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Command A vs Llama 3.2 3B Instruct

Command A leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 13.1 to 5.7, and Llama 3.2 3B Instruct costs 30.1x less per output token ($0.3325 vs $10.00 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace Command A Replace Llama 3.2 3B Instruct
Command ACohere
Coding
No data
Overall
No data
Output $/M
$10.00
34
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
No data
Output $/M
$0.3325
0
vs boards won of 34 shared

Command A or Llama 3.2 3B Instruct: which to pick

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

Pick Command A for a larger context window (256K against 131K tokens).

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel BLlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
Command A only
28
Llama 3.2 3B Instruct only
1
Like-for-like categories
7 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
Command A not benchmarked, Llama 3.2 3B Instruct not benchmarked.
Widest category
Agentic work, 42.1 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Command A 80.7% against Llama 3.2 3B Instruct 21.1%, 59.6 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 30.1x apart.
Context window
Command A takes 256K 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
CategoryCommand ABarsLlama 3.2 3B InstructTies
Coding30
Intelligence10
Agentic work20
Reasoning50
Math40
Knowledge50
General140
All shared boards340
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
AA Coding Index27.8Not measuredOnly Command A measured
LiveCodeBench (Vals AI)35.1%Not measuredOnly Command A measured
SWE-bench Verified (Vals AI)7.8%Not measuredOnly Command A measured
Terminal-Bench 2.1 (Vals AI)17.6%Not measuredOnly Command A measured
LiveCodeBench (Artificial Analysis run)28.7%8.3%Command A
SciCode38.5%Not measuredOnly Command A measured
Terminal-Bench Hard (Artificial Analysis run)25.0%Not measuredOnly Command A measured
LMArena Text: coding13301098Command A
LMArena Text: software and IT13361103Command A
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
AA Intelligence Index13.15.7Command A
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
AA Agentic Index1.0Not measuredOnly Command A measured
Finance Agent (Vals AI)9.0%Not measuredOnly Command A measured
τ²-Bench Telecom80.7%21.1%Command A
BALROGNot measured10.1%Only Llama 3.2 3B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)46.5%21.9%Command A
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
GPQA Diamond48.5%Not measuredOnly Command A measured
GPQA Diamond (Artificial Analysis run)76.1%25.5%Command A
Humanity's Last Exam (Artificial Analysis run)12.0%5.3%Command A
LMArena Text: hard prompts13261095Command A
LMArena Text: expert12961090Command A
LMArena Text: hard prompts in English13351141Command A
Kagi LLM Benchmark28.8%Not measuredOnly Command A measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
AIME13.3%Not measuredOnly Command A measured
MATH 500 (Vals AI)76.2%Not measuredOnly Command A measured
MGSM (Vals AI)85.7%Not measuredOnly Command A measured
AA Math Index13.03.3Command A
AIME 2025 (Artificial Analysis run)13.0%3.3%Command A
LMArena Text: math13001126Command A
LMArena Text: mathematical work13121128Command A
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
MMLU Pro69.2%Not measuredOnly Command A measured
LegalBench (Vals AI)79.7%Not measuredOnly Command A measured
CaseLaw (Vals AI)64.5%Not measuredOnly Command A measured
CorpFin (Vals AI)46.0%Not measuredOnly Command A measured
TaxEval (Vals AI)61.4%Not measuredOnly Command A measured
MedQA (Vals AI)80.5%Not measuredOnly Command A measured
MedCode (Vals AI)19.7%Not measuredOnly Command A measured
MMLU Pro (Artificial Analysis run)71.2%34.7%Command A
LMArena Text: science13391106Command A
LMArena Text: medicine and healthcare13341104Command A
LMArena Text: legal and government13481115Command A
LMArena Text: business and finance13151074Command A
Neither model has a result on the other 6 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 3.2 3B InstructLeader
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
IFBench73.9%26.2%Command A
Long context reasoning (AA-LCR)52.7%4.3%Command A
DTBench61.3%Not measuredOnly Command A measured
LMCA10.3%Not measuredOnly Command A measured
LMArena Text13311110Command A
LMArena Text: instruction following13091089Command A
LMArena Text: creative writing13191094Command A
LMArena Text: English13441180Command A
LMArena Text: non-English13131019Command A
LMArena Text: Chinese13271017Command A
LMArena Text: German13421056Command A
LMArena Text: Japanese1285Not measuredOnly Command A measured
LMArena Text: Korean1285Not measuredOnly Command A measured
LMArena Text: French1351Not measuredOnly Command A measured
LMArena Text: Spanish1347Not measuredOnly Command A measured
LMArena Text: Russian1314949Command A
LMArena Text: Polish1324Not measuredOnly Command A measured
LMArena Text: multi-turn13381105Command A
LMArena Text: longer queries13341100Command A
LMArena Text: writing and language13281096Command A
LMArena Text: entertainment and media13061083Command A
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command A and Llama 3.2 3B Instruct
BenchmarkCommand ALlama 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

Command A$0.0075Fits 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

Command A$0.155Fits 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

Command A$0.600Fits in one request
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 Command A and Llama 3.2 3B Instruct
SpecificationCohere Command AMeta Llama 3.2 3B Instruct
MakerCohereMeta Llama
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025September 25, 2024
Context window256K131K
Input $/Mtok$2.50$0.0505
Output $/Mtok$10.00$0.3325
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved task$2.69Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than Llama 3.2 3B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 131K tokens).

Which is cheaper, Command A or Llama 3.2 3B Instruct?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for Command A only.

Which has the bigger context window, Command A or Llama 3.2 3B Instruct?

Command A, with 256K tokens against 131K.

How many benchmarks is the Command A vs Llama 3.2 3B Instruct comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. Command A'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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