Noometry

Model comparison

Claude Opus 4.7 vs Llama 3.1-8B

Claude Opus 4.7 is the stronger model overall, scoring 58.4 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 4.7's lead doesn't matter for your workload.

Last verified . 27 shared benchmarks.

Claude Opus 4.7 Anthropic

58.4

Rank #17 Confirmed

Llama 3.1-8B Meta

22.8

Rank #347 Confirmed

Summary

  • They share 27 benchmarks with published results for both. Claude Opus 4.7 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 4.7 leads 66.5 to 9.5.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Claude Opus 4.7 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 4.7.
  • Claude Opus 4.7 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 4.7Llama 3.1-8B
ProviderAnthropicMeta
Noometry Index58.422.8
Released2026-04-142024-07-23
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.05
Output $ / M tokens$25$0.08
Results tracked6643

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Category by category

Coding Claude Opus 4.7 leads

Claude Opus 4.7: 59.9 (#10), Llama 3.1-8B: 19.9 (#335)

BenchmarkClaude Opus 4.7Llama 3.1-8B
SciCode54.5%13.2%
WeirdML76.4%1.7%
LMArena Coding15191195
SWE-bench Verified83.5%—
FrontierCode38.5%—
LMArena WebDev1557—
GSO44.1%—
BigCodeBench Instruct—32.8%
MirrorCode31.1%—
BigCodeBench Complete—40.5%
ALE-Bench1,323—
HumanEval+—62.8%
MBPP+—55.6%

Agentic & Tool Use Claude Opus 4.7 leads

Claude Opus 4.7: 47.9 (#8), Llama 3.1-8B: 22.3 (#123)

BenchmarkClaude Opus 4.7Llama 3.1-8B
Terminal-Bench80.2%—
APEX-Agents49.2%—
Berkeley Function Calling Leaderboard—25.8%
OSWorld 2.018.2%—
τ²-bench Banking40.2%—
PostTrainBench28.6%—
BALROG—15.1%
ExploitBench26.5%—
GBAEval43.8%—
GDP.pdf21%—
LMArena Search1233—
Vending-Bench 210,937—

Reasoning Claude Opus 4.7 leads

Claude Opus 4.7: 53.7 (#24), Llama 3.1-8B: 14.5 (#316)

BenchmarkClaude Opus 4.7Llama 3.1-8B
CritPt12%0%
Chess Puzzles30%0%
LMArena Hard Prompts15071175
DTBench94.7%50.9%
LMCA52.2%5.4%
Epoch Capabilities Index156.39116.48
ARC-AGI-275.8%—
SimpleBench61.7%—
Kagi LLM Benchmark80.7%—
NYT Connections (extended)39%—
ARC-AGI-193.5%—
Thematic Generalization72.8%—
EBR-Bench19%—
Mystery Game Puzzles28%—
ForecastBench60.3—
PIQA—81.2%

Math Claude Opus 4.7 leads

Claude Opus 4.7: 66.5 (#20), Llama 3.1-8B: 9.5 (#311)

BenchmarkClaude Opus 4.7Llama 3.1-8B
OTIS Mock AIME 2024-202597.8%1.7%
LMArena Math14991179
FrontierMath (Tiers 1-3)70.2%—
FrontierMath Tier 431.7%—
MathArena Final-Answer Competitions73.6%—
ProofBench54%—
Omni-MATH—13.7%
MATH Level 5—22.9%
FrontierMath (Feb 2025 set)43.8%—
FrontierMath Tier 4 (v1)22.9%—
GSM8K—82.4%

Knowledge Claude Opus 4.7 leads

Claude Opus 4.7: 62.6 (#20), Llama 3.1-8B: 8.0 (#302)

BenchmarkClaude Opus 4.7Llama 3.1-8B
GPQA Diamond90.2%27%
LMArena Expert15211144
Humanity's Last Exam36.2%—
SimpleQA Verified51.7%—
MMLU-Pro—40.6%
Vectara Hallucination Rate12%—
GPQA (HELM)—24.7%
BoolQ—82.8%
MMLU—56.1%

Multimodal Not comparable

Claude Opus 4.7: 41.5 (#29), Llama 3.1-8B: —

BenchmarkClaude Opus 4.7Llama 3.1-8B
LMArena Vision1317—
Blueprint-Bench 224.5%—
Furniture Assembly33.3%—
LMArena Document1495—

Multilingual Claude Opus 4.7 leads

Claude Opus 4.7: 57.5 (#9), Llama 3.1-8B: 34.2 (#244)

BenchmarkClaude Opus 4.7Llama 3.1-8B
LMArena Non-English14811148
LMArena Chinese15301151
LMArena French15051176
LMArena German14941144
LMArena Japanese14721061
LMArena Korean14661053
LMArena Russian14941158
LMArena Spanish14971169

Instruction Following Claude Opus 4.7 leads

Claude Opus 4.7: 78.6 (#9), Llama 3.1-8B: 59.1 (#253)

BenchmarkClaude Opus 4.7Llama 3.1-8B
LMArena Instruction Following14981159
IFEval—74.3%

Long Context Claude Opus 4.7 leads

Claude Opus 4.7: 46.1 (#24), Llama 3.1-8B: 35.8 (#233)

BenchmarkClaude Opus 4.7Llama 3.1-8B
LMArena Longer Query15051182

Writing & Preference Claude Opus 4.7 leads

Claude Opus 4.7: 75.3 (#8), Llama 3.1-8B: 29.9 (#285)

BenchmarkClaude Opus 4.7Llama 3.1-8B
LMArena Text14911187
LMArena Creative Writing14861154
EQ-Bench Creative Writing1914713
LMArena Multi-Turn15061172
WildBench—68.7%
EQ-Bench 41311—

Frequently asked questions

Is Claude Opus 4.7 better than Llama 3.1-8B?

Claude Opus 4.7 is the stronger model overall, scoring 58.4 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 4.7's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.7 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 4.7 lists at $5 and $25.

Is Claude Opus 4.7 or Llama 3.1-8B better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.9 versus 19.9 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 128K.

How many benchmarks do Claude Opus 4.7 and Llama 3.1-8B share?

27 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and Llama 3.1-8B has 43.

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