Model comparison
GLM-4.5V vs Llama 4 Maverick
GLM-4.5V is the stronger model overall, scoring 39.8 to 30.9 on the Noometry Index. Llama 4 Maverick costs 3.0× 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 8 categories and Llama 4 Maverick in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 10.1.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama 4 Maverick accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | Llama 4 Maverick | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.8 | 30.9 |
| Released | 2025-08-11 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $0.60 | $0.19 |
| Output $ / M tokens | $1.80 | $0.65 |
| Results tracked | 15 | 54 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Llama 4 Maverick: 26.6 (#324)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Coding | 1347 | 1302 |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| BigCodeBench Instruct | — | 49.7% |
| BigCodeBench Complete | — | 61.4% |
| ALE-Bench | — | 172.97 |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Llama 4 Maverick: 28.2 (#91)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.3% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Llama 4 Maverick: 10.1 (#342)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 55.9% |
| LMArena Hard Prompts | 1334 | 1281 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| EnigmaEval | — | 0.6% |
| DTBench | — | 61.9% |
| LMCA | — | 15.9% |
| Epoch Capabilities Index | — | 132.2 |
| ForecastBench | — | 57.5 |
Math GLM-4.5V leads
GLM-4.5V: 37.4 (#159), Llama 4 Maverick: 26.0 (#262)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Math | 1354 | 1299 |
| OTIS Mock AIME 2024-2025 | — | 20.6% |
| Omni-MATH | — | 42.2% |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), Llama 4 Maverick: 33.4 (#204)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Expert | 1353 | 1259 |
| GPQA Diamond | — | 67% |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |
Multimodal GLM-4.5V leads
GLM-4.5V: 34.3 (#92), Llama 4 Maverick: 31.6 (#105)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | 1154 | 1142 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), Llama 4 Maverick: 42.2 (#195)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1303 | 1269 |
| LMArena Chinese | 1337 | 1277 |
| LMArena Russian | 1298 | 1286 |
| LMArena Spanish | 1336 | 1293 |
| LMArena French | — | 1259 |
| LMArena German | — | 1291 |
| LMArena Japanese | — | 1207 |
| LMArena Korean | — | 1203 |
Instruction Following Llama 4 Maverick leads
GLM-4.5V: 69.2 (#175), Llama 4 Maverick: 71.7 (#146)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1311 | 1267 |
| IFEval | — | 90.8% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), Llama 4 Maverick: 31.4 (#279)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1304 | 1280 |
| Fiction.LiveBench | — | 46.2% |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Llama 4 Maverick: 38.8 (#252)
| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1333 | 1287 |
| LMArena Creative Writing | 1295 | 1267 |
| LMArena Multi-Turn | 1332 | 1289 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |
Frequently asked questions
Is GLM-4.5V better than Llama 4 Maverick?
GLM-4.5V is the stronger model overall, scoring 39.8 to 30.9 on the Noometry Index. Llama 4 Maverick costs 3.0× 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 Maverick?
Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Llama 4 Maverick better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 26.6 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Maverick does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and Llama 4 Maverick share?
15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 4 Maverick has 54.