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GLM 4.7 vs Llama 4 Scout

Updated October 21, 2026. 59 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace Llama 4 Scout
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
58
Llama 4 ScoutMeta Llama
Coding
9.1
Overall
6.5
Output $/M
$0.30
1
vs boards won of 59 shared

Decision reading

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 9.1 for Llama 4 Scout.

Pick Llama 4 Scout when output tokens cost 6.6x less, or when the context window is larger, 1.31M against 205K tokens.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BLlama 4 ScoutMeta Llama9.1/1001 independent benchmark$1.276 per solved task
Shared results
59
GLM 4.7 only
21
Llama 4 Scout only
21
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 4.7 33.6 from 1 benchmark, Llama 4 Scout 9.1 from 1 benchmark.
Widest category
Agentic work, 80.4 points apart on average over 1 shared benchmark.
Widest single gap
AA Math Index: GLM 4.7 95.0 against Llama 4 Scout 14.0, 81.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against Llama 4 Scout $0.30 per million output tokens, 6.6x apart.
Context window
GLM 4.7 takes 205K 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 4.7BarsLlama 4 ScoutTies
Coding60
Intelligence20
Agentic work10
Reasoning70
Math70
Knowledge101
Web design50
General200
All shared boards581
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
Coding benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
SWE-bench VerifiedNot measured9.1%Only Llama 4 Scout measured
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.38.2GLM 4.7
LiveCodeBench (Vals AI)82.2%38.5%GLM 4.7
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%29.9%GLM 4.7
SciCodeNot measured21.3%Only Llama 4 Scout measured
Terminal-Bench Hard (Artificial Analysis run)31.8%1.5%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541286GLM 4.7
LMArena Text: software and IT14531286GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
AA Intelligence Index22.26.5GLM 4.7
Epoch Capabilities Index143.6129.6GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
AA Agentic IndexNot measured0.5Only Llama 4 Scout measured
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%15.5%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
Reasoning benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
GPQA Diamond80.0%47.0%GLM 4.7
MMMU Pro (Vals AI)Not measured58.8%Only Llama 4 Scout measured
GPQA Diamond (Artificial Analysis run)85.9%58.7%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%3.8%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%51.8%GLM 4.7
ARC-AGINot measured0.5%Only Llama 4 Scout measured
ARC-AGI-2Not measured0.0%Only Llama 4 Scout measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431266GLM 4.7
LMArena Text: expert14241235GLM 4.7
LMArena Text: hard prompts in English14521293GLM 4.7
LMArena Vision: diagramsNot measured1125Only Llama 4 Scout measured
LMArena Vision: homeworkNot measured1148Only Llama 4 Scout measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index70
Math benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
AIME93.3%19.0%GLM 4.7
MATH 500 (Vals AI)Not measured79.2%Only Llama 4 Scout measured
MGSM (Vals AI)88.2%88.0%GLM 4.7
AA Math Index95.014.0GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%14.0%GLM 4.7
OTIS Mock AIME83.3%7.8%GLM 4.7
ProofBench6.0%Not measuredOnly GLM 4.7 measured
MATH level 5Not measured62.3%Only Llama 4 Scout measured
LMArena Text: math14241287GLM 4.7
LMArena Text: mathematical work14111286GLM 4.7
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index101
Knowledge benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
MMLU Pro82.7%69.6%GLM 4.7
LegalBench (Vals AI)83.4%72.0%GLM 4.7
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%46.8%Llama 4 Scout
TaxEval (Vals AI)68.8%55.2%GLM 4.7
MortgageTax (Vals AI)Not measured57.8%Only Llama 4 Scout measured
MedQA (Vals AI)93.7%50.9%GLM 4.7
MedCode (Vals AI)32.8%23.3%GLM 4.7
MMLU Pro (Artificial Analysis run)85.6%75.2%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571278GLM 4.7
LMArena Text: medicine and healthcare14691275GLM 4.7
LMArena Text: legal and government14441285GLM 4.7
LMArena Text: business and finance14291257GLM 4.7
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
Design Arena: websites1226750GLM 4.7
Design Arena: UI components1199767GLM 4.7
Design Arena: game dev1189773GLM 4.7
Design Arena: data viz1195896GLM 4.7
Design Arena: 3D1191Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1217790GLM 4.7
Design Arena: SVG1142Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
General benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 4 ScoutLeader
SAGE (Vals AI)Not measured34.8%Only Llama 4 Scout measured
MedScribe (Vals AI)68.6%50.6%GLM 4.7
IFBench67.9%39.5%GLM 4.7
Long context reasoning (AA-LCR)71.0%27.7%GLM 4.7
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
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351279GLM 4.7
LMArena Text: instruction following14121248GLM 4.7
LMArena Text: creative writing14011249GLM 4.7
LMArena VisionNot measured1118Only Llama 4 Scout measured
LMArena Text: English14531304GLM 4.7
LMArena Text: non-English14171252GLM 4.7
LMArena Text: Chinese14951255GLM 4.7
LMArena Text: German14261273GLM 4.7
LMArena Text: Japanese14391206GLM 4.7
LMArena Text: Korean13991207GLM 4.7
LMArena Text: French14321283GLM 4.7
LMArena Text: Spanish14341278GLM 4.7
LMArena Text: Russian14231263GLM 4.7
LMArena Text: Polish14351265GLM 4.7
LMArena Text: multi-turn14461280GLM 4.7
LMArena Text: longer queries14321265GLM 4.7
LMArena Text: writing and language14111269GLM 4.7
LMArena Text: entertainment and media14001242GLM 4.7
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 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Llama 4 Scout
BenchmarkGLM 4.7Llama 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 4.7$0.0015Fits in one request
Llama 4 Scout$0.0003Fits in one request

Llama 4 Scout costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
Llama 4 Scout$0.0059Fits in one request

Llama 4 Scout costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds 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 4.7 and Llama 4 Scout
SpecificationZ.ai GLM 4.7Meta Llama 4 Scout
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025April 5, 2025
Context window205K1.31M
Input $/Mtok$0.54$0.10
Output $/Mtok$1.98$0.30
Coding index33.69.1
Independent measurements behind it11
Cost per solved task$0.29$1.276
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Llama 4 Scout for coding?

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 6.6x less, or when the context window is larger, 1.31M against 205K tokens.

Which is cheaper, GLM 4.7 or Llama 4 Scout?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Llama 4 Scout costs $0.10 and $0.30. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Llama 4 Scout $1.276.

Which has the bigger context window, GLM 4.7 or Llama 4 Scout?

Llama 4 Scout, with 1.31M tokens against 205K.

How many benchmarks is the GLM 4.7 vs Llama 4 Scout comparison based on?

59 benchmarks have a result for both models as of October 21, 2026. GLM 4.7'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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