GLM 4.5 Air vs Llama 3.3 70B Instruct
GLM 4.5 Air leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 11.1 to 7.7, and Llama 3.3 70B Instruct costs 1.5x less per output token ($0.72 vs $1.10 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
GLM 4.5 Air or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for work measured by IFBench, where it leads.
- Shared results
- 39
- GLM 4.5 Air only
- 10
- Llama 3.3 70B Instruct only
- 13
- 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 4.5 Air
GLM 4.5 Air leads, 11.1 against 7.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.5 Air has a Design Arena: websites result, 1146.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5 Air
GLM 4.5 Air $0.0008, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 Air not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Math, 47.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GLM 4.5 Air 80.7% against Llama 3.3 70B Instruct 7.7%, 73.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 Air $1.10 against Llama 3.3 70B Instruct $0.72 per million output tokens, 1.5x apart.
- Context window
- Both take 131K tokens.
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 4.5 Air | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 18 | 1 | ||
| All shared boards | 38 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 11.9 | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 68.4% | 28.8% | GLM 4.5 Air |
| Terminal-Bench Hard (Artificial Analysis run) | 20.5% | 3.0% | GLM 4.5 Air |
| GSO-Bench | 2.9% | Not measured | Only GLM 4.5 Air measured |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1397 | 1268 | GLM 4.5 Air |
| LMArena Text: software and IT | 1401 | 1273 | GLM 4.5 Air |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 7.7 | GLM 4.5 Air |
| 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 index10
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 46.5% | 26.6% | GLM 4.5 Air |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 31.9% | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 73.3% | 49.8% | GLM 4.5 Air |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 3.6% | GLM 4.5 Air |
| 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 | 1379 | 1257 | GLM 4.5 Air |
| LMArena Text: expert | 1370 | 1225 | GLM 4.5 Air |
| LMArena Text: hard prompts in English | 1388 | 1277 | GLM 4.5 Air |
| Kagi LLM Benchmark | 43.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 80.7 | 7.7 | GLM 4.5 Air |
| AIME 2025 (Artificial Analysis run) | 80.7% | 7.7% | GLM 4.5 Air |
| 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 | 1396 | 1267 | GLM 4.5 Air |
| LMArena Text: mathematical work | 1396 | 1267 | GLM 4.5 Air |
| MathArena | 12.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.5% | 71.3% | GLM 4.5 Air |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1407 | 1284 | GLM 4.5 Air |
| LMArena Text: medicine and healthcare | 1402 | 1275 | GLM 4.5 Air |
| LMArena Text: legal and government | 1382 | 1284 | GLM 4.5 Air |
| LMArena Text: business and finance | 1371 | 1243 | GLM 4.5 Air |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1146 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: UI components | 1121 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: game dev | 1093 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: data viz | 1186 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: 3D | 1122 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: all code categories | 1137 | Not measured | Only GLM 4.5 Air measured |
| Design Arena: SVG | 1070 | Not measured | Only GLM 4.5 Air 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 4.5 Air | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 37.6% | 47.1% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 46.7% | 15.7% | GLM 4.5 Air |
| 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 | 1384 | 1274 | GLM 4.5 Air |
| LMArena Text: instruction following | 1354 | 1242 | GLM 4.5 Air |
| LMArena Text: creative writing | 1343 | 1250 | GLM 4.5 Air |
| LMArena Text: English | 1397 | 1303 | GLM 4.5 Air |
| LMArena Text: non-English | 1366 | 1236 | GLM 4.5 Air |
| LMArena Text: Chinese | 1426 | 1217 | GLM 4.5 Air |
| LMArena Text: German | 1377 | 1251 | GLM 4.5 Air |
| LMArena Text: Japanese | 1348 | 1150 | GLM 4.5 Air |
| LMArena Text: Korean | 1308 | 1142 | GLM 4.5 Air |
| LMArena Text: French | 1400 | 1282 | GLM 4.5 Air |
| LMArena Text: Spanish | 1385 | 1269 | GLM 4.5 Air |
| LMArena Text: Russian | 1373 | 1252 | GLM 4.5 Air |
| LMArena Text: Polish | 1368 | 1259 | GLM 4.5 Air |
| LMArena Text: multi-turn | 1371 | 1280 | GLM 4.5 Air |
| LMArena Text: longer queries | 1366 | 1255 | GLM 4.5 Air |
| LMArena Text: writing and language | 1354 | 1255 | GLM 4.5 Air |
| LMArena Text: entertainment and media | 1339 | 1241 | GLM 4.5 Air |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 Air | 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
GLM 4.5 Air costs less
Repository review
50K input + 3K output tokens
GLM 4.5 Air costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 Air 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 4.5 Air | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Z.ai | Meta Llama |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | December 6, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.20 | $0.45 |
| Output $/Mtok | $1.10 | $0.72 |
| 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 4.5 Air better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for work measured by IFBench, where it leads.
Which is cheaper, GLM 4.5 Air or Llama 3.3 70B Instruct?
GLM 4.5 Air costs $0.20 per million input tokens and $1.10 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 4.5 Air or Llama 3.3 70B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.5 Air vs Llama 3.3 70B Instruct comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 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.