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Claude Opus 4.8 vs Kimi K3.1 Pro Together

Full profiles: Claude Opus 4.8 (Anthropic) and Kimi K3.1 Pro Together (Moonshot AI): every benchmark, price and rank.

Updated · Data from 6 sources

As of 9 Nov 2026, Claude Opus 4.8 and Kimi K3.1 Pro Together are neck and neck on quality (60.5 vs 60.5 on the BenchLeader Index, inside the ±5.9 margin). Choose Claude Opus 4.8 for maths (67 vs 66); choose Kimi K3.1 Pro Together for reasoning (67 vs 60).

Key facts (as of 9 Nov 2026)

  • Claude Opus 4.8 scores 60.5 on the BenchLeader Index and Kimi K3.1 Pro Together scores 60.5 (BenchLeader, 9 Nov 2026).
  • Claude Opus 4.8 costs $10.00 per 1M tokens blended (list price, 9 Nov 2026).
Claude Opus 4.8 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
MathsClaude Opus 4.867 vs 66 category scoreMedium
ReasoningKimi K3.1 Pro Together67 vs 60 category scoreMedium
Chat quality (human preference)–Only Claude Opus 4.8 has an Arena rating (67 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 4.8 (high) and Kimi K3.1 Pro Together (max) are neck and neck on quality (60.5 vs 60.5, within the ±5.9 margin).
  • Claude Opus 4.8 (high) is stronger in coding, human preference, maths, multimodal.
  • Kimi K3.1 Pro Together (max) is stronger in reasoning, knowledge.

At a glance

SpecClaude Opus 4.8Kimi K3.1 Pro Together
DeveloperAnthropicMoonshot AI
Released28 May 202623 Oct 2026
Context window (tokens)1,000,000–
Compared atClaude Opus 4.8 (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 4.8 (high)Kimi K3.1 Pro Together (max)Leader
Reasoning60.067.1Kimi K3.1 Pro Together
Coding61.4–not comparable
Maths67.465.8Claude Opus 4.8
Knowledge–67.6not comparable
Human preference67.3–not comparable
Multimodal64.0–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 4.8, Reasoning: 60.0Claude Opus 4.8, Coding: 61.4Claude Opus 4.8, Maths: 67.4Claude Opus 4.8, Multimodal: 64.0Claude Opus 4.8, Human preference: 67.3Kimi 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 4.8 leads 0, Kimi K3.1 Pro Together leads 0.

Overall indexClaude Opus 4.8 (high)Kimi K3.1 Pro Together (max)Leader
BenchLeader Index60.560.5neck 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 4.8 (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 speed71 tok/s–
Time to first answer0.8 s–

Each model is compared at its best-ranked reasoning-effort setting. Claude Opus 4.8 (high) scores 60.5 and takes 1 s to its first answer token; Claude Opus 4.8 (max) scores 60.2 in 37 s, Claude Opus 4.8 scores 60.1 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

120 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 4.8 (high)Kimi K3.1 Pro Together (max)
BenchLeader IndexBenchLeader60.560.5
Coding scoreBenchLeader61.4–
Human preference scoreBenchLeader67.3–
Maths scoreBenchLeader67.465.8
Multimodal scoreBenchLeader64.0–
Reasoning scoreBenchLeader60.067.1
Knowledge scoreBenchLeader–67.6
Blended price $/MBenchLeader$10.00–
Output speedBenchLeader71 tok/s–
Time to first answerBenchLeader0.8 s–
Context windowBenchLeader1,000,000 tokens–
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub91.0%†94.2%
SimpleBenchSimpleBench64.8%†–
Arena Hard PromptsArena (LMArena)1513–
LiveBench 1.0 ReasoningLiveBench89.2%†–
GPQA Diamond (AA)Artificial Analysis92.0%†–
Humanity's Last Exam (AA)Artificial Analysis48.7%†–
GPQA Diamond (Vals)Vals AI92.4%†–
EnigmaEvalScale AI SEAL23.5%†–
Kagi LLM BenchmarkKagi LLM Benchmark88.8%†–
ARC-AGI-1ARC Prize92.0%–
ARC-AGI-2ARC Prize72.1%–
ARC-AGI-3ARC Prize1.5%–
CritPtArtificial Analysis20.9%†–
Chess PuzzlesEpoch AI Benchmarking Hub52.0%†45.0%
Mystery Game PuzzlesEpoch AI Benchmarking Hub36.0%†42.0%
LMCAEpoch AI Benchmarking Hub57.5%†–
DTBenchEpoch AI Benchmarking Hub94.9%†–
ForecastBenchForecastBench62.7%†–
BTF-3Epoch AI Benchmarking Hub14.0%–
Coding
SciCodeSciCode53.5%†–
WeirdMLWeirdML70.5%†–
FrontierCodeCognition46.5%†–
GSO-BenchGSO-Bench47.1%†–
Arena CodingArena (LMArena)1534–
Arena WebDevArena (LMArena)1555–
Show all 120 benchmarks
MetricPublisherClaude Opus 4.8 (high)Kimi K3.1 Pro Together (max)
Coding
LiveBench 1.0 CodingLiveBench81.8%†–
SciCode (AA)Artificial Analysis54.4%†–
LiveCodeBenchVals AI87.8%†–
SWE-bench (Vals)Vals AI88.6%†–
Code MigrationVals AI47.3%†–
ProgramBenchVals AI1.0%†–
Vibe Code Bench v1.1Vals AI82.7%†–
SWE Atlas: Codebase QnAScale AI SEAL57.3%†–
SWE Atlas: RefactoringScale AI SEAL46.7%†–
SWE Atlas: Test WritingScale AI SEAL49.6%†–
Surface Evolver BenchEpoch AI Benchmarking Hub87.5%–
DeepSWE v1.1Epoch AI Benchmarking Hub51.8%–
ALE-BenchEpoch AI Benchmarking Hub1563.8–
Terminal-Bench 4.0 (AA)Artificial Analysis21.7%†–
Terminal-Bench 2.1 (AA)Artificial Analysis84.6%†–
GameBench 2 OverallDuelLab GameBench 249.6†–
GameBench 2 by reasoning settingDuelLab GameBench 245.4†–
Design Arena WebsiteDesign Arena1256†–
Agents & tools
Terminal-BenchTerminal-Bench23.6%†–
OSWorld-Verified 2.0OSWorld20.6%†–
Remote Labor IndexScale AI / CAIS8.3%†–
APEX-AgentsMercor48.9%†–
LiveBench Agentic CodingLiveBench50.5%†–
Terminal-Bench HardArtificial Analysis58.3%†–
τ²-Bench Telecom (AA)Artificial Analysis94.4%†–
Terminal-Bench 2.1 (Vals)Vals AI71.9%†–
MCP AtlasScale AI SEAL82.2%†–
HiL-BenchScale AI SEAL35.3%†–
τ³-bench Bankingτ²-bench39.7%†–
τ³-Banking (AA)Artificial Analysis34.2%†–
Analyst Agent (AA)Artificial Analysis45.0%†–
Finance Agent v2Vals AI53.9%†–
Harvey's Legal Agent Benchmark v1Vals AI9.6%†–
Legal Research BenchVals AI43.8%†–
SkillsBenchVals AI59.2%†–
Tax Agent BenchVals AI30.0%†–
Terminal-Bench 2.0 (Vals)Vals AI70.0%†–
Terminal-Bench 4.0 (Vals)Vals AI23.2%†–
Terminal-Bench ScienceVals AI4.3%†–
Time Horizon Index: KSPVals AI11.8%†–
Arena SearchArena (LMArena)1194†–
DeepResearch BenchDeepResearch Bench50.2%–
PostTrainBenchEpoch AI Benchmarking Hub33.8%–
GBAEvalEpoch AI Benchmarking Hub70.9%†–
Vending-Bench 2Andon Labs5787.4†–
AutomationBenchArtificial Analysis45.6%†–
GDP.pdf (AA)Artificial Analysis22.8%†–
EnterpriseOps-GymArtificial Analysis44.0%†–
AA-Briefcase v1.1Artificial Analysis1320†–
Design Arena AgenticDesign Arena1221†–
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub80.0%†78.3%
FrontierMath Tier 4Epoch AI Benchmarking Hub56.1%†65.8%
OTIS Mock AIMEEpoch AI Benchmarking Hub98.3%†100.0%
ProofBenchVals AI69.0%†–
LiveBench MathematicsLiveBench94.3%†–
AIME 2026MathArena100.0%†–
HMMT February 2026MathArena95.5%†–
MathArena ApexMathArena81.3%†–
Arena MathsArena (LMArena)1492–
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub53.0%†62.2%
LiveBench 1.0 Data AnalysisLiveBench66.0%†–
MMLU-ProVals AI89.6%†–
LegalBenchVals AI83.6%†–
CorpFinVals AI66.7%†–
TaxEvalVals AI75.6%†–
Excel Modeling BenchmarkVals AI69.4%†–
MedCodeVals AI53.2%†–
MedScribeVals AI85.8%†–
Public Benefits Bench v1.1Vals AI68.1%†–
DrugDiscoveryBenchScale AI SEAL46.8%†–
EBR-benchEpoch AI Benchmarking Hub28.6%†–
GDP.pdfEpoch AI Benchmarking Hub24.0%†–
Instruction following
LiveBench LanguageLiveBench79.7%†–
LiveBench Instruction FollowingLiveBench72.0%†–
IFBenchArtificial Analysis62.2%†–
Arena Instruction FollowingArena (LMArena)1492–
Human preference
Arena TextArena (LMArena)1482–
EQ-Bench 4EQ-Bench1268†–
Arena Creative WritingArena (LMArena)1470–
Arena Multi-turnArena (LMArena)1497–
Arena Longer QueriesArena (LMArena)1503–
Multimodal
Arena VisionArena (LMArena)1295–
MMMU-Pro (Vals)Vals AI86.6%†–
MortgageTaxVals AI69.9%†–
Blueprint-Bench 2Epoch AI Benchmarking Hub14.5%†–
Long context
AA-LCR v1.1Artificial Analysis77.7%†–
Arena DocumentArena (LMArena)1485–
MLCRArtificial Analysis19.4%†–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub159.9†–
LiveBench 1.0LiveBench76.2%†–
AA Intelligence Index v4.3.2Artificial Analysis41.8†–
AA Progress Index v1Artificial Analysis318.4†–
Vals RSI IndexVals AI24.0%†–
Vals Multimodal IndexVals AI70.9%†–
Safety & honesty
FORTRESSScale AI SEAL18.2%†–

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

DuelLab GameBench 2, release gb2-20261103-eaea31df94e6 (https://benchmarks.duellab.org/releases/gb2-20261103-eaea31df94e6/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.

What changed

  • Kimi K3.1 Pro Together (max): first results.
  • Claude Opus 4.8 (high): BenchLeader Index 61.1 → 60.6.
  • Claude Opus 4.8 (high): 1 score revision.
  • Claude Opus 4.8 (high): 1 score revision.

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

Claude Opus 4.8 vs Kimi K3.1 Pro Together: questions

Is Claude Opus 4.8 better than Kimi K3.1 Pro Together?
Claude Opus 4.8 (high) and Kimi K3.1 Pro Together (max) are neck and neck on quality (60.5 vs 60.5, within the ±5.9 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.