Noometry

Model comparison

Llama-3.3-70B-Instruct vs Mercury 2.5

Llama-3.3-70B-Instruct has enough public results to be ranked (#283); Mercury 2.5 does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

Llama-3.3-70B-Instruct Meta

30.3

Rank #283 Confirmed

Mercury 2.5 Inception

30.2

Unranked Sparse

Summary

  • The widest gap is in math, where Mercury 2.5 leads 22.4 to 14.3.
  • Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
  • Mercury 2.5 accepts more context: 260K tokens versus 128K.
  • Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Llama-3.3-70B-InstructMercury 2.5
ProviderMetaInception
Noometry Index30.330.2
Released2024-12-062026-09-08
WeightsOpenProprietary
Context window128K260K
Max output4K66K
Input $ / M tokens$0.10$0.04
Output $ / M tokens$0.32$0.15
Results tracked432

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Category by category

Coding Not comparable

Llama-3.3-70B-Instruct: 30.6 (#284), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
SciCode26%—
WeirdML14.4%—
BigCodeBench Instruct46.9%—
LiveBench Coding36.6%—
LMArena Coding1268—
BigCodeBench Complete57.5%—
ALE-Bench—301.65

Agentic & Tool Use Not comparable

Llama-3.3-70B-Instruct: 25.6 (#100), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
Berkeley Function Calling Leaderboard31.9%—
BALROG23%—

Reasoning Not comparable

Llama-3.3-70B-Instruct: 13.7 (#325), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
SimpleBench19.9%—
CritPt0%—
LiveBench Reasoning50.8%—
LMArena Hard Prompts1257—
DTBench59.5%—
LiveBench Data Analysis49.5%—
LMCA17.5%—
Epoch Capabilities Index127.32—
ForecastBench58.6—
LiveBench50.2%—

Math Mercury 2.5 leads

Llama-3.3-70B-Instruct: 14.3 (#292), Mercury 2.5: 22.4

BenchmarkLlama-3.3-70B-InstructMercury 2.5
OTIS Mock AIME 2024-20255.1%—
ProofBench—3%
LiveBench Math42.2%—
LMArena Math1268—
MATH Level 541.6%—

Knowledge Not comparable

Llama-3.3-70B-Instruct: 30.5 (#220), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
GPQA Diamond47.4%—
Confabulations22.8%—
Vectara Hallucination Rate4.1%—
LMArena Expert1225—
MMLU86.3%—

Multilingual Not comparable

Llama-3.3-70B-Instruct: 40.1 (#215), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
LMArena Non-English1236—
LMArena Chinese1217—
LMArena French1280—
LMArena German1251—
LMArena Japanese1149—
LMArena Korean1142—
LMArena Russian1252—
LMArena Spanish1269—

Instruction Following Not comparable

Llama-3.3-70B-Instruct: 71.3 (#152), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
LiveBench Instruction Following82.7%—
LMArena Instruction Following1242—

Long Context Not comparable

Llama-3.3-70B-Instruct: 26.4 (#290), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
Fiction.LiveBench33.3%—
LMArena Longer Query1256—

Writing & Preference Not comparable

Llama-3.3-70B-Instruct: 47.8 (#203), Mercury 2.5: —

BenchmarkLlama-3.3-70B-InstructMercury 2.5
LMArena Text1274—
LMArena Creative Writing1250—
LMArena Multi-Turn1279—
LiveBench Language39.2%—

Frequently asked questions

Is Llama-3.3-70B-Instruct better than Mercury 2.5?

Llama-3.3-70B-Instruct has enough public results to be ranked (#283); Mercury 2.5 does not yet, so treat this comparison as directional.

Which is cheaper, Llama-3.3-70B-Instruct or Mercury 2.5?

Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.

Which has the bigger context window?

Mercury 2.5 does, with 260K tokens against 128K.

How many benchmarks do Llama-3.3-70B-Instruct and Mercury 2.5 share?

0 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Mercury 2.5 has 2.

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