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BenchLM

Model comparison

Claude Opus 4.6 (Adaptive) vs Claude Opus 4.8

Data verified

Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use BenchLM's provisional ranking lane.

24/100
Margin
61.0pts
winning →
85/100
0 category wins0 category wins

Verified leaderboard positions: Claude Opus 4.6 (Adaptive) unranked; Claude Opus 4.8 #3

Evidence parity. Claude Opus 4.6 (Adaptive) and Claude Opus 4.8 share 13 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to Claude Opus 4.6 (Adaptive); 40 to Claude Opus 4.8.

Updated July 12, 2026
Shared results
13
Claude Opus 4.6 (Adaptive) only
4
Claude Opus 4.8 only
40
Comparable categories
0 / 8

Benchmark data for Claude Opus 4.6 (Adaptive) and Claude Opus 4.8 is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Claude Opus 4.6 (Adaptive) and Claude Opus 4.8
CategoryClaude Opus 4.6 (Adaptive)ΔClaude Opus 4.8
AgenticClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapClaude Opus 4.880.3
CodingClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapClaude Opus 4.876.4
KnowledgeClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapClaude Opus 4.862.7
MathClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapClaude Opus 4.853.9
MultimodalClaude Opus 4.6 (Adaptive)Not measuredMarginNo overlapClaude Opus 4.877.0

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.6 (Adaptive)Claude Opus 4.8Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.6 (Adaptive)Not availableClaude Opus 4.8$5 input / $25 outputA complete price comparison is not available.
Generation speedtokens per secondClaude Opus 4.6 (Adaptive)Not availableClaude Opus 4.8Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.6 (Adaptive)Not availableClaude Opus 4.8Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.6 (Adaptive)1MClaude Opus 4.81MListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
APEX-Agents-AASource 33.0%—Not comparable
Tau2-TelecomSource 92.1%94.4%Claude Opus 4.8 leads
Terminal-Bench 2.0Source —74.6%Not comparable
BrowseCompSource —84.3%Not comparable
DeepSearchQASource —93.1%Not comparable
OSWorld-VerifiedSource —83.4%Not comparable
Finance Agent v2Source —53.9%Not comparable
GDPval-AASource —1600Not comparable
MCP AtlasSource —82.2%Not comparable
ToolathlonSource —59.9%Not comparable
Gert LabsSource —72.97%Not comparable
AA Agentic IndexSource —47.2%Not comparable
GDPval-AASource —55.0%Not comparable
ResearchClawBenchSource —21.1%Not comparable
OSWorld 2.0Source —20.6%Not comparable
AA BriefcaseSource —1354Not comparable
AA AutomationBenchSource —48.5%Not comparable
AA EnterpriseOps-GymSource —44.0%Not comparable
AA Harvey LABSource —7.5%Not comparable
AA Tau3 BankingSource —27.6%Not comparable
Coding
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
Vibe Code BenchSource 53.50%—Not comparable
Terminal-Bench HardSource 46.2%58.3%Claude Opus 4.8 leads
AA-SciCodeSource 51.9%53.5%Claude Opus 4.8 leads
SWE-bench VerifiedSource —88.6%Not comparable
SWE-bench ProSource —69.2%Not comparable
SWE MultilingualSource —84.4%Not comparable
SWE MultimodalSource —38.4%Not comparable
Terminal-Bench 2.0Source —74.6%Not comparable
cursorBench31Source —58.4%Not comparable
cursorBench32Source —62.3%Not comparable
AA Coding IndexSource —74.3%Not comparable
FrontierCodeSource —46.5%Not comparable
AA Terminal-Bench 2.1Source —84.6%Not comparable
Reasoning
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
AA-LCRSource 70.7%67.7%Claude Opus 4.6 (Adaptive) leads
CritPtSource 12.6%20.9%Claude Opus 4.8 leads
Knowledge
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
Artificial Analysis Intelligence IndexSource 43.7%55.7%Claude Opus 4.8 leads
AA-GPQA DiamondSource 89.6%92.0%Claude Opus 4.8 leads
AA-HLESource 36.7%45.7%Claude Opus 4.8 leads
AA-Omniscience IndexSource 13.5%27.4%Claude Opus 4.8 leads
AA-Omniscience AccuracySource 46.4%46.6%Claude Opus 4.8 leads
AA-Omniscience Hallucination RateSource 61.3%35.9%Claude Opus 4.8 leads
GPQASource —93.6%Not comparable
GPQA-DSource —93.6%Not comparable
HLESource —57.9%Not comparable
HLE w/o toolsSource —49.8%Not comparable
Math
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
USAMO 2026Source —96.7%Not comparable
FrontierMath v2 (Tiers 1-3)Source —47.241%Not comparable
FrontierMath v2 (Tier 4)Source —31.250%Not comparable
Multilingual
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
AA Global-MMLU-LiteSource 92.2%—Not comparable
INCLUDESource —87.6%Not comparable
Multimodal
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
AA-MMMU-ProSource 75.4%—Not comparable
Design Arena WebsiteSource 13371281Claude Opus 4.6 (Adaptive) leads
OfficeQA ProSource —66.2%Not comparable
ScreenSpot ProSource —87.9%Not comparable
CharXivSource —89.9%Not comparable
CharXiv w/o toolsSource —80.5%Not comparable
Inst. Following
BenchmarkClaude Opus 4.6 (Adaptive)Claude Opus 4.8Result
AA-IFBenchSource 53.1%62.2%Claude Opus 4.8 leads
Frequently Asked Questions (3)

Can I compare Claude Opus 4.6 (Adaptive) and Claude Opus 4.8 on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for Claude Opus 4.6 (Adaptive) and Claude Opus 4.8 today?

Claude Opus 4.8: $5.00 input / $25.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 12, 2026

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