# GLM-5.3-Flash vs Llama-3.3-70B-Instruct

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.3 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.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- 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 53.1 to 14.3.
- 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.

## Snapshot

| | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 49.6 | 30.3 |
| Rank | 45 | 283 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.10 |
| Output $/M | $0.50 | $0.32 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 51.6% | 26% |
| LMArena Coding | 1506 | 1268 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| 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: 34.3 (#41)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| 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: 31.7 (#69)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 15.4% | 0% |
| LMArena Hard Prompts | 1490 | 1257 |
| Epoch Capabilities Index | 151.9 | 127.32 |
| SimpleBench | — | 19.9% |
| 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: 53.1 (#38)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 5.1% |
| LMArena Math | 1499 | 1268 |
| 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: 58.4 (#33)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 90.2% | 47.4% |
| LMArena Expert | 1512 | 1225 |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Llama-3.3-70B-Instruct: —

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1462 | 1236 |
| LMArena Chinese | 1529 | 1217 |
| LMArena French | 1495 | 1280 |
| LMArena German | 1457 | 1251 |
| LMArena Japanese | 1420 | 1149 |
| LMArena Korean | 1443 | 1142 |
| LMArena Russian | 1472 | 1252 |
| LMArena Spanish | 1471 | 1269 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1480 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | 1256 |
| Fiction.LiveBench | — | 33.3% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | GLM-5.3-Flash | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1471 | 1274 |
| LMArena Creative Writing | 1440 | 1250 |
| LMArena Multi-Turn | 1471 | 1279 |
| LiveBench Language | — | 39.2% |

## FAQ

### Is GLM-5.3-Flash better than Llama-3.3-70B-Instruct?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.3 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: 53.7 versus 30.6 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 36 scored results on Noometry and Llama-3.3-70B-Instruct has 43.