Noometry

Model comparison

Claude Sonnet 4.5 vs Llama-3.3-70B-Instruct

Claude Sonnet 4.5 is the stronger model overall, scoring 44.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 39× less per token, which makes it the better buy when Claude Sonnet 4.5's lead doesn't matter for your workload.

Last verified . 30 shared benchmarks.

Claude Sonnet 4.5 Anthropic

44.1

Rank #81 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Summary

  • They share 30 benchmarks with published results for both. Claude Sonnet 4.5 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 writing & preference, where Claude Sonnet 4.5 leads 66.5 to 47.6.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 77.8% for Claude Sonnet 4.5 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 $3 / $15 for Claude Sonnet 4.5.
  • Claude Sonnet 4.5 accepts more context: 200K tokens versus 128K.
  • Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Claude Sonnet 4.5Llama-3.3-70B-Instruct
ProviderAnthropicMeta
Noometry Index44.130.6
Released2025-09-292024-12-06
WeightsProprietaryOpen
Context window200K128K
Max output64K4K
Input $ / M tokens$3$0.10
Output $ / M tokens$15$0.32
Results tracked7343

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Category by category

Coding Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 47.3 (#61), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
SciCode44.7%26%
WeirdML47.7%14.4%
LMArena Coding14891268
SWE-bench Verified71.3%—
SWE-bench Verified (bash only)71.4%—
LMArena WebDev1393—
SWE-bench Multilingual67%—
GSO14.7%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
BigCodeBench Complete—57.5%
ALE-Bench796.15—
AlgoTune1.52—

Agentic & Tool Use Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 38.3 (#32), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
Berkeley Function Calling Leaderboard73.2%31.9%
Terminal-Bench46.5%—
GDPval42.5%—
Remote Labor Index2.1%—
τ²-bench Airline72%—
τ²-bench Banking25.3%—
τ²-bench Retail72.4%—
τ²-bench Telecom84.9%—
Cybench60%—
DeepResearch Bench52.6%—
OSWorld62.9%—
BALROG—23%
LMArena Search1159—
METR Time Horizons67.4%—
Vending-Bench 23,839—

Reasoning Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 26.9 (#125), Llama-3.3-70B-Instruct: 14.1 (#327)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
SimpleBench54.3%19.9%
CritPt1.1%0%
LMArena Hard Prompts14621257
DTBench83.2%59.5%
LMCA38.8%17.5%
Epoch Capabilities Index146.84127.33
ForecastBench61.958.6
ARC-AGI-213.6%—
Kagi LLM Benchmark57.9%—
NYT Connections (extended)37.3%—
ARC-AGI-163.7%—
Chess Puzzles12%—
EnigmaEval6%—
EBR-Bench2.4%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles17%—
LiveBench Data Analysis—49.5%
LiveBench—50.2%

Math Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 32.3 (#216), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202577.8%5.1%
LMArena Math14491267
MATH Level 597.7%41.6%
FrontierMath (Tiers 1-3)23.9%—
FrontierMath Tier 42.4%—
ProofBench19%—
Omni-MATH55.3%—
LiveBench Math—42.2%
FrontierMath (Feb 2025 set)15.2%—
FrontierMath Tier 4 (v1)4.2%—

Knowledge Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 48.4 (#76), Llama-3.3-70B-Instruct: 30.6 (#226)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
GPQA Diamond82.3%47.4%
Vectara Hallucination Rate12%4.1%
LMArena Expert14821225
Humanity's Last Exam13.7%—
SimpleQA Verified30.7%—
MMLU-Pro86.9%—
Confabulations—22.8%
GPQA (HELM)68.6%—
MMLU—86.3%

Multimodal Not comparable

Claude Sonnet 4.5: 34.8 (#89), Llama-3.3-70B-Instruct: —

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
VPCT39.8%—
LMArena Document1450—

Multilingual Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 53.4 (#69), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
LMArena Non-English14251236
LMArena Chinese14591217
LMArena French14581281
LMArena German14271251
LMArena Japanese13901150
LMArena Korean14031143
LMArena Russian14371252
LMArena Spanish14571270

Instruction Following Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 75.0 (#78), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
LMArena Instruction Following14591242
LiveBench Instruction Following—82.7%
IFEval85%—

Long Context Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 45.2 (#46), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
LMArena Longer Query14761256
Fiction.LiveBench—33.3%

Writing & Preference Claude Sonnet 4.5 leads

Claude Sonnet 4.5: 66.5 (#34), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkClaude Sonnet 4.5Llama-3.3-70B-Instruct
LMArena Text14391274
LMArena Creative Writing14421250
LMArena Multi-Turn14651280
EQ-Bench Creative Writing1678—
WildBench85.4%—
LiveBench Language—39.2%

Frequently asked questions

Is Claude Sonnet 4.5 better than Llama-3.3-70B-Instruct?

Claude Sonnet 4.5 is the stronger model overall, scoring 44.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 39× less per token, which makes it the better buy when Claude Sonnet 4.5's lead doesn't matter for your workload.

Which is cheaper, Claude Sonnet 4.5 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 Sonnet 4.5 lists at $3 and $15.

Is Claude Sonnet 4.5 or Llama-3.3-70B-Instruct better for coding?

Claude Sonnet 4.5 scores higher on coding benchmarks: 47.3 versus 31.0 in the Noometry coding category.

Which has the bigger context window?

Claude Sonnet 4.5 does, with 200K tokens against 128K.

How many benchmarks do Claude Sonnet 4.5 and Llama-3.3-70B-Instruct share?

30 benchmarks have published results for both models. Claude Sonnet 4.5 has 73 scored results on Noometry and Llama-3.3-70B-Instruct has 43.

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