Model comparison
GLM-5.2 vs Llama-3.3-70B-Instruct
GLM-5.2 is the stronger model overall, scoring 51.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 14× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. GLM-5.2 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-5.2 leads 55.7 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 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-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.2 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.1 | 30.6 |
| Released | 2026-06-13 | 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 | 51 | 43 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 50.5% | 26% |
| WeirdML | 70.1% | 14.4% |
| LMArena Coding | 1485 | 1268 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| BALROG | — | 23% |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 58.8% | 19.9% |
| CritPt | 20.9% | 0% |
| LMArena Hard Prompts | 1480 | 1257 |
| DTBench | 93.6% | 59.5% |
| LMCA | 45.8% | 17.5% |
| Epoch Capabilities Index | 151.78 | 127.33 |
| ARC-AGI-2 | 22.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | — | 49.5% |
| Surface Evolver Bench | 55.6% | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 5.1% |
| LMArena Math | 1482 | 1267 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 91.9% | 47.4% |
| LMArena Expert | 1486 | 1225 |
| SimpleQA Verified | 34.2% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1459 | 1236 |
| LMArena Chinese | 1519 | 1217 |
| LMArena French | 1479 | 1281 |
| LMArena German | 1468 | 1251 |
| LMArena Japanese | 1451 | 1150 |
| LMArena Korean | 1445 | 1143 |
| LMArena Russian | 1466 | 1252 |
| LMArena Spanish | 1477 | 1270 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1465 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GLM-5.2 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1470 | 1274 |
| LMArena Creative Writing | 1462 | 1250 |
| LMArena Multi-Turn | 1469 | 1280 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GLM-5.2 better than Llama-3.3-70B-Instruct?
GLM-5.2 is the stronger model overall, scoring 51.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 14× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 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-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Llama-3.3-70B-Instruct better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.2 and Llama-3.3-70B-Instruct share?
26 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Llama-3.3-70B-Instruct has 43.