Model comparison
GLM-4.5V vs Llama-3.3-70B-Instruct
GLM-4.5V is the stronger model overall, scoring 39.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.8× 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 7 categories and Llama-3.3-70B-Instruct in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5V leads 34.7 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama-3.3-70B-Instruct accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.2 | 30.3 |
| Released | 2025-08-11 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $0.60 | $0.10 |
| Output $ / M tokens | $1.80 | $0.32 |
| Results tracked | 15 | 43 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 38.9 (#152), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1347 | 1268 |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning GLM-4.5V leads
GLM-4.5V: 26.5 (#118), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1335 | 1257 |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 59.8% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GLM-4.5V leads
GLM-4.5V: 34.7 (#157), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1353 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GLM-4.5V leads
GLM-4.5V: 36.8 (#155), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1353 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GLM-4.5V: 33.6 (#86), Llama-3.3-70B-Instruct: —
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.9 (#172), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1304 | 1236 |
| LMArena Chinese | 1338 | 1217 |
| LMArena Russian | 1299 | 1252 |
| LMArena Spanish | 1335 | 1269 |
| LMArena French | — | 1280 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1149 |
| LMArena Korean | — | 1142 |
Instruction Following Llama-3.3-70B-Instruct leads
GLM-4.5V: 69.4 (#170), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1311 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#166), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1305 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.7 (#162), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | GLM-4.5V | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1333 | 1274 |
| LMArena Creative Writing | 1295 | 1250 |
| LMArena Multi-Turn | 1333 | 1279 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GLM-4.5V better than Llama-3.3-70B-Instruct?
GLM-4.5V is the stronger model overall, scoring 39.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.8× 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-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Llama-3.3-70B-Instruct better for coding?
GLM-4.5V scores higher on coding benchmarks: 38.9 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Llama-3.3-70B-Instruct does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and Llama-3.3-70B-Instruct share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama-3.3-70B-Instruct has 43.