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Model comparison

Claude Opus 4.6 vs Llama 3.1 405B

Data verified

Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

68.59/100
Margin
16.9pts
← winning
51.71/100
0 category wins0 category wins

Public leaderboard positions: Claude Opus 4.6 #16 (Supported); Llama 3.1 405B #102 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.6 and Llama 3.1 405B share 11 comparable benchmark results. 0 of 8 categories are comparable. 35 results are unique to Claude Opus 4.6; 0 to Llama 3.1 405B.

Updated July 20, 2026
Shared results
11
Claude Opus 4.6 only
35
Llama 3.1 405B only
0
Comparable categories
0 / 8

Benchmark data for Claude Opus 4.6 and Llama 3.1 405B is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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.

Claude Opus 4.6 is priced at $5.00 input / $25.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Llama 3.1 405B. Claude Opus 4.6 has the larger context window at 1M, compared with 128K for Llama 3.1 405B.

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 and Llama 3.1 405B
CategoryClaude Opus 4.6ΔLlama 3.1 405B
AgenticClaude Opus 4.673.0MarginNo overlapLlama 3.1 405BNot measured
CodingClaude Opus 4.668.1MarginNo overlapLlama 3.1 405BNot measured
KnowledgeClaude Opus 4.669.1MarginNo overlapLlama 3.1 405BNot measured
MathClaude Opus 4.636.3MarginNo overlapLlama 3.1 405BNot measured
MultimodalClaude Opus 4.677.3MarginNo overlapLlama 3.1 405BNot measured

Operational comparison

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

MetricClaude Opus 4.6Llama 3.1 405BComparison
Input / output priceUSD per 1M tokensClaude Opus 4.6$5 input / $25 outputLlama 3.1 405B$0 input / $0 outputLlama 3.1 405B has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.640 tok/sLlama 3.1 405B29 tok/sClaude Opus 4.6 has the higher measured throughput.
First-answer latencyseconds to first tokenClaude Opus 4.61.78 sLlama 3.1 405B2.19 sClaude Opus 4.6 reaches the first token sooner.
Context windowmaximum listed tokensClaude Opus 4.61MLlama 3.1 405B128KClaude Opus 4.6 lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
Terminal-Bench 2.0Source 65.4%—Not comparable
BrowseCompSource 83.7%—Not comparable
OSWorld-VerifiedSource 72.7%—Not comparable
τ²-bench resultsSource 84.8%19%Claude Opus 4.6 leads
Claw-EvalSource 70.4%—Not comparable
DeepSearchQASource 73.7%—Not comparable
CyberGymSource 66.6%—Not comparable
Gert LabsSource 61.85%—Not comparable
ResearchClawBenchSource 19.9%—Not comparable
JobBenchSource 36.7%—Not comparable
Coding
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
SWE-bench VerifiedSource 80.8%—Not comparable
SWE-bench Verified*Source 75.6%—Not comparable
LiveCodeBench ProSource 70.7%—Not comparable
SWE-bench ProSource 53.4%—Not comparable
SWE-RebenchSource 65.3%—Not comparable
React Native EvalsSource 84.1%—Not comparable
Vibe Code BenchSource 57.57%—Not comparable
AA-SciCodeSource 45.7%29.9%Claude Opus 4.6 leads
FrontierCode 1.1 MainSource 26.9%—Not comparable
Reasoning
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
AA-LCRSource 58.3%24.3%Claude Opus 4.6 leads
CritPtSource 2.8%0.0%Claude Opus 4.6 leads
Knowledge
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
GPQASource 91.3%—Not comparable
GPQA-DSource 89.2%—Not comparable
SuperGPQASource 95%—Not comparable
MMLU-ProSource 82%—Not comparable
MMLU-Pro (Arcee)Source 89.1%—Not comparable
HLESource 53%—Not comparable
HLE w/o toolsSource 40%—Not comparable
HealthBench HardSource 14.8%—Not comparable
MedXpertQA (Text)Source 52.1%—Not comparable
Artificial Analysis Intelligence IndexSource 37.8%8.5%Claude Opus 4.6 leads
AA-GPQA DiamondSource 84.0%51.5%Claude Opus 4.6 leads
AA-HLESource 18.6%4.2%Claude Opus 4.6 leads
AA-Omniscience IndexSource 3.5%-17.3%Claude Opus 4.6 leads
AA-Omniscience AccuracySource 45.2%22.3%Claude Opus 4.6 leads
AA-Omniscience Hallucination RateSource 76.0%51.0%Llama 3.1 405B leads
Math
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
AIME25 (Arcee)Source 99.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 40.700%—Not comparable
FrontierMath v2 (Tier 4)Source 22.900%—Not comparable
Multimodal
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
MMMU-ProSource 77.3%—Not comparable
ERQASource 51.6%—Not comparable
ScreenSpot ProSource 83.1%—Not comparable
MedXpertQA (MM)Source 64.8%—Not comparable
AA-MMMU-ProSource 72.5%—Not comparable
Design Arena WebsiteSource 1328—Not comparable
Inst. Following
BenchmarkClaude Opus 4.6Llama 3.1 405BResult
AA-IFBenchSource 44.6%39.0%Claude Opus 4.6 leads
Frequently Asked Questions (3)

Can I compare Claude Opus 4.6 and Llama 3.1 405B 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 and Llama 3.1 405B today?

Claude Opus 4.6: $5.00 input / $25.00 output per 1M tokens Llama 3.1 405B: $0.00 input / $0.00 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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