Model comparison
GLM-4.7-Flash vs Olmo 2 0325 32b Instruct
GLM-4.7-Flash is the stronger model overall, scoring 38.8 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-Flash scores higher in 7 categories and Olmo 2 0325 32b Instruct in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7-Flash leads 35.5 to 19.5.
Side by side
| GLM-4.7-Flash | Olmo 2 0325 32b Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Allen Institute for AI (Ai2) |
| Noometry Index | 38.8 | 32.7 |
| Released | 2026-01-19 | — |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 16 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Olmo 2 0325 32b Instruct: 35.2 (#227)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Coding | 1383 | 1210 |
Reasoning Olmo 2 0325 32b Instruct leads
GLM-4.7-Flash: 20.9 (#229), Olmo 2 0325 32b Instruct: 23.6 (#175)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1208 |
| Chess Puzzles | 0% | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Olmo 2 0325 32b Instruct: 26.8 (#255)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Math | 1355 | 1208 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| Omni-MATH | — | 16.1% |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.5 (#184), Olmo 2 0325 32b Instruct: 19.5 (#279)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| GPQA Diamond | 60.5% | — |
| MMLU-Pro | — | 41.4% |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | — | 28.7% |
| LMArena Expert | 1357 | — |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Olmo 2 0325 32b Instruct: 34.8 (#248)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Non-English | 1330 | 1160 |
| LMArena Chinese | 1403 | 1192 |
| LMArena Russian | 1332 | 1187 |
| LMArena French | 1332 | — |
| LMArena German | 1337 | — |
| LMArena Korean | 1283 | — |
| LMArena Spanish | 1350 | — |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Olmo 2 0325 32b Instruct: 61.5 (#244)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Instruction Following | 1327 | 1186 |
| IFEval | — | 78% |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), Olmo 2 0325 32b Instruct: 36.2 (#234)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Longer Query | 1345 | 1194 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.4 (#210), Olmo 2 0325 32b Instruct: 42.1 (#236)
| Benchmark | GLM-4.7-Flash | Olmo 2 0325 32b Instruct |
|---|---|---|
| LMArena Text | 1351 | 1218 |
| LMArena Creative Writing | 1297 | 1199 |
| LMArena Multi-Turn | 1342 | 1221 |
| EQ-Bench Creative Writing | 1125 | — |
| WildBench | — | 73.4% |
Frequently asked questions
Is GLM-4.7-Flash better than Olmo 2 0325 32b Instruct?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 32.7 on the Noometry Index.
Is GLM-4.7-Flash or Olmo 2 0325 32b Instruct better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 35.2 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Olmo 2 0325 32b Instruct share?
11 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Olmo 2 0325 32b Instruct has 16.