Model comparison
GLM-4.6V vs Llama 4 Scout
GLM-4.6V is the stronger model overall, scoring 41.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
MetaSummary
- They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.6V leads 40.9 to 20.2.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
Side by side
| GLM-4.6V | Llama 4 Scout | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 27.7 |
| Released | 2025-12-08 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 33K | 4K |
| Input $ / M tokens | $0.30 | $0.10 |
| Output $ / M tokens | $0.90 | $0.30 |
| Results tracked | 12 | 43 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Llama 4 Scout: 20.2 (#339)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1390 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Llama 4 Scout: 9.1 (#345)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1266 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math Not comparable
GLM-4.6V: —, Llama 4 Scout: 19.6 (#286)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| LMArena Math | — | 1287 |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Llama 4 Scout: 31.9 (#217)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1371 | 1235 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal GLM-4.6V leads
GLM-4.6V: 34.8 (#90), Llama 4 Scout: 32.2 (#102)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1164 | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Llama 4 Scout: 41.0 (#212)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1359 | 1252 |
| LMArena Chinese | 1425 | 1255 |
| LMArena Russian | 1340 | 1263 |
| LMArena French | — | 1282 |
| LMArena German | — | 1272 |
| LMArena Japanese | — | 1206 |
| LMArena Korean | — | 1207 |
| LMArena Spanish | — | 1278 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Llama 4 Scout: 65.8 (#217)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1352 | 1248 |
| IFEval | — | 81.8% |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#143), Llama 4 Scout: 27.5 (#294)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1358 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Llama 4 Scout: 37.0 (#261)
| Benchmark | GLM-4.6V | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1377 | 1279 |
| LMArena Creative Writing | 1347 | 1249 |
| LMArena Multi-Turn | 1360 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is GLM-4.6V better than Llama 4 Scout?
GLM-4.6V is the stronger model overall, scoring 41.3 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6V 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-4.6V lists at $0.30 and $0.90.
Is GLM-4.6V or Llama 4 Scout better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do GLM-4.6V and Llama 4 Scout share?
12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 4 Scout has 43.