Llama 3.2 1B Instruct vs Summarize

Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Llama 3.2 1B Instruct Replace Summarize

Llama 3.2 1B InstructMeta Llama

Coding: No data
Overall: 4.8
Output $/M: $0.201

S SummarizeScaledown

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

vs 60K vs 1M context window. Summarize fits 16.7x more.

Decision reading

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 ALlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task Model BSummarizeScaledownNot benchmarkedNo coding benchmarkNo cost per solved task

Shared results: 0
Llama 3.2 1B Instruct only: 37
Summarize only: 0
Like-for-like categories: 0 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
Summarize
Summarize takes 1M tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Summarize
Llama 3.2 1B Instruct $0.0001, Summarize $0.0000 per turn.
List prices

At a glance

Coding index: Llama 3.2 1B Instruct not benchmarked, Summarize not benchmarked.
Widest category: No category has a result for both models.
Widest single gap: No benchmark has a result for both models.
Output price: Llama 3.2 1B Instruct $0.201 against Summarize Free per million output tokens.
Context window: Llama 3.2 1B Instruct takes 60K tokens, Summarize 1M.

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.

CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared

Coding benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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 34 boards here

IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared

Intelligence benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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 index2 boards, none shared

Agentic work benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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
Neither model has a result on the other 37 boards here

ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared

Reasoning benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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 15 boards here

MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared

Math benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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, none shared

Knowledge benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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 12 boards here

Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Web design benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	Leader
Neither model has a result on 9 boards here

GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared

General benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	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

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for Llama 3.2 1B Instruct and Summarize
Benchmark	Llama 3.2 1B Instruct	Summarize	Leader
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

Llama 3.2 1B Instruct$0.0001Fits in one request

Summarize$0.0000Fits in one request

Summarize costs less

Repository review

50K input + 3K output tokens

Llama 3.2 1B Instruct$0.0020Fits in one request

Summarize$0.0020Fits 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

Summarize$0.0080Fits 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 Summarize
Specification	Meta Llama 3.2 1B Instruct	ScaleDown Summarize
Maker	Meta Llama	Scaledown
Weights	Open weights	No public weights listed
Released	September 25, 2024	October 13, 2026
Context window	60K	1M
Input $/Mtok	$0.027	$0.04
Output $/Mtok	$0.201	Free
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 Summarize 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, Llama 3.2 1B Instruct or Summarize?

Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Summarize costs $0.04 and Free. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.2 1B Instruct or Summarize?

Summarize, with 1M tokens against 60K.

How many benchmarks is the Llama 3.2 1B Instruct vs Summarize comparison based on?

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

Decision reading

Which one for your work

Coding

Intelligence

Long documents

Everyday cost