Model comparison
GLM-5V-Turbo vs Llama-3.3-70B-Instruct
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
MetaSummary
- They share 16 benchmarks with published results for both. GLM-5V-Turbo scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5V-Turbo leads 39.4 to 15.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- GLM-5V-Turbo accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GLM-5V-Turbo | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 43.8 | 30.6 |
| Released | 2026-04-01 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 200K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.20 | $0.10 |
| Output $ / M tokens | $4 | $0.32 |
| Results tracked | 19 | 43 |
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Category by category
Coding GLM-5V-Turbo leads
GLM-5V-Turbo: 42.1 (#111), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1466 | 1268 |
| LMArena WebDev | 1401 | — |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
GLM-5V-Turbo: —, Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning GLM-5V-Turbo leads
GLM-5V-Turbo: 29.7 (#89), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1257 |
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.33 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GLM-5V-Turbo leads
GLM-5V-Turbo: 39.4 (#106), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1441 | 1267 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GLM-5V-Turbo leads
GLM-5V-Turbo: 40.6 (#117), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1452 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GLM-5V-Turbo: 40.9 (#42), Llama-3.3-70B-Instruct: —
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1264 | — |
| LMArena Document | 1416 | — |
Multilingual GLM-5V-Turbo leads
GLM-5V-Turbo: 53.0 (#73), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1420 | 1236 |
| LMArena Chinese | 1488 | 1217 |
| LMArena French | 1444 | 1281 |
| LMArena German | 1423 | 1251 |
| LMArena Korean | 1396 | 1143 |
| LMArena Russian | 1431 | 1252 |
| LMArena Spanish | 1450 | 1270 |
| LMArena Japanese | — | 1150 |
Instruction Following GLM-5V-Turbo leads
GLM-5V-Turbo: 75.0 (#80), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1423 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GLM-5V-Turbo leads
GLM-5V-Turbo: 44.0 (#80), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1438 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GLM-5V-Turbo leads
GLM-5V-Turbo: 62.5 (#73), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GLM-5V-Turbo | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1437 | 1274 |
| LMArena Creative Writing | 1416 | 1250 |
| LMArena Multi-Turn | 1432 | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GLM-5V-Turbo better than Llama-3.3-70B-Instruct?
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.
Which is cheaper, GLM-5V-Turbo 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-5V-Turbo lists at $1.20 and $4.
Is GLM-5V-Turbo or Llama-3.3-70B-Instruct better for coding?
GLM-5V-Turbo scores higher on coding benchmarks: 42.1 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5V-Turbo does, with 200K tokens against 128K.
How many benchmarks do GLM-5V-Turbo and Llama-3.3-70B-Instruct share?
16 benchmarks have published results for both models. GLM-5V-Turbo has 19 scored results on Noometry and Llama-3.3-70B-Instruct has 43.