Model comparison
GLM-4.5V vs Llama 4 Scout
GLM-4.5V is the stronger model overall, scoring 39.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 6.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. GLM-4.5V scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.5V leads 38.9 to 19.9.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 36.9% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama 4 Scout accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | Llama 4 Scout | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.2 | 27.4 |
| Released | 2025-08-11 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $0.60 | $0.10 |
| Output $ / M tokens | $1.80 | $0.30 |
| Results tracked | 15 | 43 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 38.9 (#152), Llama 4 Scout: 19.9 (#334)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1347 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Llama 4 Scout: 24.4 (#109)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning GLM-4.5V leads
GLM-4.5V: 26.5 (#118), Llama 4 Scout: 9.2 (#339)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 36.9% |
| LMArena Hard Prompts | 1335 | 1266 |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math GLM-4.5V leads
GLM-4.5V: 34.7 (#157), Llama 4 Scout: 18.4 (#280)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1353 | 1286 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge GLM-4.5V leads
GLM-4.5V: 36.8 (#155), Llama 4 Scout: 31.8 (#208)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1353 | 1236 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal GLM-4.5V leads
GLM-4.5V: 33.6 (#86), Llama 4 Scout: 31.6 (#98)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1154 | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.9 (#172), Llama 4 Scout: 41.3 (#207)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1304 | 1253 |
| LMArena Chinese | 1338 | 1256 |
| LMArena Russian | 1299 | 1263 |
| LMArena Spanish | 1335 | 1277 |
| LMArena French | — | 1283 |
| LMArena German | — | 1274 |
| LMArena Japanese | — | 1209 |
| LMArena Korean | — | 1207 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.4 (#170), Llama 4 Scout: 66.0 (#212)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1311 | 1248 |
| IFEval | — | 81.8% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#166), Llama 4 Scout: 27.5 (#289)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1305 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.7 (#162), Llama 4 Scout: 37.1 (#256)
| Benchmark | GLM-4.5V | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1333 | 1279 |
| LMArena Creative Writing | 1295 | 1249 |
| LMArena Multi-Turn | 1333 | 1281 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is GLM-4.5V better than Llama 4 Scout?
GLM-4.5V is the stronger model overall, scoring 39.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 6.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Llama 4 Scout better for coding?
GLM-4.5V scores higher on coding benchmarks: 38.9 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Scout does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and Llama 4 Scout share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 4 Scout has 43.