Model comparison
GLM-5.3-Flash vs Llama 4 Scout
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash'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-Flash scores higher in 10 categories and Llama 4 Scout in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 9.1.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 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 4 Scout | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.8 | 27.7 |
| Released | 2026-08-20 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.15 | $0.10 |
| Output $ / M tokens | $0.50 | $0.30 |
| 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 4 Scout: 20.2 (#339)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| SciCode | 51.6% | 17% |
| LMArena Coding | 1508 | 1286 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Llama 4 Scout: 24.6 (#119)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Llama 4 Scout: 9.1 (#345)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 65.8% | 0% |
| ARC-AGI-1 | 91% | 0.5% |
| CritPt | 15.4% | 0% |
| LMArena Hard Prompts | 1491 | 1266 |
| Epoch Capabilities Index | 151.88 | 129.64 |
| Kagi LLM Benchmark | — | 36.9% |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57.5 |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Llama 4 Scout: 19.6 (#286)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 7.8% |
| LMArena Math | 1500 | 1287 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Llama 4 Scout: 31.9 (#217)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 90.2% | 51.8% |
| LMArena Expert | 1513 | 1235 |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), Llama 4 Scout: 32.2 (#102)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1296 | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Llama 4 Scout: 41.0 (#212)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1462 | 1252 |
| LMArena Chinese | 1527 | 1255 |
| LMArena French | 1496 | 1282 |
| LMArena German | 1470 | 1272 |
| LMArena Japanese | 1429 | 1206 |
| LMArena Korean | 1446 | 1207 |
| LMArena Russian | 1469 | 1263 |
| LMArena Spanish | 1471 | 1278 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Llama 4 Scout: 65.8 (#217)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1478 | 1248 |
| IFEval | — | 81.8% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Llama 4 Scout: 27.5 (#294)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1482 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Llama 4 Scout: 37.0 (#261)
| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1471 | 1279 |
| LMArena Creative Writing | 1442 | 1249 |
| LMArena Multi-Turn | 1467 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is GLM-5.3-Flash better than Llama 4 Scout?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 1.6× 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 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.
Is GLM-5.3-Flash or Llama 4 Scout better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 20.2 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 4 Scout share?
25 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Llama 4 Scout has 43.