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GLM 4.5 vs Llama 4 Scout

Updated October 21, 2026. 55 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 4 Scout
GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
55
Llama 4 ScoutMeta Llama
Coding
9.1
Overall
6.5
Output $/M
$0.30
0
vs boards won of 55 shared

Decision reading

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 9.1 for Llama 4 Scout.

Pick Llama 4 Scout when output tokens cost 7.3x less, or when the context window is larger, 1.31M against 131K tokens.

Model AGLM 4.5Z.ai64.2/1001 independent benchmarkNo cost per solved taskModel BLlama 4 ScoutMeta Llama9.1/1001 independent benchmark$1.276 per solved task
Shared results
55
GLM 4.5 only
5
Llama 4 Scout only
25
Like-for-like categories
8 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 64.2 from 1 benchmark, Llama 4 Scout 9.1 from 1 benchmark.
Widest category
Web design, 45.2 points apart on average over 5 shared benchmarks.
Widest single gap
AIME: GLM 4.5 86.7% against Llama 4 Scout 19.0%, 67.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Llama 4 Scout $0.30 per million output tokens, 7.3x apart.
Context window
GLM 4.5 takes 131K tokens, Llama 4 Scout 1.31M.

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 4 ScoutTies
Coding60
Intelligence10
Agentic work10
Reasoning60
Math70
Knowledge100
Web design50
General190
All shared boards550
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
Coding benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
SWE-bench Verified64.2%9.1%GLM 4.5
AA Coding IndexNot measured8.2Only Llama 4 Scout measured
LiveCodeBench (Vals AI)67.5%38.5%GLM 4.5
LiveCodeBench (Artificial Analysis run)73.8%29.9%GLM 4.5
SciCodeNot measured21.3%Only Llama 4 Scout measured
Terminal-Bench Hard (Artificial Analysis run)22.0%1.5%GLM 4.5
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341286GLM 4.5
LMArena Text: software and IT14401286GLM 4.5
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
AA Intelligence Index12.86.5GLM 4.5
Epoch Capabilities IndexNot measured129.6Only Llama 4 Scout measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
AA Agentic IndexNot measured0.5Only Llama 4 Scout measured
τ²-Bench Telecom43.0%15.5%GLM 4.5
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
GPQA Diamond72.2%47.0%GLM 4.5
MMMU Pro (Vals AI)Not measured58.8%Only Llama 4 Scout measured
GPQA Diamond (Artificial Analysis run)78.2%58.7%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%3.8%GLM 4.5
GPQA Diamond (Epoch AI run)Not measured51.8%Only Llama 4 Scout measured
ARC-AGINot measured0.5%Only Llama 4 Scout measured
ARC-AGI-2Not measured0.0%Only Llama 4 Scout measured
LMArena Text: hard prompts14291266GLM 4.5
LMArena Text: expert14331235GLM 4.5
LMArena Text: hard prompts in English14291293GLM 4.5
LMArena Vision: diagramsNot measured1125Only Llama 4 Scout measured
LMArena Vision: homeworkNot measured1148Only Llama 4 Scout measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index70
Math benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
AIME86.7%19.0%GLM 4.5
MATH 500 (Vals AI)94.0%79.2%GLM 4.5
MGSM (Vals AI)90.8%88.0%GLM 4.5
AA Math Index73.714.0GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%14.0%GLM 4.5
OTIS Mock AIMENot measured7.8%Only Llama 4 Scout measured
MATH level 5Not measured62.3%Only Llama 4 Scout measured
LMArena Text: math14271287GLM 4.5
LMArena Text: mathematical work14301286GLM 4.5
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
Knowledge benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
MMLU Pro81.2%69.6%GLM 4.5
LegalBench (Vals AI)75.6%72.0%GLM 4.5
CorpFin (Vals AI)61.0%46.8%GLM 4.5
TaxEval (Vals AI)72.4%55.2%GLM 4.5
MortgageTax (Vals AI)Not measured57.8%Only Llama 4 Scout measured
MedQA (Vals AI)90.0%50.9%GLM 4.5
MedCode (Vals AI)Not measured23.3%Only Llama 4 Scout measured
MMLU Pro (Artificial Analysis run)83.5%75.2%GLM 4.5
LMArena Text: science14441278GLM 4.5
LMArena Text: medicine and healthcare14551275GLM 4.5
LMArena Text: legal and government14381285GLM 4.5
LMArena Text: business and finance14201257GLM 4.5
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
Design Arena: websites1170750GLM 4.5
Design Arena: UI components1144767GLM 4.5
Design Arena: game dev1145773GLM 4.5
Design Arena: data viz1159896GLM 4.5
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165790GLM 4.5
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 index190
General benchmarks for GLM 4.5 and Llama 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
SAGE (Vals AI)Not measured34.8%Only Llama 4 Scout measured
MedScribe (Vals AI)Not measured50.6%Only Llama 4 Scout measured
IFBench44.1%39.5%GLM 4.5
Long context reasoning (AA-LCR)52.7%27.7%GLM 4.5
DTBenchNot measured57.9%Only Llama 4 Scout measured
LMCANot measured12.0%Only Llama 4 Scout measured
Fiction.LiveBenchNot measured36.0%Only Llama 4 Scout measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301279GLM 4.5
LMArena Text: instruction following14041248GLM 4.5
LMArena Text: creative writing13951249GLM 4.5
LMArena VisionNot measured1118Only Llama 4 Scout measured
LMArena Text: English14371304GLM 4.5
LMArena Text: non-English14171252GLM 4.5
LMArena Text: Chinese14651255GLM 4.5
LMArena Text: German14071273GLM 4.5
LMArena Text: Japanese14151206GLM 4.5
LMArena Text: Korean13801207GLM 4.5
LMArena Text: French14181283GLM 4.5
LMArena Text: Spanish14541278GLM 4.5
LMArena Text: Russian14131263GLM 4.5
LMArena Text: Polish14221265GLM 4.5
LMArena Text: multi-turn14151280GLM 4.5
LMArena Text: longer queries14121265GLM 4.5
LMArena Text: writing and language14031269GLM 4.5
LMArena Text: entertainment and media14041242GLM 4.5
LMArena Vision: EnglishNot measured1143Only Llama 4 Scout measured
LMArena Vision: ChineseNot measured1106Only Llama 4 Scout measured
LMArena Vision: reading textNot measured1117Only Llama 4 Scout measured
LMArena Vision: humorNot measured1102Only Llama 4 Scout measured
LMArena Vision: creative writingNot measured1126Only Llama 4 Scout measured
Neither model has a result on the other 15 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 4 Scout
BenchmarkGLM 4.5Llama 4 ScoutLeader
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 4 Scout$0.0003Fits in one request

Llama 4 Scout costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Llama 4 Scout$0.0059Fits in one request

Llama 4 Scout costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Llama 4 Scout$0.023Fits in one request

Llama 4 Scout 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 4 Scout
SpecificationZ.ai GLM 4.5Meta Llama 4 Scout
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025April 5, 2025
Context window131K1.31M
Input $/Mtok$0.60$0.10
Output $/Mtok$2.20$0.30
Coding index64.29.1
Independent measurements behind it11
Cost per solved taskNot computable$1.276
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Llama 4 Scout for coding?

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 7.3x less, or when the context window is larger, 1.31M against 131K tokens.

Which is cheaper, GLM 4.5 or Llama 4 Scout?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Llama 4 Scout costs $0.10 and $0.30. Cost per solved task is known for Llama 4 Scout only.

Which has the bigger context window, GLM 4.5 or Llama 4 Scout?

Llama 4 Scout, with 1.31M tokens against 131K.

How many benchmarks is the GLM 4.5 vs Llama 4 Scout comparison based on?

55 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and Llama 4 Scout's on 1. Where only one model has a result, the page says so instead of naming a winner.

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