GLM 4.6 vs Llama 3.3 70B Instruct
GLM 4.6 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 18.5 to 7.7, and Llama 3.3 70B Instruct costs 3.1x less per output token ($0.72 vs $2.20 per million). Updated October 21, 2026; 41 benchmarks have a result for both.
GLM 4.6 or Llama 3.3 70B Instruct: which to pick
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 3.1x cheaper ($0.72 vs $2.20 per million) and work measured by IFBench, where it leads.
- Shared results
- 41
- GLM 4.6 only
- 28
- Llama 3.3 70B Instruct only
- 11
- 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
Only GLM 4.6 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6
GLM 4.6 leads, 18.5 against 7.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
Long documents
Listed context window
GLM 4.6
GLM 4.6 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
GLM 4.6 $0.0017, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Math, 53.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GLM 4.6 86.0% against Llama 3.3 70B Instruct 7.7%, 78.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Llama 3.3 70B Instruct $0.72 per million output tokens, 3.1x apart.
- Context window
- GLM 4.6 takes 205K 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.
| Category | GLM 4.6 | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 18 | 1 | ||
| All shared boards | 40 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | Not measured | Only GLM 4.6 measured |
| AA Coding Index | 45.8 | 11.9 | GLM 4.6 |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Vibe Code Bench (Vals AI) | 3.1% | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | 28.8% | GLM 4.6 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 3.0% | GLM 4.6 |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | 1268 | GLM 4.6 |
| LMArena Text: software and IT | 1455 | 1273 | GLM 4.6 |
| LMArena WebDev: HTML | 1350 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 7.7 | GLM 4.6 |
| Epoch Capabilities Index | Not measured | 127.3 | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| τ²-Bench Telecom | 76.9% | 26.6% | GLM 4.6 |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | 31.9% | GLM 4.6 |
| BrowseComp-Plus | 48.2% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 49.8% | GLM 4.6 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 3.6% | GLM 4.6 |
| GPQA Diamond (Epoch AI run) | Not measured | 47.4% | Only Llama 3.3 70B Instruct measured |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1440 | 1257 | GLM 4.6 |
| LMArena Text: expert | 1432 | 1225 | GLM 4.6 |
| LMArena Text: hard prompts in English | 1445 | 1277 | GLM 4.6 |
| Kagi LLM Benchmark | 45.7% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | 7.7 | GLM 4.6 |
| AIME 2025 (Artificial Analysis run) | 86.0% | 7.7% | GLM 4.6 |
| OTIS Mock AIME | Not measured | 5.1% | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1432 | 1267 | GLM 4.6 |
| LMArena Text: mathematical work | 1447 | 1267 | GLM 4.6 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | 71.3% | GLM 4.6 |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1456 | 1284 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1275 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1284 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1243 | GLM 4.6 |
| 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
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1148 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1151 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1129 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1166 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1109 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | GLM 4.6 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 43.4% | 47.1% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 54.0% | 15.7% | GLM 4.6 |
| DTBench | Not measured | 59.5% | Only Llama 3.3 70B Instruct measured |
| LMCA | Not measured | 17.5% | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1440 | 1274 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1242 | GLM 4.6 |
| LMArena Text: creative writing | 1412 | 1250 | GLM 4.6 |
| LMArena Text: English | 1449 | 1303 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1236 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1217 | GLM 4.6 |
| LMArena Text: German | 1447 | 1251 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1150 | GLM 4.6 |
| LMArena Text: Korean | 1400 | 1142 | GLM 4.6 |
| LMArena Text: French | 1459 | 1282 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1269 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1252 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1259 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1280 | GLM 4.6 |
| LMArena Text: longer queries | 1422 | 1255 | GLM 4.6 |
| LMArena Text: writing and language | 1415 | 1255 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1241 | GLM 4.6 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | Llama 3.3 70B 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 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.3 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.
| Specification | Z.ai GLM 4.6 | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | December 6, 2024 |
| Context window | 205K | 131K |
| Input $/Mtok | $0.60 | $0.45 |
| Output $/Mtok | $2.20 | $0.72 |
| Coding index | 8.8 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Llama 3.3 70B Instruct for coding?
Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 3.1x cheaper ($0.72 vs $2.20 per million) and work measured by IFBench, where it leads.
Which is cheaper, GLM 4.6 or Llama 3.3 70B Instruct?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Llama 3.3 70B Instruct?
GLM 4.6, with 205K tokens against 131K.
How many benchmarks is the GLM 4.6 vs Llama 3.3 70B Instruct comparison based on?
41 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 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.