Model comparison
GLM-4.7 vs Olmo 3.1 32b Instruct
GLM-4.7 is the stronger model overall, scoring 41.7 to 38.8 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-4.7 scores higher in 7 categories and Olmo 3.1 32b Instruct in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 35.4.
Side by side
| GLM-4.7 | Olmo 3.1 32b Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Allen Institute for AI (Ai2) |
| Noometry Index | 41.7 | 38.8 |
| Released | 2025-12-22 | — |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 36 | 16 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 43.6 (#74), Olmo 3.1 32b Instruct: 38.9 (#154)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Coding | 1454 | 1346 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.4 (#96), Olmo 3.1 32b Instruct: —
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning Olmo 3.1 32b Instruct leads
GLM-4.7: 23.7 (#161), Olmo 3.1 32b Instruct: 25.4 (#131)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1322 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| Epoch Capabilities Index | 143.53 | — |
Math GLM-4.7 leads
GLM-4.7: 37.7 (#92), Olmo 3.1 32b Instruct: 33.6 (#169)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Math | 1423 | 1305 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#74), Olmo 3.1 32b Instruct: 35.4 (#175)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Expert | 1424 | 1307 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
Multilingual GLM-4.7 leads
GLM-4.7: 53.0 (#75), Olmo 3.1 32b Instruct: 42.8 (#185)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1275 |
| LMArena Chinese | 1495 | 1304 |
| LMArena French | 1432 | 1328 |
| LMArena German | 1426 | 1283 |
| LMArena Korean | 1401 | 1206 |
| LMArena Russian | 1423 | 1268 |
| LMArena Spanish | 1433 | 1337 |
| LMArena Japanese | 1439 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.6 (#90), Olmo 3.1 32b Instruct: 68.8 (#182)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1298 |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#111), Olmo 3.1 32b Instruct: 39.9 (#161)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1312 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 61.1 (#89), Olmo 3.1 32b Instruct: 50.4 (#179)
| Benchmark | GLM-4.7 | Olmo 3.1 32b Instruct |
|---|---|---|
| LMArena Text | 1436 | 1311 |
| LMArena Creative Writing | 1401 | 1264 |
| LMArena Multi-Turn | 1446 | 1309 |
| EQ-Bench Creative Writing | 1413 | — |
Frequently asked questions
Is GLM-4.7 better than Olmo 3.1 32b Instruct?
GLM-4.7 is the stronger model overall, scoring 41.7 to 38.8 on the Noometry Index.
Is GLM-4.7 or Olmo 3.1 32b Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 43.6 versus 38.9 in the Noometry coding category.
How many benchmarks do GLM-4.7 and Olmo 3.1 32b Instruct share?
16 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Olmo 3.1 32b Instruct has 16.