Noometry

Model comparison

Claude Opus 4.8 vs Llama 3.1-8B

Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 174× 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 #9 Confirmed

Llama 3.1-8B Meta

22.8

Rank #347 Confirmed

Summary

  • They share 27 benchmarks with published results for both. Claude Opus 4.8 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in math, where Claude Opus 4.8 leads 78.2 to 9.5.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for Claude Opus 4.8 and 1.7% for Llama 3.1-8B.
  • Llama 3.1-8B is cheaper at $0.05 / $0.08 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 3.1-8B has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.8Llama 3.1-8B
ProviderAnthropicMeta
Noometry Index60.722.8
Released2026-05-282024-07-23
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.05
Output $ / M tokens$25$0.08
Results tracked6543

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Category by category

Coding Claude Opus 4.8 leads

Claude Opus 4.8: 60.2 (#9), Llama 3.1-8B: 19.9 (#335)

BenchmarkClaude Opus 4.8Llama 3.1-8B
SciCode53.5%13.2%
WeirdML82.9%1.7%
LMArena Coding14911195
DeepSWE59%—
FrontierCode46.5%—
LMArena WebDev1556—
GSO47.1%—
BigCodeBench Instruct—32.8%
BigCodeBench Complete—40.5%
ALE-Bench1,564—
HumanEval+—62.8%
MBPP+—55.6%

Agentic & Tool Use Claude Opus 4.8 leads

Claude Opus 4.8: 47.6 (#10), Llama 3.1-8B: 22.3 (#123)

BenchmarkClaude Opus 4.8Llama 3.1-8B
APEX-Agents48.9%—
Berkeley Function Calling Leaderboard—25.8%
OSWorld 2.020.6%—
Remote Labor Index8.3%—
τ²-bench Banking39.7%—
DeepResearch Bench50.2%—
PostTrainBench33.8%—
BALROG—15.1%
GBAEval70.9%—
GDP.pdf24%—
LMArena Search1204—
Vending-Bench 25,787—

Reasoning Claude Opus 4.8 leads

Claude Opus 4.8: 64.6 (#14), Llama 3.1-8B: 14.5 (#316)

BenchmarkClaude Opus 4.8Llama 3.1-8B
CritPt20.9%0%
Chess Puzzles34%0%
LMArena Hard Prompts14811175
DTBench94.9%50.9%
LMCA57.5%5.4%
Epoch Capabilities Index158.3116.48
ARC-AGI-272.1%—
SimpleBench64.8%—
Kagi LLM Benchmark88.8%—
NYT Connections (extended)91.1%—
ARC-AGI-192.5%—
EnigmaEval23.5%—
EBR-Bench28.6%—
Mystery Game Puzzles36%—
Surface Evolver Bench87.5%—
Bench to the Future 30.14—
ForecastBench59.9—
PIQA—81.2%

Math Claude Opus 4.8 leads

Claude Opus 4.8: 78.2 (#11), Llama 3.1-8B: 9.5 (#311)

BenchmarkClaude Opus 4.8Llama 3.1-8B
OTIS Mock AIME 2024-202598.3%1.7%
LMArena Math14871179
FrontierMath (Tiers 1-3)80%—
FrontierMath Tier 456.1%—
MathArena Final-Answer Competitions91.8%—
ProofBench69%—
Omni-MATH—13.7%
MATH Level 5—22.9%
FrontierMath (Feb 2025 set)47.2%—
FrontierMath Tier 4 (v1)31.3%—
GSM8K—82.4%

Knowledge Claude Opus 4.8 leads

Claude Opus 4.8: 61.3 (#26), Llama 3.1-8B: 8.0 (#302)

BenchmarkClaude Opus 4.8Llama 3.1-8B
GPQA Diamond91%27%
LMArena Expert15011144
SimpleQA Verified53%—
MMLU-Pro—40.6%
GPQA (HELM)—24.7%
BoolQ—82.8%
MMLU—56.1%

Multimodal Not comparable

Claude Opus 4.8: 43.2 (#20), Llama 3.1-8B: —

BenchmarkClaude Opus 4.8Llama 3.1-8B
LMArena Vision1295—
Blueprint-Bench 214.5%—
Furniture Assembly42.5%—
LMArena Document1475—

Multilingual Claude Opus 4.8 leads

Claude Opus 4.8: 55.4 (#31), Llama 3.1-8B: 34.2 (#244)

BenchmarkClaude Opus 4.8Llama 3.1-8B
LMArena Non-English14501148
LMArena Chinese15091151
LMArena French14821176
LMArena German14701144
LMArena Japanese14401061
LMArena Korean14321053
LMArena Russian14761158
LMArena Spanish14701169

Instruction Following Claude Opus 4.8 leads

Claude Opus 4.8: 77.6 (#22), Llama 3.1-8B: 59.1 (#253)

BenchmarkClaude Opus 4.8Llama 3.1-8B
LMArena Instruction Following14761159
IFEval—74.3%

Long Context Claude Opus 4.8 leads

Claude Opus 4.8: 45.4 (#37), Llama 3.1-8B: 35.8 (#233)

BenchmarkClaude Opus 4.8Llama 3.1-8B
LMArena Longer Query14821182

Writing & Preference Claude Opus 4.8 leads

Claude Opus 4.8: 72.1 (#17), Llama 3.1-8B: 29.9 (#285)

BenchmarkClaude Opus 4.8Llama 3.1-8B
LMArena Text14601187
LMArena Creative Writing14531154
EQ-Bench Creative Writing1840713
LMArena Multi-Turn14761172
WildBench—68.7%
EQ-Bench 41281—

Frequently asked questions

Is Claude Opus 4.8 better than Llama 3.1-8B?

Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 22.8 on the Noometry Index. Llama 3.1-8B costs 174× 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 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Claude Opus 4.8 lists at $5 and $25.

Is Claude Opus 4.8 or Llama 3.1-8B better for coding?

Claude Opus 4.8 scores higher on coding benchmarks: 60.2 versus 19.9 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 3.1-8B share?

27 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Llama 3.1-8B has 43.

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