus 4.6 benchmarks high Anthropic Kimi K3.1 Pro benchmarks Moonshot AIOpen 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
ReasoningKimi K3.1 Pro81 vs 66 category scoreHigh
Images and other mediaEither67 vs 67 category scoreEither
Chat quality (human preference)–Only Claude Opus 4.6 has an Arena rating (70 category score)Not comparable
Lowest costKimi K3.1 Pro$4.00 vs $10.00 per 1M blended; agentic coding session $0.160 vs $0.400–
Fastest first answerClaude Opus 4.61.2 s (Claude Opus 4.6 (high)) vs 27.7 s (Kimi K3.1 Pro) to the first answer token–
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 are neck and neck on quality (62.7 vs 66.8, within the ±6.6 margin).
  • Claude Opus 4.6 (high) is stronger in coding, human preference, maths.
  • Kimi K3.1 Pro is stronger in multimodal, reasoning, agents & tools, composite, knowledge, long context.
  • Kimi K3.1 Pro is 2.5× cheaper ($4.00 vs $10.00 per 1M blended).
  • Kimi K3.1 Pro streams 2.0× faster (81 vs 40 tokens per second).

At a glance

SpecClaude Opus 4.6Kimi K3.1 Pro
DeveloperAnthropicMoonshot AI
Released5 Feb 202623 Oct 2026
Context window (tokens)1,000,0001,048,576
Compared atClaude Opus 4.6 (high)Kimi K3.1 Pro
Open weightsNoNo
Price in / out / cached input per 1M$5.00 / $25.00 / $0.500$2.00 / $10.00 / $0.200

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 ProLeader
Reasoning65.581.3Kimi K3.1 Pro
Coding62.8–not comparable
Agents & tools–67.3not comparable
Maths63.5–not comparable
Knowledge–74.8not comparable
Human preference70.0–not comparable
Multimodal66.667.0level
Long context–68.3not comparable
Composite–75.8not comparable

Where sources disagree

Across the 0 benchmarks both have, Claude Opus 4.6 leads 0, Kimi K3.1 Pro leads 0. Every overall index that rates both puts Kimi K3.1 Pro ahead.

Overall indexClaude Opus 4.6 (high)Kimi K3.1 ProLeader
BenchLeader Index62.766.8neck and neck
AA Intelligence Index v4.3.226.4†55.1Kimi K3.1 Pro

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.

MeasureClaude Opus 4.6 (high)Kimi K3.1 Pro
Blended price per 1M tokens$10.00$4.00
Input / output per 1M$5.00 / $25.00$2.00 / $10.00
Cached input per 1M$0.500$0.200
Price per task: chat reply$0.0095$0.0038
Price per task: summarize a 30-page report$0.075$0.030
Price per task: code edit$0.068$0.027
Price per task: agentic coding session$0.400$0.160
Price per task: structured extraction$0.015$0.0060
Cost to run a full benchmark suite–$2.11
Output speed40 tok/s81 tok/s
Time to first answer1.2 s27.7 s
Time to a complete answer–33.9 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 scores 62.6 in 1 s, Claude Opus 4.6 (thinking) scores 62.5 in 1 s. Kimi K3.1 Pro scores 66.8 and takes 28 s to its first answer token. 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

129 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
BenchLeader IndexBenchLeader62.766.8
Coding scoreBenchLeader62.8–
Human preference scoreBenchLeader70.0–
Maths scoreBenchLeader63.5–
Multimodal scoreBenchLeader66.667.0
Reasoning scoreBenchLeader65.581.3
Agents & tools scoreBenchLeader–67.3
Composite scoreBenchLeader–75.8
Knowledge scoreBenchLeader–74.8
Long context scoreBenchLeader–68.3
Blended price $/MBenchLeader$10.00$4.00
Output speedBenchLeader40 tok/s81 tok/s
Time to first answerBenchLeader1.2 s27.7 s
Context windowBenchLeader1,000,000 tokens1,048,576 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub90.5%†–
Humanity's Last ExamScale AI / CAIS19.0%†–
SimpleBenchSimpleBench67.6%†–
Arena Hard PromptsArena (LMArena)1530–
LiveBench 2.0 ReasoningLiveBench–91.1%
LiveBench 1.0 ReasoningLiveBench88.7%–
GPQA Diamond (AA)Artificial Analysis89.6%†–
Humanity's Last Exam (AA)Artificial Analysis39.9%†52.5%
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 Analysis12.6%†24.6%
Chess PuzzlesEpoch AI Benchmarking Hub21.0%†–
Mystery Game PuzzlesEpoch AI Benchmarking Hub15.0%†–
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%–
Show all 129 benchmarks
MetricPublisherClaude Opus 4.6 (high)Kimi K3.1 Pro
Coding
Arena CodingArena (LMArena)1547–
Arena WebDevArena (LMArena)1546–
LiveBench 2.0 CodingLiveBench–81.8%
LiveBench 1.0 CodingLiveBench78.2%–
SciCode (AA)Artificial Analysis–60.8%
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–
Terminal-Bench 4.0 (AA)Artificial Analysis–67.2%
FrontierCode 1.1 (AA)Artificial Analysis–47.1%
Agents & tools
Terminal-BenchTerminal-Bench79.8%†–
CybenchCybench93.0%†–
Remote Labor IndexScale AI / CAIS4.2%†–
APEX-AgentsMercor46.3%†–
LiveBench Long AgenticLiveBench–49.5%
LiveBench BrowserLiveBench–80.0%
LiveBench Agentic CodingLiveBench49.0%–
Terminal-Bench HardArtificial Analysis46.2%†–
τ²-Bench Telecom (AA)Artificial Analysis92.1%†–
MCP AtlasScale AI SEAL76.8%†–
HiL-BenchScale AI SEAL38.3%†–
τ³-bench Bankingτ²-bench27.3%†–
GDPval-AA v2.1Artificial Analysis–67.3%
APEX-Agents (AA)Artificial Analysis33.0%†–
Terminal-Bench 2.0 (Vals)Vals AI58.4%†–
DeepResearch BenchDeepResearch Bench55.3%–
METR Time HorizonsMETR78.9%†–
GBAEvalEpoch AI Benchmarking Hub44.1%†–
Vending-Bench 2Andon Labs8017.6†–
AutomationBenchArtificial Analysis–73.3%
GDP.pdf (AA)Artificial Analysis–31.4%
AA-Briefcase v1.1Artificial Analysis–1678
CWE-Bench (AA)Artificial Analysis–60.8%
DeepSec-Bench (AA)Artificial Analysis–36.7%
CyberGym end-to-end (AA)Artificial Analysis–94.7%
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub66.0%†–
FrontierMath Tier 4Epoch AI Benchmarking Hub26.8%†–
OTIS Mock AIMEEpoch AI Benchmarking Hub94.4%†–
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)1517–
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub47.0%†–
LiveBench 2.0 Data AnalysisLiveBench–79.1%
LiveBench ResearchLiveBench–77.9%
LiveBench 1.0 Data AnalysisLiveBench69.9%–
AA-OmniscienceArtificial Analysis13.7†29.5
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 SEAL57.1%†–
CaseLaw v2Vals AI62.1%†–
MedCodeVals AI48.2%†–
MedScribeVals AI86.7%†–
DrugDiscoveryBenchScale AI SEAL27.7%†–
EBR-benchEpoch AI Benchmarking Hub12.7%†–
AA-Omniscience: accuracyArtificial Analysis47.0%†51.0%
AA-Omniscience: non-hallucinationArtificial Analysis37.2%†56.1%
Global-MMLU-Lite (AA)Artificial Analysis92.2%†–
Instruction following
LiveBench LanguageLiveBench83.3%–
LiveBench Instruction FollowingLiveBench63.3%–
IFBenchArtificial Analysis53.1%†–
MultiChallengeScale AI SEAL37.1%†–
TutorBenchScale AI SEAL53.7%†–
Arena Instruction FollowingArena (LMArena)1511–
Human preference
Arena TextArena (LMArena)1503–
EQ-Bench 4EQ-Bench1223†–
Arena Creative WritingArena (LMArena)1500–
Arena Multi-turnArena (LMArena)1516–
Arena Longer QueriesArena (LMArena)1521–
Multimodal
Arena VisionArena (LMArena)1316–
LiveBench DesignLiveBench–77.8%
MMMU-ProArtificial Analysis75.4%†83.9%
VISTAScale AI SEAL46.1%†–
MMMU-Pro (Vals)Vals AI83.9%†–
MortgageTaxVals AI68.5%†–
SAGEVals AI51.6%†–
VTBScale AI SEAL27.5%†–
Long context
AA-LCRArtificial Analysis78.0%†86.3%
Arena DocumentArena (LMArena)1495–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub155.7†–
LiveBench 2.0LiveBench–76.7%
LiveBench 1.0LiveBench74.5%–
AA Intelligence Index v4.3.2Artificial Analysis31.9†55.1
AA Cyber IndexArtificial Analysis–64.1
Safety & honesty
FORTRESSScale AI SEAL13.0%†–
MASKScale AI SEAL85.4%†–

Data as of 2026-10-24. Best configuration of each model; every score links to its source on the model pages.

What changed

  • Kimi K3.1 Pro: BenchLeader Index 65.6 → 66.8.
  • Kimi K3.1 Pro: 5 new results (FrontierCode 1.1 (AA), AA Cyber Index, CWE-Bench (AA)…).
  • Kimi K3.1 Pro: first results.
  • Claude Opus 4.6 (high): BenchLeader Index 60.4 → 62.9.
  • Claude Opus 4.6 (high): 1 new result (USAMO 2026).
  • Claude Opus 4.6 (high): BenchLeader Index 61.1 → 60.4.

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

Claude Opus 4.6 vs Kimi K3.1 Pro: questions

Is Claude Opus 4.6 better than Kimi K3.1 Pro?
Claude Opus 4.6 (high) and Kimi K3.1 Pro are neck and neck on quality (62.7 vs 66.8, within the ±6.6 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-24, so check the category scores for your use.
Which is cheaper, Claude Opus 4.6 or Kimi K3.1 Pro, and will that change?
Kimi K3.1 Pro is cheaper today: $4.00 against $10.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
Which is better for long documents?
Both take about 1M tokens: Claude Opus 4.6 1,000,000, Kimi K3.1 Pro 1,048,576.
Which has lower latency?
Claude Opus 4.6 (high) answers first: 1.2 s against 27.7 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
Which is faster, Claude Opus 4.6 or Kimi K3.1 Pro?
Kimi K3.1 Pro streams faster: 81 against 40 output tokens per second.
When was Kimi K3.1 Pro released?
Kimi K3.1 Pro was released on 23 Oct 2026.