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GLM 4.5 vs Llama 3.2 3B Instruct

Updated October 21, 2026. 33 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5 Replace Llama 3.2 3B Instruct
GLM 4.5Z.ai
Coding
No data
Overall
12.8
Output $/M
$2.20
33
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
5.7
Output $/M
$0.3325
0
vs boards won of 33 shared

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 AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BLlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
33
GLM 4.5 only
27
Llama 3.2 3B Instruct only
1
Like-for-like categories
7 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.

At a glance

Coding index
GLM 4.5 not benchmarked, Llama 3.2 3B Instruct not benchmarked.
Widest category
Math, 60.3 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena Text: Chinese: GLM 4.5 1465 against Llama 3.2 3B Instruct 1017, 74.6 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 6.6x apart.
Context window
Both take 131K tokens.

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
CategoryGLM 4.5BarsLlama 3.2 3B InstructTies
Coding30
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General140
All shared boards330
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%8.3%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.5 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341098GLM 4.5
LMArena Text: software and IT14401103GLM 4.5
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 GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
AA Intelligence Index12.85.7GLM 4.5
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 GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
τ²-Bench Telecom43.0%21.1%GLM 4.5
BALROGNot measured10.1%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 GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%25.5%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%5.3%GLM 4.5
LMArena Text: hard prompts14291095GLM 4.5
LMArena Text: expert14331090GLM 4.5
LMArena Text: hard prompts in English14291141GLM 4.5
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 GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.73.3GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%3.3%GLM 4.5
LMArena Text: math14271126GLM 4.5
LMArena Text: mathematical work14301128GLM 4.5
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 GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%34.7%GLM 4.5
LMArena Text: science14441106GLM 4.5
LMArena Text: medicine and healthcare14551104GLM 4.5
LMArena Text: legal and government14381115GLM 4.5
LMArena Text: business and finance14201074GLM 4.5
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
Design Arena: websites1170Not measuredOnly GLM 4.5 measured
Design Arena: UI components1144Not measuredOnly GLM 4.5 measured
Design Arena: game dev1145Not measuredOnly GLM 4.5 measured
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 3.2 3B InstructLeader
IFBench44.1%26.2%GLM 4.5
Long context reasoning (AA-LCR)52.7%4.3%GLM 4.5
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301110GLM 4.5
LMArena Text: instruction following14041089GLM 4.5
LMArena Text: creative writing13951094GLM 4.5
LMArena Text: English14371180GLM 4.5
LMArena Text: non-English14171019GLM 4.5
LMArena Text: Chinese14651017GLM 4.5
LMArena Text: German14071056GLM 4.5
LMArena Text: Japanese1415Not measuredOnly GLM 4.5 measured
LMArena Text: Korean1380Not measuredOnly GLM 4.5 measured
LMArena Text: French1418Not measuredOnly GLM 4.5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 4.5 measured
LMArena Text: Russian1413949GLM 4.5
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn14151105GLM 4.5
LMArena Text: longer queries14121100GLM 4.5
LMArena Text: writing and language14031096GLM 4.5
LMArena Text: entertainment and media14041083GLM 4.5
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5 and Llama 3.2 3B Instruct
BenchmarkGLM 4.5Llama 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

GLM 4.5$0.0017Fits 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

GLM 4.5$0.037Fits 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

GLM 4.5$0.142Exceeds 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 GLM 4.5 and Llama 3.2 3B Instruct
SpecificationZ.ai GLM 4.5Meta Llama 3.2 3B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025September 25, 2024
Context window131K131K
Input $/Mtok$0.60$0.0505
Output $/Mtok$2.20$0.3325
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 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, GLM 4.5 or Llama 3.2 3B Instruct?

GLM 4.5 costs $0.60 per million input tokens and $2.20 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, GLM 4.5 or Llama 3.2 3B Instruct?

Neither: both take 131K tokens.

How many benchmarks is the GLM 4.5 vs Llama 3.2 3B Instruct comparison based on?

33 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 independent benchmarks 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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