Model comparison
GLM-4.5 vs Olmo 3.1 32b Instruct
GLM-4.5 is the stronger model overall, scoring 42.0 to 39.4 on the Noometry Index.
Last verified . 16 shared benchmarks.
Allen Institute for AI (Ai2)Summary
- They share 16 benchmarks with published results for both. GLM-4.5 scores higher in 6 categories and Olmo 3.1 32b Instruct in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GLM-4.5 leads 52.8 to 42.6.
Side by side
| GLM-4.5 | Olmo 3.1 32b Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Allen Institute for AI (Ai2) |
| Noometry Index | 42.0 | 39.4 |
| Released | 2025-07-27 | — |
| Weights | Open | Open |
| Context window | 131K | — |
| Max output | 98K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 27 | 16 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Olmo 3.1 32b Instruct: 39.5 (#157)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Coding | 1434 | 1347 |
| SWE-bench Verified (bash only) | 54.2% | — |
| WeirdML | 40.6% | — |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Olmo 3.1 32b Instruct: 26.4 (#132)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1429 | 1322 |
| Kagi LLM Benchmark | 57.9% | — |
Math GLM-4.5 leads
GLM-4.5: 39.0 (#116), Olmo 3.1 32b Instruct: 36.3 (#167)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Math | 1427 | 1305 |
Knowledge Too close to call
GLM-4.5: 35.9 (#179), Olmo 3.1 32b Instruct: 36.1 (#175)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Expert | 1433 | 1308 |
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
Multilingual GLM-4.5 leads
GLM-4.5: 52.8 (#77), Olmo 3.1 32b Instruct: 42.6 (#191)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1275 |
| LMArena Chinese | 1465 | 1304 |
| LMArena French | 1418 | 1328 |
| LMArena German | 1407 | 1282 |
| LMArena Korean | 1380 | 1206 |
| LMArena Russian | 1414 | 1268 |
| LMArena Spanish | 1454 | 1336 |
| LMArena Japanese | 1415 | — |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Olmo 3.1 32b Instruct: 68.6 (#187)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1404 | 1299 |
Long Context Olmo 3.1 32b Instruct leads
GLM-4.5: 38.2 (#201), Olmo 3.1 32b Instruct: 39.9 (#166)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1412 | 1312 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.5 (#127), Olmo 3.1 32b Instruct: 50.2 (#185)
| Benchmark | GLM-4.5 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Text | 1430 | 1311 |
| LMArena Creative Writing | 1395 | 1264 |
| LMArena Multi-Turn | 1415 | 1309 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
Frequently asked questions
Is GLM-4.5 better than Olmo 3.1 32b Instruct?
GLM-4.5 is the stronger model overall, scoring 42.0 to 39.4 on the Noometry Index.
Is GLM-4.5 or Olmo 3.1 32b Instruct better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 39.5 in the Noometry coding category.
How many benchmarks do GLM-4.5 and Olmo 3.1 32b Instruct share?
16 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Olmo 3.1 32b Instruct has 16.