Model comparison
GLM-4.7-Flash vs Yi-1.5-34B
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.6 on the Noometry Index.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Yi-1.5-34B 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 14.8.
- The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 32% for Yi-1.5-34B.
Side by side
| GLM-4.7-Flash | Yi-1.5-34B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | 01.AI |
| Noometry Index | 38.8 | 30.6 |
| Released | 2026-01-19 | 2024-05-13 |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 21 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Yi-1.5-34B: 32.4 (#272)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Coding | 1383 | 1169 |
| BigCodeBench Instruct | — | 33.9% |
| BigCodeBench Complete | — | 43.8% |
Reasoning Yi-1.5-34B leads
GLM-4.7-Flash: 20.9 (#229), Yi-1.5-34B: 22.5 (#191)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1160 |
| Chess Puzzles | 0% | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Yi-1.5-34B: 27.5 (#249)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1355 | 1182 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| MATH Level 5 | — | 25.5% |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.5 (#184), Yi-1.5-34B: 14.8 (#295)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | 60.5% | 32% |
| LMArena Expert | 1357 | 1144 |
| Vectara Hallucination Rate | 9.3% | — |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Yi-1.5-34B: 32.3 (#256)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1330 | 1121 |
| LMArena Chinese | 1403 | 1213 |
| LMArena French | 1332 | 1156 |
| LMArena German | 1337 | 1111 |
| LMArena Korean | 1283 | 1005 |
| LMArena Russian | 1332 | 1091 |
| LMArena Spanish | 1350 | 1121 |
| LMArena Japanese | — | 1021 |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Yi-1.5-34B: 59.2 (#257)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1139 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), Yi-1.5-34B: 34.6 (#248)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1345 | 1143 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.4 (#210), Yi-1.5-34B: 37.4 (#257)
| Benchmark | GLM-4.7-Flash | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1351 | 1173 |
| LMArena Creative Writing | 1297 | 1135 |
| LMArena Multi-Turn | 1342 | 1153 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Yi-1.5-34B?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 30.6 on the Noometry Index.
Is GLM-4.7-Flash or Yi-1.5-34B better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 32.4 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Yi-1.5-34B share?
17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Yi-1.5-34B has 21.