Model comparison
GLM-5.3-Flash vs Llama-3.3-70B-Instruct
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.5× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. GLM-5.3-Flash 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-Flash leads 53.3 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash 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 $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.3-Flash | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.8 | 30.6 |
| Released | 2026-08-20 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.15 | $0.10 |
| Output $ / M tokens | $0.50 | $0.32 |
| Results tracked | 40 | 43 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 51.6% | 26% |
| LMArena Coding | 1508 | 1268 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 15.4% | 0% |
| LMArena Hard Prompts | 1491 | 1257 |
| Epoch Capabilities Index | 151.88 | 127.33 |
| ARC-AGI-2 | 65.8% | — |
| SimpleBench | — | 19.9% |
| ARC-AGI-1 | 91% | — |
| Chess Puzzles | 14% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 5.1% |
| LMArena Math | 1500 | 1267 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 90.2% | 47.4% |
| LMArena Expert | 1513 | 1225 |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Llama-3.3-70B-Instruct: —
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1462 | 1236 |
| LMArena Chinese | 1527 | 1217 |
| LMArena French | 1496 | 1281 |
| LMArena German | 1470 | 1251 |
| LMArena Japanese | 1429 | 1150 |
| LMArena Korean | 1446 | 1143 |
| LMArena Russian | 1469 | 1252 |
| LMArena Spanish | 1471 | 1270 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1478 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1482 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1471 | 1274 |
| LMArena Creative Writing | 1442 | 1250 |
| LMArena Multi-Turn | 1467 | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GLM-5.3-Flash better than Llama-3.3-70B-Instruct?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.5× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash 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-Flash lists at $0.15 and $0.50.
Is GLM-5.3-Flash or Llama-3.3-70B-Instruct better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3-Flash and Llama-3.3-70B-Instruct share?
22 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Llama-3.3-70B-Instruct has 43.