Noometry

Model comparison

Llama-3.3-70B-Instruct vs Mercury

Mercury is the stronger model overall, scoring 37.6 to 30.6 on the Noometry Index.

Last verified . 8 shared benchmarks.

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Mercury Inception

37.6

Rank #199 Confirmed

Summary

  • They share 8 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 2 categories and Mercury in 4 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in long context, where Mercury leads 38.4 to 26.4.
  • Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Llama-3.3-70B-InstructMercury
ProviderMetaInception
Noometry Index30.637.6
Released2024-12-06—
WeightsOpenProprietary
Context window128K—
Max output4K—
Input $ / M tokens$0.10—
Output $ / M tokens$0.32—
Results tracked439

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Category by category

Coding Mercury leads

Llama-3.3-70B-Instruct: 31.0 (#290), Mercury: 38.7 (#170)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Coding12681322
SciCode26%—
WeirdML14.4%—
BigCodeBench Instruct46.9%—
LiveBench Coding36.6%—
BigCodeBench Complete57.5%—

Agentic & Tool Use Not comparable

Llama-3.3-70B-Instruct: 25.8 (#105), Mercury: —

BenchmarkLlama-3.3-70B-InstructMercury
Berkeley Function Calling Leaderboard31.9%—
BALROG23%—

Reasoning Mercury leads

Llama-3.3-70B-Instruct: 14.1 (#327), Mercury: 17.5 (#293)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Hard Prompts12571285
SimpleBench19.9%—
Kagi LLM Benchmark—21.6%
CritPt0%—
LiveBench Reasoning50.8%—
DTBench59.5%—
LiveBench Data Analysis49.5%—
LMCA17.5%—
Epoch Capabilities Index127.33—
ForecastBench58.6—
LiveBench50.2%—

Math Not comparable

Llama-3.3-70B-Instruct: 15.3 (#298), Mercury: —

BenchmarkLlama-3.3-70B-InstructMercury
OTIS Mock AIME 2024-20255.1%—
LiveBench Math42.2%—
LMArena Math1267—
MATH Level 541.6%—

Knowledge Not comparable

Llama-3.3-70B-Instruct: 30.6 (#226), Mercury: —

BenchmarkLlama-3.3-70B-InstructMercury
GPQA Diamond47.4%—
Confabulations22.8%—
Vectara Hallucination Rate4.1%—
LMArena Expert1225—
MMLU86.3%—

Multilingual Mercury leads

Llama-3.3-70B-Instruct: 39.9 (#220), Mercury: 41.6 (#206)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Non-English12361260
LMArena Chinese1217—
LMArena French1281—
LMArena German1251—
LMArena Japanese1150—
LMArena Korean1143—
LMArena Russian1252—
LMArena Spanish1270—

Instruction Following Llama-3.3-70B-Instruct leads

Llama-3.3-70B-Instruct: 71.1 (#157), Mercury: 65.2 (#224)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Instruction Following12421239
LiveBench Instruction Following82.7%—

Long Context Mercury leads

Llama-3.3-70B-Instruct: 26.4 (#295), Mercury: 38.4 (#198)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Longer Query12561266
Fiction.LiveBench33.3%—

Writing & Preference Llama-3.3-70B-Instruct leads

Llama-3.3-70B-Instruct: 47.6 (#207), Mercury: 46.2 (#221)

BenchmarkLlama-3.3-70B-InstructMercury
LMArena Text12741282
LMArena Creative Writing12501191
LMArena Multi-Turn12801282
LiveBench Language39.2%—

Frequently asked questions

Is Llama-3.3-70B-Instruct better than Mercury?

Mercury is the stronger model overall, scoring 37.6 to 30.6 on the Noometry Index.

Is Llama-3.3-70B-Instruct or Mercury better for coding?

Mercury scores higher on coding benchmarks: 38.7 versus 31.0 in the Noometry coding category.

How many benchmarks do Llama-3.3-70B-Instruct and Mercury share?

8 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mercury has 9.

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