Model comparison
GLM-4.7 vs Olmo 2 0325 32b Instruct
GLM-4.7 is the stronger model overall, scoring 42.0 to 32.7 on the Noometry Index.
Last verified . 11 shared benchmarks.
Allen Institute for AI (Ai2)Summary
- They share 11 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Olmo 2 0325 32b Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 19.5.
Side by side
| GLM-4.7 | Olmo 2 0325 32b Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Allen Institute for AI (Ai2) |
| Noometry Index | 42.0 | 32.7 |
| 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: 44.0 (#79), Olmo 2 0325 32b Instruct: 35.2 (#227)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Coding | 1454 | 1210 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), Olmo 2 0325 32b Instruct: —
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning Too close to call
GLM-4.7: 24.3 (#164), Olmo 2 0325 32b Instruct: 23.6 (#175)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1208 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| Epoch Capabilities Index | 143.51 | — |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Olmo 2 0325 32b Instruct: 26.8 (#255)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Math | 1423 | 1208 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| Omni-MATH | — | 16.1% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Olmo 2 0325 32b Instruct: 19.5 (#279)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 41.4% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 28.7% |
| LMArena Expert | 1424 | — |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Olmo 2 0325 32b Instruct: 34.8 (#248)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1160 |
| LMArena Chinese | 1495 | 1192 |
| LMArena Russian | 1423 | 1187 |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Spanish | 1434 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Olmo 2 0325 32b Instruct: 61.5 (#244)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1186 |
| IFEval | — | 78% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Olmo 2 0325 32b Instruct: 36.2 (#234)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1194 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Olmo 2 0325 32b Instruct: 42.1 (#236)
| Benchmark | GLM-4.7 | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Text | 1435 | 1218 |
| LMArena Creative Writing | 1401 | 1199 |
| LMArena Multi-Turn | 1446 | 1221 |
| EQ-Bench Creative Writing | 1413 | — |
| WildBench | — | 73.4% |
Frequently asked questions
Is GLM-4.7 better than Olmo 2 0325 32b Instruct?
GLM-4.7 is the stronger model overall, scoring 42.0 to 32.7 on the Noometry Index.
Is GLM-4.7 or Olmo 2 0325 32b Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 35.2 in the Noometry coding category.
How many benchmarks do GLM-4.7 and Olmo 2 0325 32b Instruct share?
11 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Olmo 2 0325 32b Instruct has 16.