Model comparison
GLM-4.7 vs Llama 4 Scout
GLM-4.7 is the stronger model overall, scoring 42.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 6.7× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7 leads 60.9 to 37.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-4.7 | Llama 4 Scout | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 42.0 | 27.7 |
| Released | 2025-12-22 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.60 | $0.10 |
| Output $ / M tokens | $2.20 | $0.30 |
| Results tracked | 36 | 43 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Llama 4 Scout: 20.2 (#339)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| SciCode | 45.1% | 17% |
| LMArena Coding | 1454 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1435 | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Llama 4 Scout: 24.6 (#119)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Llama 4 Scout: 9.1 (#345)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1443 | 1266 |
| Epoch Capabilities Index | 143.51 | 129.64 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| Chess Puzzles | 6% | — |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| ForecastBench | — | 57.5 |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Llama 4 Scout: 19.6 (#286)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 7.8% |
| LMArena Math | 1423 | 1287 |
| FrontierMath (Feb 2025 set) | 2.4% | 0% |
| ProofBench | 6% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Llama 4 Scout: 31.9 (#217)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 83.3% | 51.8% |
| Vectara Hallucination Rate | 11.7% | 7.7% |
| LMArena Expert | 1424 | 1235 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
GLM-4.7: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Llama 4 Scout: 41.0 (#212)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1417 | 1252 |
| LMArena Chinese | 1495 | 1255 |
| LMArena French | 1432 | 1282 |
| LMArena German | 1424 | 1272 |
| LMArena Japanese | 1439 | 1206 |
| LMArena Korean | 1399 | 1207 |
| LMArena Russian | 1423 | 1263 |
| LMArena Spanish | 1434 | 1278 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Llama 4 Scout: 65.8 (#217)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1411 | 1248 |
| IFEval | — | 81.8% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Llama 4 Scout: 27.5 (#294)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1432 | 1265 |
| Fiction.LiveBench | — | 36% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Llama 4 Scout: 37.0 (#261)
| Benchmark | GLM-4.7 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1435 | 1279 |
| LMArena Creative Writing | 1401 | 1249 |
| EQ-Bench Creative Writing | 1413 | 783 |
| LMArena Multi-Turn | 1446 | 1280 |
| WildBench | — | 78% |
Frequently asked questions
Is GLM-4.7 better than Llama 4 Scout?
GLM-4.7 is the stronger model overall, scoring 42.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 6.7× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 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-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Llama 4 Scout better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.7 and Llama 4 Scout share?
25 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama 4 Scout has 43.