Noometry

Model comparison

Claude Opus 4.6 vs Llama 3.1-70B

Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 29.6 on the Noometry Index. Llama 3.1-70B costs 25× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.

Last verified . 24 shared benchmarks.

Claude Opus 4.6 Anthropic

58.2

Rank #20 Confirmed

Llama 3.1-70B Meta

29.6

Rank #308 Confirmed

Summary

  • They share 24 benchmarks with published results for both. Claude Opus 4.6 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in math, where Claude Opus 4.6 leads 63.0 to 13.5.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for Claude Opus 4.6 and 3.6% for Llama 3.1-70B.
  • Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.6.
  • Claude Opus 4.6 accepts more context: 1M tokens versus 128K.
  • Llama 3.1-70B has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.6Llama 3.1-70B
ProviderAnthropicMeta
Noometry Index58.229.6
Released2026-02-042024-07-23
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.40
Output $ / M tokens$25$0.40
Results tracked6835

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Category by category

Coding Claude Opus 4.6 leads

Claude Opus 4.6: 57.2 (#20), Llama 3.1-70B: 30.3 (#296)

BenchmarkClaude Opus 4.6Llama 3.1-70B
WeirdML78%9%
LMArena Coding15361260
SWE-bench Verified78.7%—
FrontierCode26.6%—
SWE-bench Verified (bash only)75.6%—
LMArena WebDev1547—
SWE-bench Multilingual72%—
GSO41.2%—
BigCodeBench Instruct—46.1%
BigCodeBench Complete—54.8%
ALE-Bench996.5—
AlgoTune1.47—

Agentic & Tool Use Claude Opus 4.6 leads

Claude Opus 4.6: 51.1 (#4), Llama 3.1-70B: 25.1 (#112)

BenchmarkClaude Opus 4.6Llama 3.1-70B
Terminal-Bench79.8%—
APEX-Agents46.3%—
Remote Labor Index4.2%—
TheAgentCompany—6.9%
τ²-bench Banking27.3%—
Cybench93%—
DeepResearch Bench55.3%—
BALROG—27.9%
GBAEval44.1%—
LMArena Search1253—
METR Time Horizons78.9%—
Vending-Bench 28,018—

Reasoning Claude Opus 4.6 leads

Claude Opus 4.6: 57.8 (#23), Llama 3.1-70B: 21.6 (#220)

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Hard Prompts15271241
DTBench91.2%60%
LMCA55.8%14.8%
Epoch Capabilities Index155.24125.92
ARC-AGI-269.2%—
SimpleBench67.6%—
Kagi LLM Benchmark83.6%—
NYT Connections (extended)92.1%—
ARC-AGI-194%—
Chess Puzzles17%—
EnigmaEval7.6%—
Thematic Generalization80.6%—
EBR-Bench12.7%—
Mystery Game Puzzles25%—
ForecastBench60—

Math Claude Opus 4.6 leads

Claude Opus 4.6: 63.0 (#31), Llama 3.1-70B: 13.5 (#304)

BenchmarkClaude Opus 4.6Llama 3.1-70B
OTIS Mock AIME 2024-202594.4%3.6%
LMArena Math15191252
FrontierMath (Tiers 1-3)66%—
FrontierMath Tier 426.8%—
MathArena Final-Answer Competitions78.5%—
ProofBench50%—
Omni-MATH—21%
MATH Level 5—36.7%
FrontierMath (Feb 2025 set)40.7%—
FrontierMath Tier 4 (v1)22.9%—

Knowledge Claude Opus 4.6 leads

Claude Opus 4.6: 61.9 (#26), Llama 3.1-70B: 24.2 (#269)

BenchmarkClaude Opus 4.6Llama 3.1-70B
GPQA Diamond90.5%44.2%
LMArena Expert15461209
Humanity's Last Exam34.4%—
SimpleQA Verified47%—
MMLU-Pro—65.3%
Vectara Hallucination Rate12.2%—
GPQA (HELM)—42.6%
MMLU—80.1%

Multimodal Not comparable

Claude Opus 4.6: 37.3 (#74), Llama 3.1-70B: —

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Vision1316—
Furniture Assembly28.3%—
LMArena Document1507—

Multilingual Claude Opus 4.6 leads

Claude Opus 4.6: 57.9 (#6), Llama 3.1-70B: 38.8 (#225)

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Non-English14891219
LMArena Chinese15511215
LMArena French15131261
LMArena German15021222
LMArena Japanese14841132
LMArena Korean14641140
LMArena Russian14971234
LMArena Spanish15101253

Instruction Following Claude Opus 4.6 leads

Claude Opus 4.6: 79.5 (#4), Llama 3.1-70B: 65.3 (#223)

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Instruction Following15231231
IFEval—82.1%

Long Context Claude Opus 4.6 leads

Claude Opus 4.6: 48.1 (#13), Llama 3.1-70B: 37.6 (#214)

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Longer Query15201241
CL-bench20.7%—
CL-bench Life17%—

Writing & Preference Claude Opus 4.6 leads

Claude Opus 4.6: 73.5 (#10), Llama 3.1-70B: 35.4 (#267)

BenchmarkClaude Opus 4.6Llama 3.1-70B
LMArena Text15031261
LMArena Creative Writing15051232
EQ-Bench Creative Writing1809784
LMArena Multi-Turn15131256
WildBench—75.8%
EQ-Bench 41223—

Frequently asked questions

Is Claude Opus 4.6 better than Llama 3.1-70B?

Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 29.6 on the Noometry Index. Llama 3.1-70B costs 25× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.6 or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Claude Opus 4.6 lists at $5 and $25.

Is Claude Opus 4.6 or Llama 3.1-70B better for coding?

Claude Opus 4.6 scores higher on coding benchmarks: 57.2 versus 30.3 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.6 does, with 1M tokens against 128K.

How many benchmarks do Claude Opus 4.6 and Llama 3.1-70B share?

24 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and Llama 3.1-70B has 35.

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