Model comparison
Codellama 34b Instruct vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.6 to 30.2 on the Noometry Index.
Last verified . 10 shared benchmarks.
MetaSummary
- They share 10 benchmarks with published results for both. Codellama 34b Instruct scores higher in 0 categories and GLM-5.3 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.9 to 28.4.
Side by side
| Codellama 34b Instruct | GLM-5.3 | |
|---|---|---|
| Provider | Meta | Z.ai (Zhipu) |
| Noometry Index | 30.2 | 54.6 |
| Released | — | 2026-08-14 |
| Weights | Open | Open |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $1.40 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 14 | 41 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.3 leads
Codellama 34b Instruct: 28.0 (#310), GLM-5.3: 59.0 (#12)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Coding | 1046 | 1499 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| LMArena WebDev | — | 1623 |
| FrontierSWE | — | 30.2% |
| SciCode | — | 59% |
| WeirdML | — | 75.4% |
| BigCodeBench Instruct | 29% | — |
| BigCodeBench Complete | 37.1% | — |
| ALE-Bench | — | 1,317 |
| HumanEval+ | 43.9% | — |
| MBPP+ | 56.3% | — |
Agentic & Tool Use Not comparable
Codellama 34b Instruct: —, GLM-5.3: 36.3 (#34)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
Codellama 34b Instruct: 18.8 (#250), GLM-5.3: 45.2 (#42)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Hard Prompts | 1032 | 1491 |
| NYT Connections (extended) | — | 74.2% |
| CritPt | — | 19.1% |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 33% |
| DTBench | — | 87.7% |
| LMCA | — | 55.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.56 |
Math GLM-5.3 leads
Codellama 34b Instruct: 28.5 (#227), GLM-5.3: 62.1 (#27)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Math | 1056 | 1493 |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| OTIS Mock AIME 2024-2025 | — | 91.1% |
| ProofBench | — | 49% |
Knowledge Not comparable
Codellama 34b Instruct: —, GLM-5.3: 58.3 (#34)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 41% |
| LMArena Expert | — | 1512 |
Multilingual GLM-5.3 leads
Codellama 34b Instruct: 25.9 (#279), GLM-5.3: 55.9 (#27)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1011 | 1457 |
| LMArena Chinese | 976 | 1523 |
| LMArena French | — | 1499 |
| LMArena German | — | 1505 |
| LMArena Japanese | — | 1438 |
| LMArena Korean | — | 1478 |
| LMArena Russian | — | 1460 |
| LMArena Spanish | — | 1444 |
Instruction Following GLM-5.3 leads
Codellama 34b Instruct: 52.4 (#286), GLM-5.3: 77.8 (#15)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1028 | 1481 |
Long Context GLM-5.3 leads
Codellama 34b Instruct: 30.8 (#279), GLM-5.3: 45.5 (#32)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1014 | 1485 |
Writing & Preference GLM-5.3 leads
Codellama 34b Instruct: 28.4 (#292), GLM-5.3: 75.9 (#6)
| Benchmark | Codellama 34b Instruct | GLM-5.3 |
|---|---|---|
| LMArena Text | 1066 | 1473 |
| LMArena Creative Writing | 1032 | 1459 |
| LMArena Multi-Turn | 1015 | 1473 |
| EQ-Bench Creative Writing | — | 2075 |
Frequently asked questions
Is Codellama 34b Instruct better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.6 to 30.2 on the Noometry Index.
Is Codellama 34b Instruct or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.0 versus 28.0 in the Noometry coding category.
How many benchmarks do Codellama 34b Instruct and GLM-5.3 share?
10 benchmarks have published results for both models. Codellama 34b Instruct has 14 scored results on Noometry and GLM-5.3 has 41.