Noometry

Model comparison

Claude Opus 4.8 vs Llama-3.3-70B-Instruct

Claude Opus 4.8 is the stronger model overall, scoring 60.7 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 4.8's lead doesn't matter for your workload.

Last verified . 27 shared benchmarks.

Claude Opus 4.8 Anthropic

60.7

Rank #13 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Summary

  • They share 27 benchmarks with published results for both. Claude Opus 4.8 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 4.8 leads 78.4 to 15.3.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for Claude Opus 4.8 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 4.8.
  • Claude Opus 4.8 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 4.8Llama-3.3-70B-Instruct
ProviderAnthropicMeta
Noometry Index60.730.6
Released2026-05-282024-12-06
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.10
Output $ / M tokens$25$0.32
Results tracked6543

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Category by category

Coding Claude Opus 4.8 leads

Claude Opus 4.8: 59.9 (#12), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
SciCode53.5%26%
WeirdML82.9%14.4%
LMArena Coding14901268
DeepSWE59%—
FrontierCode46.5%—
LMArena WebDev1556—
GSO47.1%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
BigCodeBench Complete—57.5%
ALE-Bench1,564—

Agentic & Tool Use Claude Opus 4.8 leads

Claude Opus 4.8: 47.6 (#11), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
APEX-Agents48.9%—
Berkeley Function Calling Leaderboard—31.9%
OSWorld 2.020.6%—
Remote Labor Index8.3%—
τ²-bench Banking39.7%—
DeepResearch Bench50.2%—
PostTrainBench33.8%—
BALROG—23%
GBAEval70.9%—
GDP.pdf24%—
LMArena Search1204—
Vending-Bench 25,787—

Reasoning Claude Opus 4.8 leads

Claude Opus 4.8: 64.7 (#16), Llama-3.3-70B-Instruct: 14.1 (#327)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
SimpleBench64.8%19.9%
CritPt20.9%0%
LMArena Hard Prompts14821257
DTBench94.9%59.5%
LMCA57.5%17.5%
Epoch Capabilities Index158.21127.33
ForecastBench59.958.6
ARC-AGI-272.1%—
Kagi LLM Benchmark88.8%—
NYT Connections (extended)91.1%—
ARC-AGI-192.5%—
Chess Puzzles34%—
EnigmaEval23.5%—
EBR-Bench28.6%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles36%—
LiveBench Data Analysis—49.5%
Surface Evolver Bench87.5%—
Bench to the Future 30.14—
LiveBench—50.2%

Math Claude Opus 4.8 leads

Claude Opus 4.8: 78.4 (#13), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202598.3%5.1%
LMArena Math14871267
FrontierMath (Tiers 1-3)80%—
FrontierMath Tier 456.1%—
MathArena Final-Answer Competitions91.8%—
ProofBench69%—
LiveBench Math—42.2%
MATH Level 5—41.6%
FrontierMath (Feb 2025 set)47.2%—
FrontierMath Tier 4 (v1)31.3%—

Knowledge Claude Opus 4.8 leads

Claude Opus 4.8: 61.3 (#29), Llama-3.3-70B-Instruct: 30.6 (#226)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
GPQA Diamond91%47.4%
LMArena Expert15021225
SimpleQA Verified53%—
Confabulations—22.8%
Vectara Hallucination Rate—4.1%
MMLU—86.3%

Multimodal Not comparable

Claude Opus 4.8: 42.9 (#26), Llama-3.3-70B-Instruct: —

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
LMArena Vision1294—
Blueprint-Bench 214.5%—
Furniture Assembly42.5%—
LMArena Document1475—

Multilingual Claude Opus 4.8 leads

Claude Opus 4.8: 55.2 (#33), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
LMArena Non-English14501236
LMArena Chinese15071217
LMArena French14811281
LMArena German14721251
LMArena Japanese14401150
LMArena Korean14321143
LMArena Russian14741252
LMArena Spanish14661270

Instruction Following Claude Opus 4.8 leads

Claude Opus 4.8: 77.4 (#24), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
LMArena Instruction Following14761242
LiveBench Instruction Following—82.7%

Long Context Claude Opus 4.8 leads

Claude Opus 4.8: 45.4 (#35), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
LMArena Longer Query14831256
Fiction.LiveBench—33.3%

Writing & Preference Claude Opus 4.8 leads

Claude Opus 4.8: 72.0 (#16), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkClaude Opus 4.8Llama-3.3-70B-Instruct
LMArena Text14611274
LMArena Creative Writing14541250
LMArena Multi-Turn14761280
EQ-Bench Creative Writing1840—
EQ-Bench 41281—
LiveBench Language—39.2%

Frequently asked questions

Is Claude Opus 4.8 better than Llama-3.3-70B-Instruct?

Claude Opus 4.8 is the stronger model overall, scoring 60.7 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 4.8's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.8 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 4.8 lists at $5 and $25.

Is Claude Opus 4.8 or Llama-3.3-70B-Instruct better for coding?

Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 31.0 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.8 does, with 1M tokens against 128K.

How many benchmarks do Claude Opus 4.8 and Llama-3.3-70B-Instruct share?

27 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Llama-3.3-70B-Instruct has 43.

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