Model comparison
GLM-5.3-Flash vs Llama 3.1-70B
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 29.4 on the Noometry Index.
Last verified . 20 shared benchmarks.
MetaSummary
- They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.1 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 3.6% for Llama 3.1-70B.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.3-Flash | Llama 3.1-70B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 49.6 | 29.4 |
| Released | 2026-08-20 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.15 | $0.40 |
| Output $ / M tokens | $0.50 | $0.40 |
| Results tracked | 36 | 35 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), Llama 3.1-70B: 29.8 (#292)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1506 | 1260 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.3 (#41), Llama 3.1-70B: 24.9 (#104)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 31.7 (#69), Llama 3.1-70B: 20.8 (#215)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1241 |
| Epoch Capabilities Index | 151.9 | 125.91 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#38), Llama 3.1-70B: 12.7 (#298)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 3.6% |
| LMArena Math | 1499 | 1252 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#33), Llama 3.1-70B: 24.1 (#264)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 90.2% | 44.2% |
| LMArena Expert | 1512 | 1209 |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
GLM-5.3-Flash: 42.6 (#25), Llama 3.1-70B: —
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1304 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), Llama 3.1-70B: 38.9 (#220)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1462 | 1219 |
| LMArena Chinese | 1529 | 1215 |
| LMArena French | 1495 | 1260 |
| LMArena German | 1457 | 1222 |
| LMArena Japanese | 1420 | 1132 |
| LMArena Korean | 1443 | 1140 |
| LMArena Russian | 1472 | 1234 |
| LMArena Spanish | 1471 | 1253 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), Llama 3.1-70B: 65.5 (#218)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1231 |
| IFEval | — | 82.1% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.3 (#41), Llama 3.1-70B: 37.6 (#208)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1479 | 1241 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.4 (#49), Llama 3.1-70B: 35.6 (#262)
| Benchmark | GLM-5.3-Flash | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1471 | 1261 |
| LMArena Creative Writing | 1440 | 1232 |
| LMArena Multi-Turn | 1471 | 1256 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GLM-5.3-Flash better than Llama 3.1-70B?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 29.4 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Llama 3.1-70B?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is GLM-5.3-Flash or Llama 3.1-70B better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 29.8 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 3.1-70B share?
20 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Llama 3.1-70B has 35.