Model comparison
GLM-5.3 vs Llama 3.1-8B
GLM-5.3 is the stronger model overall, scoring 54.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 37× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. GLM-5.3 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 math, where GLM-5.3 leads 62.3 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.3 | Llama 3.1-8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 54.8 | 23.0 |
| Released | 2026-08-14 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.40 | $0.05 |
| Output $ / M tokens | $4.40 | $0.08 |
| Results tracked | 42 | 43 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| SciCode | 59% | 13.2% |
| WeirdML | 75.4% | 1.7% |
| LMArena Coding | 1496 | 1195 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| CritPt | 19.1% | 0% |
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1489 | 1175 |
| DTBench | 87.7% | 50.9% |
| LMCA | 55.5% | 5.4% |
| Epoch Capabilities Index | 155.61 | 116.57 |
| NYT Connections (extended) | 74.2% | — |
| Mystery Game Puzzles | 33% | — |
| Bench to the Future 3 | 0.15 | — |
| PIQA | — | 81.2% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 1.7% |
| LMArena Math | 1489 | 1179 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 90.9% | 27% |
| LMArena Expert | 1516 | 1144 |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1457 | 1148 |
| LMArena Chinese | 1528 | 1151 |
| LMArena French | 1499 | 1177 |
| LMArena German | 1499 | 1144 |
| LMArena Japanese | 1453 | 1061 |
| LMArena Korean | 1472 | 1053 |
| LMArena Russian | 1463 | 1158 |
| LMArena Spanish | 1460 | 1169 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1159 |
| IFEval | — | 74.3% |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1482 | 1182 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GLM-5.3 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1471 | 1187 |
| LMArena Creative Writing | 1457 | 1154 |
| EQ-Bench Creative Writing | 2075 | 713 |
| LMArena Multi-Turn | 1472 | 1172 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GLM-5.3 better than Llama 3.1-8B?
GLM-5.3 is the stronger model overall, scoring 54.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 37× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 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-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Llama 3.1-8B better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3 and Llama 3.1-8B share?
27 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Llama 3.1-8B has 43.