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GLM 5.3 vs Llama 3.1 70B Instruct

GLM 5.3 leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 44.8 to 6.6, and Llama 3.1 70B Instruct costs 7.9x less per output token ($0.56 vs $4.40 per million). Updated October 21, 2026; 36 benchmarks have a result for both.

Replace GLM 5.3 Replace Llama 3.1 70B Instruct
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
36
Llama 3.1 70B InstructMeta Llama
Coding
No data
Overall
24.3
Output $/M
$0.56
0
vs boards won of 36 shared

GLM 5.3 or Llama 3.1 70B Instruct: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Llama 3.1 70B Instruct has none, so the two cannot be ranked on coding.

Pick Llama 3.1 70B Instruct for output tokens 7.9x cheaper ($0.56 vs $4.40 per million).

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BLlama 3.1 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
36
GLM 5.3 only
64
Llama 3.1 70B Instruct only
11
Like-for-like categories
6 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 5.3 70.6 from 6 benchmarks, Llama 3.1 70B Instruct not benchmarked.
Widest category
Math, 56.2 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GLM 5.3 91.1% against Llama 3.1 70B Instruct 3.6%, 87.5 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against Llama 3.1 70B Instruct $0.56 per million output tokens, 7.9x apart.
Context window
GLM 5.3 takes 1.05M tokens, Llama 3.1 70B 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 5.3BarsLlama 3.1 70B InstructTies
Coding30
Intelligence20
Reasoning60
Math30
Knowledge40
General180
All shared boards360
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev1621Not measuredOnly GLM 5.3 measured
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 4 (Vals AI)38.9%Not measuredOnly GLM 5.3 measured
Vibe Code Bench (Vals AI)78.1%Not measuredOnly GLM 5.3 measured
Code migration (Vals AI)44.2%Not measuredOnly GLM 5.3 measured
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%Not measuredOnly GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)Not measured23.2%Only Llama 3.1 70B Instruct measured
SciCode59.0%Not measuredOnly GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured3.0%Only Llama 3.1 70B Instruct measured
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%9.0%GLM 5.3
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971260GLM 5.3
LMArena Text: software and IT14931260GLM 5.3
LMArena WebDev: React1641Not measuredOnly GLM 5.3 measured
LMArena WebDev: HTML1595Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
AA Intelligence Index44.86.6GLM 5.3
Epoch Capabilities Index156.0125.9GLM 5.3
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
Agentic work benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1254Not measuredOnly GLM 5.3 measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%Not measuredOnly GLM 5.3 measured
Tax Agent Bench (Vals AI)39.7%Not measuredOnly GLM 5.3 measured
Legal Research Bench (Vals AI)49.0%Not measuredOnly GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%Not measuredOnly GLM 5.3 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
τ²-Bench TelecomNot measured15.2%Only Llama 3.1 70B Instruct measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
BALROGNot measured27.9%Only Llama 3.1 70B Instruct measured
TheAgentCompanyNot measured6.9%Only Llama 3.1 70B Instruct measured
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)91.7%40.9%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%4.5%GLM 5.3
GPQA Diamond (Epoch AI run)90.9%44.2%GLM 5.3
Chess puzzles37.0%Not measuredOnly GLM 5.3 measured
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901241GLM 5.3
LMArena Text: expert15141209GLM 5.3
LMArena Text: hard prompts in English14961261GLM 5.3
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
AA Math IndexNot measured4.0Only Llama 3.1 70B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured4.0%Only Llama 3.1 70B Instruct measured
OTIS Mock AIME91.1%3.6%GLM 5.3
FrontierMath, tiers 1 to 368.8%Not measuredOnly GLM 5.3 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%Not measuredOnly GLM 5.3 measured
MATH level 5Not measured36.7%Only Llama 3.1 70B Instruct measured
LMArena Text: math14951252GLM 5.3
LMArena Text: mathematical work14971253GLM 5.3
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
MMLU Pro86.8%Not measuredOnly GLM 5.3 measured
LegalBench (Vals AI)84.8%Not measuredOnly GLM 5.3 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 5.3 measured
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
MMLU Pro (Artificial Analysis run)Not measured67.6%Only Llama 3.1 70B Instruct measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
MMLUNot measured80.1%Only Llama 3.1 70B Instruct measured
LMArena Text: science14981276GLM 5.3
LMArena Text: medicine and healthcare14811257GLM 5.3
LMArena Text: legal and government14731283GLM 5.3
LMArena Text: business and finance14591235GLM 5.3
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
Design Arena: websites1295Not measuredOnly GLM 5.3 measured
Design Arena: UI components1314Not measuredOnly GLM 5.3 measured
Design Arena: game dev1311Not measuredOnly GLM 5.3 measured
Design Arena: data viz1259Not measuredOnly GLM 5.3 measured
Design Arena: 3D1332Not measuredOnly GLM 5.3 measured
Design Arena: all code categories1304Not measuredOnly GLM 5.3 measured
Design Arena: SVG1293Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1213Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for GLM 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B InstructLeader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
MedScribe (Vals AI)88.8%Not measuredOnly GLM 5.3 measured
IFBenchNot measured34.4%Only Llama 3.1 70B Instruct measured
Long context reasoning (AA-LCR)79.7%Not measuredOnly GLM 5.3 measured
DTBench87.7%60.0%GLM 5.3
LMCA55.5%14.8%GLM 5.3
LMArena Text14741261GLM 5.3
LMArena Text: instruction following14791231GLM 5.3
LMArena Text: creative writing14581232GLM 5.3
LMArena Text: English14821294GLM 5.3
LMArena Text: non-English14591219GLM 5.3
LMArena Text: Chinese15321215GLM 5.3
LMArena Text: German14881222GLM 5.3
LMArena Text: Japanese14601132GLM 5.3
LMArena Text: Korean14601140GLM 5.3
LMArena Text: French15031262GLM 5.3
LMArena Text: Spanish14621253GLM 5.3
LMArena Text: Russian14691234GLM 5.3
LMArena Text: Polish1466Not measuredOnly GLM 5.3 measured
LMArena Text: multi-turn14721256GLM 5.3
LMArena Text: longer queries14831241GLM 5.3
LMArena Text: writing and language14691236GLM 5.3
LMArena Text: entertainment and media14471230GLM 5.3
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 5.3 and Llama 3.1 70B Instruct
BenchmarkGLM 5.3Llama 3.1 70B 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 5.3$0.0036Fits in one request
Llama 3.1 70B Instruct$0.0008Fits in one request

Llama 3.1 70B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request
Llama 3.1 70B Instruct$0.030Fits in one request

Llama 3.1 70B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request
Llama 3.1 70B Instruct$0.118Exceeds the context window

Llama 3.1 70B 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 5.3 and Llama 3.1 70B Instruct
SpecificationZ.ai GLM 5.3Meta Llama 3.1 70B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedAugust 18, 2026July 23, 2024
Context window1.05M131K
Input $/Mtok$1.40$0.56
Output $/Mtok$4.40$0.56
Coding index70.6Not benchmarked
Independent measurements behind it60
Cost per solved task$0.36Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Llama 3.1 70B Instruct for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Llama 3.1 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.1 70B Instruct for output tokens 7.9x cheaper ($0.56 vs $4.40 per million).

Which is cheaper, GLM 5.3 or Llama 3.1 70B Instruct?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Llama 3.1 70B Instruct costs $0.56 and $0.56. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, GLM 5.3 or Llama 3.1 70B Instruct?

GLM 5.3, with 1.05M tokens against 131K.

How many benchmarks is the GLM 5.3 vs Llama 3.1 70B Instruct comparison based on?

36 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Llama 3.1 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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