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Mercury 2 vs Llama 3.2 3B Instruct

Mercury 2 leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 10.8 to 5.7, and Llama 3.2 3B Instruct costs 2.3x less per output token ($0.3325 vs $0.75 per million). Updated October 21, 2026; 25 benchmarks have a result for both.

Replace Mercury 2 Replace Llama 3.2 3B Instruct
Mercury 2Inception
Coding
No data
Overall
No data
Output $/M
$0.75
25
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
No data
Output $/M
$0.3325
0
vs boards won of 25 shared

Mercury 2 or Llama 3.2 3B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AMercury 2InceptionNot benchmarked1 independent benchmarkNo cost per solved taskModel BLlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
25
Mercury 2 only
17
Llama 3.2 3B Instruct only
10
Like-for-like categories
6 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Mercury 2 not benchmarked, Llama 3.2 3B Instruct not benchmarked.
Widest category
Agentic work, 49.7 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Chinese: Mercury 2 1414 against Llama 3.2 3B Instruct 1017, 66.1 points apart on a 0 to 100 scale.
Output price
Mercury 2 $0.75 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 2.3x apart.
Context window
Mercury 2 takes 128K tokens, Llama 3.2 3B Instruct 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryMercury 2BarsLlama 3.2 3B InstructTies
Coding20
Intelligence10
Agentic work10
Reasoning50
Knowledge30
General130
All shared boards250
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
LMArena WebDev1171Not measuredOnly Mercury 2 measured
AA Coding Index31.1Not measuredOnly Mercury 2 measured
LiveCodeBench (Artificial Analysis run)Not measured8.3%Only Llama 3.2 3B Instruct measured
SciCode37.7%Not measuredOnly Mercury 2 measured
Terminal-Bench Hard (Artificial Analysis run)26.5%Not measuredOnly Mercury 2 measured
WeirdML43.2%Not measuredOnly Mercury 2 measured
ALE-Bench785.6Not measuredOnly Mercury 2 measured
LMArena Text: coding13901098Mercury 2
LMArena Text: software and IT13851103Mercury 2
LMArena WebDev: React1160Not measuredOnly Mercury 2 measured
LMArena WebDev: HTML1199Not measuredOnly Mercury 2 measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
AA Intelligence Index10.85.7Mercury 2
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
AA Agentic Index1.6Not measuredOnly Mercury 2 measured
τ²-Bench Telecom70.8%21.1%Mercury 2
BALROGNot measured10.1%Only Llama 3.2 3B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)Not measured21.9%Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
GPQA Diamond (Artificial Analysis run)77.0%25.5%Mercury 2
Humanity's Last Exam (Artificial Analysis run)17.1%5.3%Mercury 2
LMArena Text: hard prompts13621095Mercury 2
LMArena Text: expert13581090Mercury 2
LMArena Text: hard prompts in English13741141Mercury 2
Neither model has a result on the other 17 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
AA Math IndexNot measured3.3Only Llama 3.2 3B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured3.3%Only Llama 3.2 3B Instruct measured
LMArena Text: mathNot measured1126Only Llama 3.2 3B Instruct measured
LMArena Text: mathematical workNot measured1128Only Llama 3.2 3B Instruct measured
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
Knowledge benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
MMLU Pro (Artificial Analysis run)Not measured34.7%Only Llama 3.2 3B Instruct measured
LMArena Text: science13431106Mercury 2
LMArena Text: medicine and healthcare13631104Mercury 2
LMArena Text: legal and governmentNot measured1115Only Llama 3.2 3B Instruct measured
LMArena Text: business and finance13491074Mercury 2
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
Design Arena: websites1036Not measuredOnly Mercury 2 measured
Design Arena: UI components998Not measuredOnly Mercury 2 measured
Design Arena: game dev985Not measuredOnly Mercury 2 measured
Design Arena: data viz1009Not measuredOnly Mercury 2 measured
Design Arena: 3D983Not measuredOnly Mercury 2 measured
Design Arena: all code categories1022Not measuredOnly Mercury 2 measured
Design Arena: SVG998Not measuredOnly Mercury 2 measured
Design Arena: ASCII art988Not measuredOnly Mercury 2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
General benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
IFBench69.8%26.2%Mercury 2
Long context reasoning (AA-LCR)43.7%4.3%Mercury 2
LMArena Text13541110Mercury 2
LMArena Text: instruction following13281089Mercury 2
LMArena Text: creative writing12861094Mercury 2
LMArena Text: English13781180Mercury 2
LMArena Text: non-English13301019Mercury 2
LMArena Text: Chinese14141017Mercury 2
LMArena Text: GermanNot measured1056Only Llama 3.2 3B Instruct measured
LMArena Text: Russian1304949Mercury 2
LMArena Text: multi-turn13571105Mercury 2
LMArena Text: longer queries13291100Mercury 2
LMArena Text: writing and language13001096Mercury 2
LMArena Text: entertainment and media13141083Mercury 2
Neither model has a result on the other 32 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Mercury 2 and Llama 3.2 3B Instruct
BenchmarkMercury 2Llama 3.2 3B InstructLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Mercury 2$0.0006Fits in one request
Llama 3.2 3B Instruct$0.0002Fits in one request

Llama 3.2 3B Instruct costs less

Repository review

50K input + 3K output tokens

Mercury 2$0.015Fits in one request
Llama 3.2 3B Instruct$0.0035Fits in one request

Llama 3.2 3B Instruct costs less

Agent loop

200K input + 10K output tokens

Mercury 2$0.058Exceeds the context window
Llama 3.2 3B Instruct$0.013Exceeds the context window

Llama 3.2 3B Instruct costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Mercury 2 and Llama 3.2 3B Instruct
SpecificationInception Mercury 2Meta Llama 3.2 3B Instruct
MakerInceptionMeta Llama
WeightsNo public weights listedOpen weights
ReleasedMarch 4, 2026September 25, 2024
Context window128K131K
Input $/Mtok$0.25$0.0505
Output $/Mtok$0.75$0.3325
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mercury 2 better than Llama 3.2 3B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Mercury 2 or Llama 3.2 3B Instruct?

Mercury 2 costs $0.25 per million input tokens and $0.75 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.

Which has the bigger context window, Mercury 2 or Llama 3.2 3B Instruct?

Llama 3.2 3B Instruct, with 131K tokens against 128K.

How many benchmarks is the Mercury 2 vs Llama 3.2 3B Instruct comparison based on?

25 benchmarks have a result for both models as of October 21, 2026. Mercury 2's coding index rests on 1 independent benchmark and Llama 3.2 3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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