GLM 5.3 Flash vs Llama 3.2 1B Instruct
GLM 5.3 Flash leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 41.8 to 4.8, and Llama 3.2 1B Instruct costs 2.5x less per output token ($0.201 vs $0.50 per million). Updated October 21, 2026; 29 benchmarks have a result for both.
GLM 5.3 Flash or Llama 3.2 1B Instruct: which to pick
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.2 1B Instruct for output tokens 2.5x cheaper ($0.201 vs $0.50 per million).
- Shared results
- 29
- GLM 5.3 Flash only
- 73
- Llama 3.2 1B Instruct only
- 8
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5.3 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 4.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
GLM 5.3 Flash $0.0004, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- GLM 5.3 Flash 57.7 from 6 benchmarks, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- Math, 76.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GLM 5.3 Flash 93.9% against Llama 3.2 1B Instruct 0.6%, 93.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Llama 3.2 1B Instruct $0.201 per million output tokens, 2.5x apart.
- Context window
- GLM 5.3 Flash takes 1.05M tokens, Llama 3.2 1B Instruct 60K.
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.
| Category | GLM 5.3 Flash | Bars | Llama 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 29 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1607 | Not measured | Only GLM 5.3 Flash measured |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 30.8% | Not measured | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | 20.5% | Not measured | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 1.9% | Only Llama 3.2 1B Instruct measured |
| SciCode | 51.6% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| ALE-Bench | 303.6 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: coding | 1507 | 1070 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1056 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | 1577 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: from an image | 1578 | Not measured | Only 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 index10
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 4.8 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | Not measured | Only GLM 5.3 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 70.1% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 28.4% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | 31.6% | Not measured | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| APEX-Agents | 52.8% | Not measured | Only GLM 5.3 Flash measured |
| BALROG | Not measured | 6.6% | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | Not measured | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 19.6% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 5.5% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | 23.9% | GLM 5.3 Flash |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | 1044 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1007 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1087 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | 1318 | Not measured | Only 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
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Llama 3.2 1B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| OTIS Mock AIME | 93.9% | 0.6% | GLM 5.3 Flash |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1503 | 1086 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1098 | GLM 5.3 Flash |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | Not measured | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | 83.9% | Not measured | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | 75.6% | Not measured | Only GLM 5.3 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 20.0% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: science | 1484 | 1055 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1009 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1071 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1000 | GLM 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
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: UI components | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: game dev | 1278 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: data viz | 1261 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: 3D | 1316 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: all code categories | 1282 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: SVG | 1284 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1268 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | 88.9% | Not measured | Only GLM 5.3 Flash measured |
| IFBench | Not measured | 22.8% | Only Llama 3.2 1B Instruct measured |
| Long context reasoning (AA-LCR) | 80.0% | 6.7% | GLM 5.3 Flash |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1471 | 1055 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1031 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1033 | GLM 5.3 Flash |
| LMArena Vision | 1294 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1474 | 1117 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 973 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 959 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1014 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: French | 1491 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Spanish | 1475 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Russian | 1465 | 941 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: multi-turn | 1469 | 1030 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1050 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1030 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1025 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | 1363 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | 1299 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | 1279 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | 1270 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| 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
Llama 3.2 1B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 1B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 1B 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.
| Specification | Z.ai GLM 5.3 Flash | Meta Llama 3.2 1B Instruct |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | August 26, 2026 | September 25, 2024 |
| Context window | 1.05M | 60K |
| Input $/Mtok | $0.15 | $0.027 |
| Output $/Mtok | $0.50 | $0.201 |
| Coding index | 57.7 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Llama 3.2 1B Instruct for coding?
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 2.5x cheaper ($0.201 vs $0.50 per million).
Which is cheaper, GLM 5.3 Flash or Llama 3.2 1B Instruct?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, GLM 5.3 Flash or Llama 3.2 1B Instruct?
GLM 5.3 Flash, with 1.05M tokens against 60K.
How many benchmarks is the GLM 5.3 Flash vs Llama 3.2 1B Instruct comparison based on?
29 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 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.