Model comparison
GLM-5.3 vs Llama-3.3-70B-Instruct
GLM-5.3 is the stronger model overall, scoring 54.6 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 14× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.1 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.3 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 54.6 | 30.3 |
| Released | 2026-08-14 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.40 | $0.10 |
| Output $ / M tokens | $4.40 | $0.32 |
| Results tracked | 41 | 43 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.0 (#12), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 59% | 26% |
| WeirdML | 75.4% | 14.4% |
| LMArena Coding | 1499 | 1268 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| LMArena WebDev | 1623 | — |
| FrontierSWE | 30.2% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.3 (#34), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 45.2 (#42), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 19.1% | 0% |
| LMArena Hard Prompts | 1491 | 1257 |
| DTBench | 87.7% | 59.5% |
| LMCA | 55.5% | 17.5% |
| Epoch Capabilities Index | 155.56 | 127.32 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 74.2% | — |
| Chess Puzzles | 21% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 33% | — |
| LiveBench Data Analysis | — | 49.5% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GLM-5.3 leads
GLM-5.3: 62.1 (#27), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 5.1% |
| LMArena Math | 1493 | 1268 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#34), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 90.9% | 47.4% |
| LMArena Expert | 1512 | 1225 |
| SimpleQA Verified | 41% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.9 (#27), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1457 | 1236 |
| LMArena Chinese | 1523 | 1217 |
| LMArena French | 1499 | 1280 |
| LMArena German | 1505 | 1251 |
| LMArena Japanese | 1438 | 1149 |
| LMArena Korean | 1478 | 1142 |
| LMArena Russian | 1460 | 1252 |
| LMArena Spanish | 1444 | 1269 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.8 (#15), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1481 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GLM-5.3 leads
GLM-5.3: 45.5 (#32), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1485 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.9 (#6), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | GLM-5.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1473 | 1274 |
| LMArena Creative Writing | 1459 | 1250 |
| LMArena Multi-Turn | 1473 | 1279 |
| EQ-Bench Creative Writing | 2075 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GLM-5.3 better than Llama-3.3-70B-Instruct?
GLM-5.3 is the stronger model overall, scoring 54.6 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 14× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Llama-3.3-70B-Instruct better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3 and Llama-3.3-70B-Instruct share?
25 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and Llama-3.3-70B-Instruct has 43.