Model comparison
GLM-4.5V vs Llama 3.1-8B
GLM-4.5V is the stronger model overall, scoring 39.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 16× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
MetaSummary
- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3.1-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.5V leads 37.5 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama 3.1-8B accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | Llama 3.1-8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.8 | 23.0 |
| Released | 2025-08-11 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $0.60 | $0.05 |
| Output $ / M tokens | $1.80 | $0.08 |
| Results tracked | 15 | 43 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Coding | 1347 | 1195 |
| SciCode | — | 13.2% |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Llama 3.1-8B: 22.5 (#131)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Hard Prompts | 1334 | 1175 |
| Kagi LLM Benchmark | 59.8% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| Epoch Capabilities Index | — | 116.57 |
| PIQA | — | 81.2% |
Math GLM-4.5V leads
GLM-4.5V: 37.4 (#159), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Math | 1354 | 1179 |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Expert | 1353 | 1144 |
| GPQA Diamond | — | 27% |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GLM-4.5V: 34.3 (#92), Llama 3.1-8B: —
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1303 | 1148 |
| LMArena Chinese | 1337 | 1151 |
| LMArena Russian | 1298 | 1158 |
| LMArena Spanish | 1336 | 1169 |
| LMArena French | — | 1177 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.2 (#175), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1311 | 1159 |
| IFEval | — | 74.3% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1304 | 1182 |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1333 | 1187 |
| LMArena Creative Writing | 1295 | 1154 |
| LMArena Multi-Turn | 1332 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GLM-4.5V better than Llama 3.1-8B?
GLM-4.5V is the stronger model overall, scoring 39.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 16× 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 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Llama 3.1-8B better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Llama 3.1-8B does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and Llama 3.1-8B share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3.1-8B has 43.