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GLM 4.5 vs Llama 3.1 8B Instruct

GLM 4.5 leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 12.8 to 6.9, and Llama 3.1 8B Instruct costs 27.5x less per output token ($0.08 vs $2.20 per million). Updated October 21, 2026; 39 benchmarks have a result for both.

Replace GLM 4.5 Replace Llama 3.1 8B Instruct
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
39
Llama 3.1 8B InstructMeta Llama
Coding
No data
Overall
17.9
Output $/M
$0.08
0
vs boards won of 39 shared

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

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 3.1 8B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
39
GLM 4.5 only
22
Llama 3.1 8B Instruct only
12
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 4.5 not benchmarked, Llama 3.1 8B Instruct not benchmarked.
Widest category
Math, 55.4 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: GLM 4.5 73.7 against Llama 3.1 8B Instruct 4.3, 69.4 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Llama 3.1 8B Instruct $0.08 per million output tokens, 27.5x 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 3.1 8B InstructTies
Coding50
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General180
All shared boards390
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for GLM 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B InstructLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
AA Coding IndexNot measured5.4Only Llama 3.1 8B Instruct measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%11.6%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%0.8%GLM 4.5
WeirdML40.6%1.7%GLM 4.5
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341195GLM 4.5
LMArena Text: software and IT14401195GLM 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 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B InstructLeader
AA Intelligence Index12.86.9GLM 4.5
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 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B InstructLeader
τ²-Bench Telecom43.0%16.4%GLM 4.5
BALROGNot measured15.1%Only Llama 3.1 8B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)Not measured25.8%Only Llama 3.1 8B Instruct measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B InstructLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%25.9%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%5.3%GLM 4.5
GPQA Diamond (Epoch AI run)Not measured27.0%Only Llama 3.1 8B Instruct measured
LMArena Text: hard prompts14291175GLM 4.5
LMArena Text: expert14331144GLM 4.5
LMArena Text: hard prompts in English14291196GLM 4.5
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B 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 Index73.74.3GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%4.3%GLM 4.5
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: math14271179GLM 4.5
LMArena Text: mathematical work14301180GLM 4.5
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B 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%47.6%GLM 4.5
MMLUNot measured56.1%Only Llama 3.1 8B Instruct measured
LMArena Text: science14441193GLM 4.5
LMArena Text: medicine and healthcare14551182GLM 4.5
LMArena Text: legal and government14381206GLM 4.5
LMArena Text: business and finance14201166GLM 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 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B 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 index180
General benchmarks for GLM 4.5 and Llama 3.1 8B Instruct
BenchmarkGLM 4.5Llama 3.1 8B InstructLeader
IFBench44.1%28.6%GLM 4.5
Long context reasoning (AA-LCR)52.7%18.0%GLM 4.5
DTBenchNot measured50.9%Only Llama 3.1 8B Instruct measured
LMCANot measured5.4%Only Llama 3.1 8B Instruct measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
PIQANot measured81.2%Only Llama 3.1 8B Instruct measured
LMArena Text14301187GLM 4.5
LMArena Text: instruction following14041159GLM 4.5
LMArena Text: creative writing13951154GLM 4.5
LMArena Text: English14371218GLM 4.5
LMArena Text: non-English14171148GLM 4.5
LMArena Text: Chinese14651151GLM 4.5
LMArena Text: German14071144GLM 4.5
LMArena Text: Japanese14151061GLM 4.5
LMArena Text: Korean13801053GLM 4.5
LMArena Text: French14181178GLM 4.5
LMArena Text: Spanish14541169GLM 4.5
LMArena Text: Russian14131158GLM 4.5
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn14151172GLM 4.5
LMArena Text: longer queries14121181GLM 4.5
LMArena Text: writing and language14031162GLM 4.5
LMArena Text: entertainment and media14041145GLM 4.5
Neither model has a result on the other 23 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 3.1 8B Instruct
BenchmarkGLM 4.5Llama 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 4.5$0.0017Fits 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 4.5$0.037Fits 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 4.5$0.142Exceeds 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 4.5 and Llama 3.1 8B Instruct
SpecificationZ.ai GLM 4.5Meta Llama 3.1 8B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025July 23, 2024
Context window131K131K
Input $/Mtok$0.60$0.05
Output $/Mtok$2.20$0.08
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 3.1 8B 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 3.1 8B Instruct?

GLM 4.5 costs $0.60 per million input tokens and $2.20 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 4.5 or Llama 3.1 8B Instruct?

Neither: both take 131K tokens.

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

39 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 3.1 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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