Enter opens the highlighted result. Esc closes.

GLM 5.3 Flash vs Llama 3.3 70B Instruct

GLM 5.3 Flash leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 41.8 to 7.7, and GLM 5.3 Flash costs 1.4x less per output token ($0.50 vs $0.72 per million). Updated October 21, 2026; 36 benchmarks have a result for both.

Replace GLM 5.3 Flash Replace Llama 3.3 70B Instruct
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
36
Llama 3.3 70B InstructMeta Llama
Coding
No data
Overall
26.0
Output $/M
$0.72
0
vs boards won of 36 shared

GLM 5.3 Flash or Llama 3.3 70B Instruct: which to pick

Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved taskModel BLlama 3.3 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
36
GLM 5.3 Flash only
66
Llama 3.3 70B Instruct only
16
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 Flash 57.7 from 6 benchmarks, Llama 3.3 70B Instruct not benchmarked.
Widest category
Math, 55.4 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GLM 5.3 Flash 93.9% against Llama 3.3 70B Instruct 5.1%, 88.8 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against Llama 3.3 70B Instruct $0.72 per million output tokens, 1.4x apart.
Context window
GLM 5.3 Flash takes 1.05M tokens, Llama 3.3 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 5.3 FlashBarsLlama 3.3 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 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev1607Not measuredOnly GLM 5.3 Flash measured
AA Coding Index71.511.9GLM 5.3 Flash
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 Flash measured
SWE-bench Verified (Vals AI)92.0%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 4 (Vals AI)37.4%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)30.8%Not measuredOnly GLM 5.3 Flash measured
Code migration (Vals AI)20.5%Not measuredOnly GLM 5.3 Flash measured
ProgramBench (Vals AI)0.0%Not measuredOnly GLM 5.3 Flash measured
IOI (Vals AI)52.5%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)62.9%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%Not measuredOnly GLM 5.3 Flash measured
CyberBench (Vals AI)67.9%Not measuredOnly GLM 5.3 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured28.8%Only Llama 3.3 70B Instruct measured
SciCode51.6%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)Not measured3.0%Only Llama 3.3 70B Instruct measured
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
WeirdMLNot measured14.4%Only Llama 3.3 70B Instruct measured
ALE-Bench303.6Not measuredOnly GLM 5.3 Flash measured
LMArena Text: coding15071268GLM 5.3 Flash
LMArena Text: software and IT15001273GLM 5.3 Flash
LMArena WebDev: React1624Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev: HTML1577Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev: from an image1578Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
AA Intelligence Index41.87.7GLM 5.3 Flash
Epoch Capabilities Index153.1127.3GLM 5.3 Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
Agentic work benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
AA Agentic Index50.9Not measuredOnly GLM 5.3 Flash measured
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly GLM 5.3 Flash measured
Finance Agent (Vals AI)57.9%Not measuredOnly GLM 5.3 Flash measured
Tax Agent Bench (Vals AI)31.6%Not measuredOnly GLM 5.3 Flash measured
Legal Research Bench (Vals AI)45.2%Not measuredOnly GLM 5.3 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)10.4%Not measuredOnly GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)55.9%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench-Science (Vals AI)2.9%Not measuredOnly GLM 5.3 Flash measured
τ²-Bench TelecomNot measured26.6%Only Llama 3.3 70B Instruct measured
APEX-Agents52.8%Not measuredOnly GLM 5.3 Flash measured
BALROGNot measured23.0%Only Llama 3.3 70B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)Not measured31.9%Only Llama 3.3 70B Instruct measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
GPQA Diamond86.4%Not measuredOnly GLM 5.3 Flash measured
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MMMU Pro (Vals AI)86.0%Not measuredOnly GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.2%49.8%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)39.9%3.6%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%47.4%GLM 5.3 Flash
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
SimpleBenchNot measured19.9%Only Llama 3.3 70B Instruct measured
Chess puzzles21.0%Not measuredOnly GLM 5.3 Flash measured
Mystery game puzzles8.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts14901257GLM 5.3 Flash
LMArena Text: expert15111225GLM 5.3 Flash
LMArena Text: hard prompts in English14881277GLM 5.3 Flash
LMArena Vision: diagrams1317Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: homework1318Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
AA Math IndexNot measured7.7Only Llama 3.3 70B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured7.7%Only Llama 3.3 70B Instruct measured
OTIS Mock AIME93.9%5.1%GLM 5.3 Flash
FrontierMath, tiers 1 to 355.8%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tier 417.1%Not measuredOnly GLM 5.3 Flash measured
ProofBench21.0%Not measuredOnly GLM 5.3 Flash measured
MATH level 5Not measured41.6%Only Llama 3.3 70B Instruct measured
LMArena Text: math15031267GLM 5.3 Flash
LMArena Text: mathematical work14971267GLM 5.3 Flash
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 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
MMLU Pro86.1%Not measuredOnly GLM 5.3 Flash measured
LegalBench (Vals AI)83.9%Not measuredOnly GLM 5.3 Flash measured
TaxEval (Vals AI)75.6%Not measuredOnly GLM 5.3 Flash measured
MMLU Pro (Artificial Analysis run)Not measured71.3%Only Llama 3.3 70B Instruct measured
MMLUNot measured86.3%Only Llama 3.3 70B Instruct measured
LMArena Text: science14841284GLM 5.3 Flash
LMArena Text: medicine and healthcare14751275GLM 5.3 Flash
LMArena Text: legal and government14731284GLM 5.3 Flash
LMArena Text: business and finance14601243GLM 5.3 Flash
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
Design Arena: websites1272Not measuredOnly GLM 5.3 Flash measured
Design Arena: UI components1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: game dev1278Not measuredOnly GLM 5.3 Flash measured
Design Arena: data viz1261Not measuredOnly GLM 5.3 Flash measured
Design Arena: 3D1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: all code categories1282Not measuredOnly GLM 5.3 Flash measured
Design Arena: SVG1284Not measuredOnly GLM 5.3 Flash measured
Design Arena: ASCII art1268Not measuredOnly GLM 5.3 Flash measured
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for GLM 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 70B InstructLeader
Vals Index50.9%Not measuredOnly GLM 5.3 Flash measured
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
MedScribe (Vals AI)88.9%Not measuredOnly GLM 5.3 Flash measured
IFBenchNot measured47.1%Only Llama 3.3 70B Instruct measured
Long context reasoning (AA-LCR)80.0%15.7%GLM 5.3 Flash
DTBenchNot measured59.5%Only Llama 3.3 70B Instruct measured
LMCANot measured17.5%Only Llama 3.3 70B Instruct measured
Fiction.LiveBenchNot measured33.3%Only Llama 3.3 70B Instruct measured
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
LMArena Text14711274GLM 5.3 Flash
LMArena Text: instruction following14781242GLM 5.3 Flash
LMArena Text: creative writing14411250GLM 5.3 Flash
LMArena Vision1294Not measuredOnly GLM 5.3 Flash measured
LMArena Text: English14741303GLM 5.3 Flash
LMArena Text: non-English14621236GLM 5.3 Flash
LMArena Text: Chinese15311217GLM 5.3 Flash
LMArena Text: German14621251GLM 5.3 Flash
LMArena Text: Japanese14441150GLM 5.3 Flash
LMArena Text: Korean14361142GLM 5.3 Flash
LMArena Text: French14911282GLM 5.3 Flash
LMArena Text: Spanish14751269GLM 5.3 Flash
LMArena Text: Russian14651252GLM 5.3 Flash
LMArena Text: Polish14501259GLM 5.3 Flash
LMArena Text: multi-turn14691280GLM 5.3 Flash
LMArena Text: longer queries14811255GLM 5.3 Flash
LMArena Text: writing and language14551255GLM 5.3 Flash
LMArena Text: entertainment and media14401241GLM 5.3 Flash
LMArena Vision: English1295Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text1299Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing1270Not measuredOnly GLM 5.3 Flash 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 5.3 Flash and Llama 3.3 70B Instruct
BenchmarkGLM 5.3 FlashLlama 3.3 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 Flash$0.0004Fits in one request
Llama 3.3 70B Instruct$0.0008Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
Llama 3.3 70B Instruct$0.025Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
Llama 3.3 70B Instruct$0.097Exceeds the context window

GLM 5.3 Flash 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 Flash and Llama 3.3 70B Instruct
SpecificationZ.ai GLM 5.3 FlashMeta Llama 3.3 70B Instruct
MakerZ.aiMeta Llama
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026December 6, 2024
Context window1.05M131K
Input $/Mtok$0.15$0.45
Output $/Mtok$0.50$0.72
Coding index57.7Not benchmarked
Independent measurements behind it60
Cost per solved task$0.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than Llama 3.3 70B Instruct for coding?

Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 5.3 Flash or Llama 3.3 70B Instruct?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for GLM 5.3 Flash only.

Which has the bigger context window, GLM 5.3 Flash or Llama 3.3 70B Instruct?

GLM 5.3 Flash, with 1.05M tokens against 131K.

How many benchmarks is the GLM 5.3 Flash vs Llama 3.3 70B Instruct comparison based on?

36 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next