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Llama 3.2 3B Instruct vs Relace Apply 3

Llama 3.2 3B Instruct costs 3.8x less per output token than Relace Apply 3 ($0.3325 vs $1.25 per million). Updated October 21, 2026; no benchmark has tested both models yet.

Replace Llama 3.2 3B Instruct Replace Relace Apply 3
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
No data
Output $/M
$0.3325
Relace Apply 3Relace
Coding
No data
Overall
No data
Output $/M
$1.25
vs $0.3325 vs $1.25 per 1M output tokens. Llama 3.2 3B Instruct is 3.8x cheaper.

Llama 3.2 3B Instruct or Relace Apply 3: which to pick

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

Pick Llama 3.2 3B Instruct for output tokens 3.8x cheaper ($0.3325 vs $1.25 per million).

Model ALlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BRelace Apply 3RelaceNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. Llama 3.2 3B Instruct has results on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; Relace Apply 3 has none yet. Compare them on price and context instead.

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
Llama 3.2 3B Instruct not benchmarked, Relace Apply 3 not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
Llama 3.2 3B Instruct $0.3325 against Relace Apply 3 $1.25 per million output tokens, 3.8x apart.
Context window
Llama 3.2 3B Instruct takes 131K tokens, Relace Apply 3 256K.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards measured
Coding benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
LiveCodeBench (Artificial Analysis run)8.3%Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: coding1098Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: software and IT1103Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 36 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Intelligence benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
AA Intelligence Index5.7Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
Agentic work benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
τ²-Bench Telecom21.1%Not measuredOnly Llama 3.2 3B Instruct measured
BALROG10.1%Not measuredOnly Llama 3.2 3B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)21.9%Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 38 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Reasoning benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
GPQA Diamond (Artificial Analysis run)25.5%Not measuredOnly Llama 3.2 3B Instruct measured
Humanity's Last Exam (Artificial Analysis run)5.3%Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: hard prompts1095Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: expert1090Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: hard prompts in English1141Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 17 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
Math benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
AA Math Index3.3Not measuredOnly Llama 3.2 3B Instruct measured
AIME 2025 (Artificial Analysis run)3.3%Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: math1126Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: mathematical work1128Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 9 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
Knowledge benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
MMLU Pro (Artificial Analysis run)34.7%Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: science1106Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: medicine and healthcare1104Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: legal and government1115Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: business and finance1074Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 13 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards measured
General benchmarks for Llama 3.2 3B Instruct and Relace Apply 3
BenchmarkLlama 3.2 3B InstructRelace Apply 3Leader
IFBench26.2%Not measuredOnly Llama 3.2 3B Instruct measured
Long context reasoning (AA-LCR)4.3%Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text1110Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: instruction following1089Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: creative writing1094Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: English1180Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: non-English1019Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: Chinese1017Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: German1056Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: Russian949Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: multi-turn1105Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: longer queries1100Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: writing and language1096Not measuredOnly Llama 3.2 3B Instruct measured
LMArena Text: entertainment and media1083Not measuredOnly Llama 3.2 3B Instruct measured
Neither model has a result on the other 32 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

Llama 3.2 3B Instruct$0.0002Fits in one request
Relace Apply 3$0.0015Fits in one request

Llama 3.2 3B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.2 3B Instruct$0.0035Fits in one request
Relace Apply 3$0.046Fits in one request

Llama 3.2 3B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.2 3B Instruct$0.013Exceeds the context window
Relace Apply 3$0.182Fits in one request

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 Llama 3.2 3B Instruct and Relace Apply 3
SpecificationMeta Llama 3.2 3B InstructRelace Apply 3
MakerMeta LlamaRelace
WeightsOpen weightsNo public weights listed
ReleasedSeptember 25, 2024September 26, 2025
Context window131K256K
Input $/Mtok$0.0505$0.85
Output $/Mtok$0.3325$1.25
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.2 3B Instruct better than Relace Apply 3 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 3.8x cheaper ($0.3325 vs $1.25 per million).

Which is cheaper, Llama 3.2 3B Instruct or Relace Apply 3?

Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Relace Apply 3 costs $0.85 and $1.25. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.2 3B Instruct or Relace Apply 3?

Relace Apply 3, with 256K tokens against 131K.

Has any benchmark tested both Llama 3.2 3B Instruct and Relace Apply 3?

No benchmark has tested both yet. Llama 3.2 3B Instruct has results on LiveCodeBench (Artificial Analysis run), LMArena Text: coding and LMArena Text: software and IT; Relace Apply 3 has none yet. Compare them on price and context instead. Data as of October 21, 2026.

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