Noometry

Model comparison

Claude Opus 4.6 vs Llama-3.3-70B-Instruct

Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.

Last verified . 26 shared benchmarks.

Claude Opus 4.6 Anthropic

58.2

Rank #20 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Summary

  • They share 26 benchmarks with published results for both. Claude Opus 4.6 scores higher in 9 categories and Llama-3.3-70B-Instruct 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 15.3.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for Claude Opus 4.6 and 5.1% for Llama-3.3-70B-Instruct.
  • Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.6Llama-3.3-70B-Instruct
ProviderAnthropicMeta
Noometry Index58.230.6
Released2026-02-042024-12-06
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.10
Output $ / M tokens$25$0.32
Results tracked6843

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Category by category

Coding Claude Opus 4.6 leads

Claude Opus 4.6: 57.2 (#20), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
WeirdML78%14.4%
LMArena Coding15361268
SWE-bench Verified78.7%—
FrontierCode26.6%—
SWE-bench Verified (bash only)75.6%—
LMArena WebDev1547—
SWE-bench Multilingual72%—
SciCode—26%
GSO41.2%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
BigCodeBench Complete—57.5%
ALE-Bench996.5—
AlgoTune1.47—

Agentic & Tool Use Claude Opus 4.6 leads

Claude Opus 4.6: 51.1 (#4), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
Terminal-Bench79.8%—
APEX-Agents46.3%—
Berkeley Function Calling Leaderboard—31.9%
Remote Labor Index4.2%—
τ²-bench Banking27.3%—
Cybench93%—
DeepResearch Bench55.3%—
BALROG—23%
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-3.3-70B-Instruct: 14.1 (#327)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
SimpleBench67.6%19.9%
LMArena Hard Prompts15271257
DTBench91.2%59.5%
LMCA55.8%17.5%
Epoch Capabilities Index155.24127.33
ForecastBench6058.6
ARC-AGI-269.2%—
Kagi LLM Benchmark83.6%—
NYT Connections (extended)92.1%—
ARC-AGI-194%—
CritPt—0%
Chess Puzzles17%—
EnigmaEval7.6%—
Thematic Generalization80.6%—
EBR-Bench12.7%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles25%—
LiveBench Data Analysis—49.5%
LiveBench—50.2%

Math Claude Opus 4.6 leads

Claude Opus 4.6: 63.0 (#31), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202594.4%5.1%
LMArena Math15191267
FrontierMath (Tiers 1-3)66%—
FrontierMath Tier 426.8%—
MathArena Final-Answer Competitions78.5%—
ProofBench50%—
LiveBench Math—42.2%
MATH Level 5—41.6%
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-3.3-70B-Instruct: 30.6 (#226)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
GPQA Diamond90.5%47.4%
Vectara Hallucination Rate12.2%4.1%
LMArena Expert15461225
Humanity's Last Exam34.4%—
SimpleQA Verified47%—
Confabulations—22.8%
MMLU—86.3%

Multimodal Not comparable

Claude Opus 4.6: 37.3 (#74), Llama-3.3-70B-Instruct: —

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
LMArena Vision1316—
Furniture Assembly28.3%—
LMArena Document1507—

Multilingual Claude Opus 4.6 leads

Claude Opus 4.6: 57.9 (#6), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
LMArena Non-English14891236
LMArena Chinese15511217
LMArena French15131281
LMArena German15021251
LMArena Japanese14841150
LMArena Korean14641143
LMArena Russian14971252
LMArena Spanish15101270

Instruction Following Claude Opus 4.6 leads

Claude Opus 4.6: 79.5 (#4), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
LMArena Instruction Following15231242
LiveBench Instruction Following—82.7%

Long Context Claude Opus 4.6 leads

Claude Opus 4.6: 48.1 (#13), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
LMArena Longer Query15201256
Fiction.LiveBench—33.3%
CL-bench20.7%—
CL-bench Life17%—

Writing & Preference Claude Opus 4.6 leads

Claude Opus 4.6: 73.5 (#10), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkClaude Opus 4.6Llama-3.3-70B-Instruct
LMArena Text15031274
LMArena Creative Writing15051250
LMArena Multi-Turn15131280
EQ-Bench Creative Writing1809—
EQ-Bench 41223—
LiveBench Language—39.2%

Frequently asked questions

Is Claude Opus 4.6 better than Llama-3.3-70B-Instruct?

Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× 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-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Claude Opus 4.6 lists at $5 and $25.

Is Claude Opus 4.6 or Llama-3.3-70B-Instruct better for coding?

Claude Opus 4.6 scores higher on coding benchmarks: 57.2 versus 31.0 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-3.3-70B-Instruct share?

26 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and Llama-3.3-70B-Instruct has 43.

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