GLM 4.6V vs Llama 3.2 1B Instruct
GLM 4.6V leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 11.2 to 4.8, and Llama 3.2 1B Instruct costs 4.5x less per output token ($0.201 vs $0.90 per million). Updated October 21, 2026; 31 benchmarks have a result for both.
GLM 4.6V or Llama 3.2 1B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for a larger context window (131K against 60K tokens).
- Shared results
- 31
- GLM 4.6V only
- 7
- Llama 3.2 1B Instruct only
- 6
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6V
GLM 4.6V leads, 11.2 against 4.8.
Same board, both measured
Long documents
Listed context window
GLM 4.6V
GLM 4.6V takes 131K tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
GLM 4.6V $0.0008, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- Math, 85.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GLM 4.6V 85.3% against Llama 3.2 1B Instruct 0.0%, 85.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Llama 3.2 1B Instruct $0.201 per million output tokens, 4.5x apart.
- Context window
- GLM 4.6V takes 131K 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 4.6V | Bars | Llama 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 31 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 41.1% | 1.9% | GLM 4.6V |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 0.0% | GLM 4.6V |
| LMArena Text: coding | 1389 | 1070 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1056 | GLM 4.6V |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 4.8 | GLM 4.6V |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | 0.0% | GLM 4.6V |
| BALROG | Not measured | 6.6% | Only Llama 3.2 1B 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
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 19.6% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 5.5% | GLM 4.6V |
| GPQA Diamond (Epoch AI run) | Not measured | 23.9% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts | 1367 | 1044 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1007 | GLM 4.6V |
| LMArena Text: hard prompts in English | 1391 | 1087 | GLM 4.6V |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | 0.0 | GLM 4.6V |
| AIME 2025 (Artificial Analysis run) | 85.3% | 0.0% | GLM 4.6V |
| OTIS Mock AIME | Not measured | 0.6% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: math | Not measured | 1086 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1098 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | 20.0% | GLM 4.6V |
| LMArena Text: science | 1389 | 1055 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1009 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1071 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1000 | GLM 4.6V |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GLM 4.6V | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| IFBench | 30.1% | 22.8% | GLM 4.6V |
| Long context reasoning (AA-LCR) | 48.7% | 6.7% | GLM 4.6V |
| LMArena Text | 1376 | 1055 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1031 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1033 | GLM 4.6V |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | 1117 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 973 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 959 | GLM 4.6V |
| LMArena Text: German | Not measured | 1014 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Russian | 1339 | 941 | GLM 4.6V |
| LMArena Text: multi-turn | 1359 | 1030 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1050 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1030 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1025 | GLM 4.6V |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | 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 4.6V | Meta Llama 3.2 1B Instruct |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | September 25, 2024 |
| Context window | 131K | 60K |
| Input $/Mtok | $0.30 | $0.027 |
| Output $/Mtok | $0.90 | $0.201 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Llama 3.2 1B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for a larger context window (131K against 60K tokens).
Which is cheaper, GLM 4.6V or Llama 3.2 1B Instruct?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6V or Llama 3.2 1B Instruct?
GLM 4.6V, with 131K tokens against 60K.
How many benchmarks is the GLM 4.6V vs Llama 3.2 1B Instruct comparison based on?
31 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 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.