Model comparison
GLM-4.6 vs phi-3-medium 14B
GLM-4.6 is the stronger model overall, scoring 41.4 to 29.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
MicrosoftSummary
- The widest gap is in knowledge, where GLM-4.6 leads 40.2 to 9.1.
Side by side
| GLM-4.6 | phi-3-medium 14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Microsoft |
| Noometry Index | 41.4 | 29.7 |
| Released | 2025-09-30 | 2024-04-23 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 13 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), phi-3-medium 14B: 36.8 (#201)
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1449 | — |
| BigCodeBench Complete | — | 48.7% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning Not comparable
GLM-4.6: 23.7 (#172), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| LMArena Hard Prompts | 1440 | — |
| Adversarial NLI | — | 55.8% |
| BIG-Bench Hard | — | 81.4% |
| Epoch Capabilities Index | — | 121.23 |
| HellaSwag | — | 82.4% |
| WinoGrande | — | 81.5% |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), phi-3-medium 14B: 27.3 (#250)
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Math | 1432 | — |
| MATH Level 5 | — | 17.6% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), phi-3-medium 14B: 9.1 (#306)
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| GPQA Diamond | — | 27.6% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
| ARC (AI2) Challenge | — | 91.6% |
| MMLU | — | 78% |
| OpenBookQA | — | 87.4% |
| TriviaQA | — | 73.9% |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.3 (#98), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
GLM-4.6: 61.1 (#90), phi-3-medium 14B: —
| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than phi-3-medium 14B?
GLM-4.6 is the stronger model overall, scoring 41.4 to 29.7 on the Noometry Index.
Is GLM-4.6 or phi-3-medium 14B better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 36.8 in the Noometry coding category.
How many benchmarks do GLM-4.6 and phi-3-medium 14B share?
0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and phi-3-medium 14B has 13.