Model comparison
GLM-5V-Turbo vs Phi 3 Small 8k Instruct
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 29.3 on the Noometry Index.
Last verified . 16 shared benchmarks.
MicrosoftSummary
- They share 16 benchmarks with published results for both. GLM-5V-Turbo scores higher in 8 categories and Phi 3 Small 8k Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5V-Turbo leads 62.5 to 31.1.
- Phi 3 Small 8k Instruct has downloadable open weights; the other is API-only.
Side by side
| GLM-5V-Turbo | Phi 3 Small 8k Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Microsoft |
| Noometry Index | 43.8 | 29.3 |
| Released | 2026-04-01 | 2024-04-23 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.20 | — |
| Output $ / M tokens | $4 | — |
| Results tracked | 19 | 32 |
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Category by category
Coding GLM-5V-Turbo leads
GLM-5V-Turbo: 42.1 (#111), Phi 3 Small 8k Instruct: 27.9 (#318)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Coding | 1466 | 1101 |
| LMArena WebDev | 1401 | — |
| LiveBench Coding | — | 20.3% |
Reasoning GLM-5V-Turbo leads
GLM-5V-Turbo: 29.7 (#89), Phi 3 Small 8k Instruct: 14.8 (#323)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1100 |
| LiveBench Reasoning | — | 15.9% |
| LiveBench Data Analysis | — | 30.3% |
| Adversarial NLI | — | 58.1% |
| BIG-Bench Hard | — | 79.1% |
| HellaSwag | — | 77% |
| LiveBench | — | 24% |
| WinoGrande | — | 81.5% |
Math GLM-5V-Turbo leads
GLM-5V-Turbo: 39.4 (#106), Phi 3 Small 8k Instruct: 27.6 (#248)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Math | 1441 | 1151 |
| LiveBench Math | — | 17.6% |
Knowledge GLM-5V-Turbo leads
GLM-5V-Turbo: 40.6 (#117), Phi 3 Small 8k Instruct: 29.1 (#240)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Expert | 1452 | 1067 |
| ARC (AI2) Challenge | — | 90.7% |
| MMLU | — | 75.7% |
| OpenBookQA | — | 88% |
| TriviaQA | — | 58.1% |
Multimodal Not comparable
GLM-5V-Turbo: 40.9 (#42), Phi 3 Small 8k Instruct: —
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Vision | 1264 | — |
| LMArena Document | 1416 | — |
Multilingual GLM-5V-Turbo leads
GLM-5V-Turbo: 53.0 (#73), Phi 3 Small 8k Instruct: 28.5 (#272)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Non-English | 1420 | 1058 |
| LMArena Chinese | 1488 | 1061 |
| LMArena French | 1444 | 1135 |
| LMArena German | 1423 | 1080 |
| LMArena Korean | 1396 | 894 |
| LMArena Russian | 1431 | 1111 |
| LMArena Spanish | 1450 | 1111 |
| LMArena Japanese | — | 966 |
Instruction Following GLM-5V-Turbo leads
GLM-5V-Turbo: 75.0 (#80), Phi 3 Small 8k Instruct: 51.9 (#292)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Instruction Following | 1423 | 1087 |
| LiveBench Instruction Following | — | 47.2% |
Long Context GLM-5V-Turbo leads
GLM-5V-Turbo: 44.0 (#80), Phi 3 Small 8k Instruct: 33.0 (#267)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Longer Query | 1438 | 1088 |
Writing & Preference GLM-5V-Turbo leads
GLM-5V-Turbo: 62.5 (#73), Phi 3 Small 8k Instruct: 31.1 (#284)
| Benchmark | GLM-5V-Turbo | Phi 3 Small 8k Instruct |
|---|---|---|
| LMArena Text | 1437 | 1110 |
| LMArena Creative Writing | 1416 | 1083 |
| LMArena Multi-Turn | 1432 | 1068 |
| LiveBench Language | — | 12.9% |
Frequently asked questions
Is GLM-5V-Turbo better than Phi 3 Small 8k Instruct?
GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 29.3 on the Noometry Index.
Is GLM-5V-Turbo or Phi 3 Small 8k Instruct better for coding?
GLM-5V-Turbo scores higher on coding benchmarks: 42.1 versus 27.9 in the Noometry coding category.
How many benchmarks do GLM-5V-Turbo and Phi 3 Small 8k Instruct share?
16 benchmarks have published results for both models. GLM-5V-Turbo has 19 scored results on Noometry and Phi 3 Small 8k Instruct has 32.