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GLM 4.5V vs Llama 3.1 8B Instruct

GLM 4.5V leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 7.6 to 6.9, and Llama 3.1 8B Instruct costs 22.5x less per output token ($0.08 vs $1.80 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace GLM 4.5V Replace Llama 3.1 8B Instruct
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
33
Llama 3.1 8B InstructMeta Llama
Coding
No data
Overall
6.9
Output $/M
$0.08
1
vs boards won of 34 shared

GLM 4.5V or Llama 3.1 8B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.5V for work measured by Long context reasoning (AA-LCR), where it leads.

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BLlama 3.1 8B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
GLM 4.5V only
6
Llama 3.1 8B Instruct only
16
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 4.5V not benchmarked, Llama 3.1 8B Instruct not benchmarked.
Widest category
Math, 48.1 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: GLM 4.5V 73.0 against Llama 3.1 8B Instruct 4.3, 68.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against Llama 3.1 8B Instruct $0.08 per million output tokens, 22.5x apart.
Context window
GLM 4.5V takes 66K tokens, Llama 3.1 8B Instruct 131K.

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 4.5VBarsLlama 3.1 8B InstructTies
Coding40
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General131
All shared boards331
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
AA Coding IndexNot measured5.4Only Llama 3.1 8B Instruct measured
LiveCodeBench (Artificial Analysis run)60.4%11.6%GLM 4.5V
Terminal-Bench Hard (Artificial Analysis run)6.8%0.8%GLM 4.5V
WeirdMLNot measured1.7%Only Llama 3.1 8B Instruct measured
LMArena Text: coding13471195GLM 4.5V
LMArena Text: software and IT13531195GLM 4.5V
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
AA Intelligence Index7.66.9GLM 4.5V
Epoch Capabilities IndexNot measured116.5Only Llama 3.1 8B Instruct measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
τ²-Bench Telecom22.5%16.4%GLM 4.5V
BALROGNot measured15.1%Only Llama 3.1 8B 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 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
GPQA Diamond (Artificial Analysis run)68.4%25.9%GLM 4.5V
Humanity's Last Exam (Artificial Analysis run)6.3%5.3%GLM 4.5V
GPQA Diamond (Epoch AI run)Not measured27.0%Only Llama 3.1 8B Instruct measured
LMArena Text: hard prompts13351175GLM 4.5V
LMArena Text: expert13531144GLM 4.5V
LMArena Text: hard prompts in English13571196GLM 4.5V
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
AA Math Index73.04.3GLM 4.5V
AIME 2025 (Artificial Analysis run)73.0%4.3%GLM 4.5V
OTIS Mock AIMENot measured1.7%Only Llama 3.1 8B Instruct measured
MATH level 5Not measured22.9%Only Llama 3.1 8B Instruct measured
GSM8KNot measured82.4%Only Llama 3.1 8B Instruct measured
LMArena Text: math13541179GLM 4.5V
LMArena Text: mathematical work13361180GLM 4.5V
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 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
MMLU Pro (Artificial Analysis run)78.8%47.6%GLM 4.5V
MMLUNot measured56.1%Only Llama 3.1 8B Instruct measured
LMArena Text: science13301193GLM 4.5V
LMArena Text: medicine and healthcare13491182GLM 4.5V
LMArena Text: legal and government13381206GLM 4.5V
LMArena Text: business and finance13091166GLM 4.5V
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index131
General benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B InstructLeader
IFBench34.2%28.6%GLM 4.5V
Long context reasoning (AA-LCR)0.0%18.0%Llama 3.1 8B Instruct
DTBenchNot measured50.9%Only Llama 3.1 8B Instruct measured
LMCANot measured5.4%Only Llama 3.1 8B Instruct measured
PIQANot measured81.2%Only Llama 3.1 8B Instruct measured
LMArena Text13331187GLM 4.5V
LMArena Text: instruction following13111159GLM 4.5V
LMArena Text: creative writing12941154GLM 4.5V
LMArena Vision1154Not measuredOnly GLM 4.5V measured
LMArena Text: English13621218GLM 4.5V
LMArena Text: non-English13031148GLM 4.5V
LMArena Text: Chinese13371151GLM 4.5V
LMArena Text: GermanNot measured1144Only Llama 3.1 8B Instruct measured
LMArena Text: JapaneseNot measured1061Only Llama 3.1 8B Instruct measured
LMArena Text: KoreanNot measured1053Only Llama 3.1 8B Instruct measured
LMArena Text: FrenchNot measured1178Only Llama 3.1 8B Instruct measured
LMArena Text: Spanish13361169GLM 4.5V
LMArena Text: Russian12981158GLM 4.5V
LMArena Text: Polish1302Not measuredOnly GLM 4.5V measured
LMArena Text: multi-turn13331172GLM 4.5V
LMArena Text: longer queries13051181GLM 4.5V
LMArena Text: writing and language13021162GLM 4.5V
LMArena Text: entertainment and media13031145GLM 4.5V
LMArena Vision: English1183Not measuredOnly GLM 4.5V measured
LMArena Vision: reading text1151Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5V and Llama 3.1 8B Instruct
BenchmarkGLM 4.5VLlama 3.1 8B 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 4.5V$0.0015Fits in one request
Llama 3.1 8B Instruct$0.0001Fits in one request

Llama 3.1 8B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
Llama 3.1 8B Instruct$0.0027Fits in one request

Llama 3.1 8B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
Llama 3.1 8B Instruct$0.011Exceeds the context window

Llama 3.1 8B 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 4.5V and Llama 3.1 8B Instruct
SpecificationZ.ai GLM 4.5VMeta Llama 3.1 8B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedAugust 11, 2025July 23, 2024
Context window66K131K
Input $/Mtok$0.60$0.05
Output $/Mtok$1.80$0.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than Llama 3.1 8B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5V for work measured by Long context reasoning (AA-LCR), where it leads.

Which is cheaper, GLM 4.5V or Llama 3.1 8B Instruct?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Llama 3.1 8B Instruct costs $0.05 and $0.08. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5V or Llama 3.1 8B Instruct?

Llama 3.1 8B Instruct, with 131K tokens against 66K.

How many benchmarks is the GLM 4.5V vs Llama 3.1 8B Instruct comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Llama 3.1 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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