Llama 3.2 1B Instruct vs Relace Apply 3

Llama 3.2 1B Instruct costs 6.2x less per output token than Relace Apply 3 ($0.201 vs $1.25 per million). Updated October 21, 2026; no benchmark has tested both models yet.

Replace Llama 3.2 1B Instruct Replace Relace Apply 3

Llama 3.2 1B InstructMeta Llama

Coding: No data
Overall: 9.6
Output $/M: $0.201

Relace Apply 3Relace

Coding: No data
Overall: No data
Output $/M: $1.25

vs $0.201 vs $1.25 per 1M output tokens. Llama 3.2 1B Instruct is 6.2x cheaper.

Llama 3.2 1B 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 1B Instruct for output tokens 6.2x cheaper ($0.201 vs $1.25 per million).

Model ALlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task Model BRelace Apply 3RelaceNot benchmarkedNo coding benchmarkNo cost per solved task

No benchmark has tested both yet. Llama 3.2 1B Instruct has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding; 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.

Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Llama 3.2 1B Instruct has a AA Intelligence Index result, 4.8.
Directional only
Long documents
Listed context window
Relace Apply 3
Relace Apply 3 takes 256K tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Llama 3.2 1B Instruct $0.0001, Relace Apply 3 $0.0015 per turn.
List prices

At a glance

Coding index: Llama 3.2 1B 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 1B Instruct $0.201 against Relace Apply 3 $1.25 per million output tokens, 6.2x apart.
Context window: Llama 3.2 1B Instruct takes 60K 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 Index4 boards measured

Coding benchmarks for Llama 3.2 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
LiveCodeBench (Artificial Analysis run)	1.9%	Not measured	Only Llama 3.2 1B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)	0.0%	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: coding	1070	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: software and IT	1056	Not measured	Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 35 boards here

IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured

Intelligence benchmarks for Llama 3.2 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
AA Intelligence Index	4.8	Not measured	Only Llama 3.2 1B 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 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
τ²-Bench Telecom	0.0%	Not measured	Only Llama 3.2 1B Instruct measured
BALROG	6.6%	Not measured	Only Llama 3.2 1B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)	10.8%	Not measured	Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 38 boards here

ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured

Reasoning benchmarks for Llama 3.2 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
GPQA Diamond (Artificial Analysis run)	19.6%	Not measured	Only Llama 3.2 1B Instruct measured
Humanity's Last Exam (Artificial Analysis run)	5.5%	Not measured	Only Llama 3.2 1B Instruct measured
GPQA Diamond (Epoch AI run)	23.9%	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: hard prompts	1044	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: expert	1007	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: hard prompts in English	1087	Not measured	Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 16 boards here

MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured

Math benchmarks for Llama 3.2 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
AA Math Index	0.0	Not measured	Only Llama 3.2 1B Instruct measured
AIME 2025 (Artificial Analysis run)	0.0%	Not measured	Only Llama 3.2 1B Instruct measured
OTIS Mock AIME	0.6%	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: math	1086	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: mathematical work	1098	Not measured	Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 8 boards here

KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured

Knowledge benchmarks for Llama 3.2 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
MMLU Pro (Artificial Analysis run)	20.0%	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: science	1055	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: medicine and healthcare	1009	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: legal and government	1071	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: business and finance	1000	Not measured	Only Llama 3.2 1B 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 1B Instruct and Relace Apply 3
Benchmark	Llama 3.2 1B Instruct	Relace Apply 3	Leader
IFBench	22.8%	Not measured	Only Llama 3.2 1B Instruct measured
Long context reasoning (AA-LCR)	6.7%	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text	1055	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: instruction following	1031	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: creative writing	1033	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: English	1117	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: non-English	973	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: Chinese	959	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: German	1014	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: Russian	941	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: multi-turn	1030	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: longer queries	1050	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: writing and language	1030	Not measured	Only Llama 3.2 1B Instruct measured
LMArena Text: entertainment and media	1025	Not measured	Only Llama 3.2 1B 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 1B Instruct$0.0001Fits in one request

Relace Apply 3$0.0015Fits in one request

Llama 3.2 1B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.2 1B Instruct$0.0020Fits in one request

Relace Apply 3$0.046Fits in one request

Llama 3.2 1B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.2 1B Instruct$0.0074Exceeds the context window

Relace Apply 3$0.182Fits in one request

Llama 3.2 1B 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 1B Instruct and Relace Apply 3
Specification	Meta Llama 3.2 1B Instruct	Relace Apply 3
Maker	Meta Llama	Relace
Weights	Open weights	No public weights listed
Released	September 25, 2024	September 26, 2025
Context window	60K	256K
Input $/Mtok	$0.027	$0.85
Output $/Mtok	$0.201	$1.25
Coding index	Not benchmarked	Not benchmarked
Independent measurements behind it	0	0
Cost per solved task	Not computable	Not computable
Source	OpenRouter listing	OpenRouter listing

Questions

Is Llama 3.2 1B 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 1B Instruct for output tokens 6.2x cheaper ($0.201 vs $1.25 per million).

Which is cheaper, Llama 3.2 1B Instruct or Relace Apply 3?

Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 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 1B Instruct or Relace Apply 3?

Relace Apply 3, with 256K tokens against 60K.

Has any benchmark tested both Llama 3.2 1B Instruct and Relace Apply 3?

Llama 3.2 1B Instruct or Relace Apply 3: which to pick

Which one for your work

Coding

Intelligence

Long documents

Everyday cost

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

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