Model comparison
GLM-4.6 vs Llama 3.1-8B
GLM-4.6 is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6 leads 39.6 to 8.0.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 25.8% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-4.6 | Llama 3.1-8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 40.9 | 22.8 |
| Released | 2025-09-30 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $0.60 | $0.05 |
| Output $ / M tokens | $2.20 | $0.08 |
| Results tracked | 29 | 43 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 39.9 (#137), Llama 3.1-8B: 19.9 (#335)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| SciCode | 38.4% | 13.2% |
| LMArena Coding | 1449 | 1195 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 340.82 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.1 (#60), Llama 3.1-8B: 22.3 (#123)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 25.8% |
| Terminal-Bench | 24.5% | — |
| BALROG | — | 15.1% |
Reasoning GLM-4.6 leads
GLM-4.6: 23.1 (#168), Llama 3.1-8B: 14.5 (#316)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1440 | 1175 |
| Kagi LLM Benchmark | 47.4% | — |
| Chess Puzzles | — | 0% |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| Epoch Capabilities Index | — | 116.48 |
| PIQA | — | 81.2% |
Math GLM-4.6 leads
GLM-4.6: 36.4 (#115), Llama 3.1-8B: 9.5 (#311)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Math | 1431 | 1179 |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 82.4% |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), Llama 3.1-8B: 8.0 (#302)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Expert | 1432 | 1144 |
| GPQA Diamond | — | 27% |
| MMLU-Pro | — | 40.6% |
| Vectara Hallucination Rate | 9.5% | — |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), Llama 3.1-8B: 34.2 (#244)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1426 | 1148 |
| LMArena Chinese | 1500 | 1151 |
| LMArena French | 1459 | 1176 |
| LMArena German | 1447 | 1144 |
| LMArena Japanese | 1394 | 1061 |
| LMArena Korean | 1401 | 1053 |
| LMArena Russian | 1418 | 1158 |
| LMArena Spanish | 1436 | 1169 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), Llama 3.1-8B: 59.1 (#253)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1159 |
| IFEval | — | 74.3% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), Llama 3.1-8B: 35.8 (#233)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1423 | 1182 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), Llama 3.1-8B: 29.9 (#285)
| Benchmark | GLM-4.6 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1440 | 1187 |
| LMArena Creative Writing | 1411 | 1154 |
| EQ-Bench Creative Writing | 1411 | 713 |
| LMArena Multi-Turn | 1427 | 1172 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GLM-4.6 better than Llama 3.1-8B?
GLM-4.6 is the stronger model overall, scoring 40.9 to 22.8 on the Noometry Index. Llama 3.1-8B costs 17× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or Llama 3.1-8B better for coding?
GLM-4.6 scores higher on coding benchmarks: 39.9 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.6 and Llama 3.1-8B share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Llama 3.1-8B has 43.