Model comparison
GPT-5.2 vs phi-3-medium 14B
GPT-5.2 is the stronger model overall, scoring 54.1 to 29.7 on the Noometry Index.
Last verified . 2 shared benchmarks.
MicrosoftSummary
- They share 2 benchmarks with published results for both. GPT-5.2 scores higher in 3 categories and phi-3-medium 14B in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.2 leads 59.3 to 9.1.
- The biggest single-benchmark swing is GPQA Diamond: 91.4% for GPT-5.2 and 27.6% for phi-3-medium 14B.
- phi-3-medium 14B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 | phi-3-medium 14B | |
|---|---|---|
| Provider | OpenAI | Microsoft |
| Noometry Index | 54.1 | 29.7 |
| Released | 2025-12-11 | 2024-04-23 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.75 | — |
| Output $ / M tokens | $14 | — |
| Results tracked | 67 | 13 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 51.6 (#37), phi-3-medium 14B: 36.8 (#201)
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1416 | — |
| SWE-bench Multilingual | 66.7% | — |
| GSO | 27.4% | — |
| WeirdML | 72.2% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1447 | — |
| BigCodeBench Complete | — | 48.7% |
| ALE-Bench | 1,294 | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use Not comparable
GPT-5.2: 40.2 (#24), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| Terminal-Bench | 64.9% | — |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Banking | 32.2% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| DeepResearch Bench | 41.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
| Vending-Bench 2 | 3,591 | — |
Reasoning Not comparable
GPT-5.2: 50.2 (#35), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| Epoch Capabilities Index | 153.45 | 121.23 |
| ARC-AGI-2 | 52.9% | — |
| SimpleBench | 45.8% | — |
| Kagi LLM Benchmark | 73.3% | — |
| NYT Connections (extended) | 83.6% | — |
| ARC-AGI-1 | 86.2% | — |
| Chess Puzzles | 49% | — |
| EnigmaEval | 10.4% | — |
| EBR-Bench | 23% | — |
| LMArena Hard Prompts | 1445 | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.9% | — |
| LMCA | 43.9% | — |
| Adversarial NLI | — | 55.8% |
| BIG-Bench Hard | — | 81.4% |
| ForecastBench | 60.1 | — |
| HellaSwag | — | 82.4% |
| WinoGrande | — | 81.5% |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), phi-3-medium 14B: 27.3 (#250)
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 72% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| ProofBench | 15% | — |
| LMArena Math | 1440 | — |
| MATH Level 5 | — | 17.6% |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), phi-3-medium 14B: 9.1 (#306)
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| GPQA Diamond | 91.4% | 27.6% |
| Humanity's Last Exam | 27.8% | — |
| SimpleQA Verified | 37.1% | — |
| Vectara Hallucination Rate | 8.4% | — |
| LMArena Expert | 1445 | — |
| ARC (AI2) Challenge | — | 91.6% |
| MMLU | — | 78% |
| OpenBookQA | — | 87.4% |
| TriviaQA | — | 73.9% |
Multimodal Not comparable
GPT-5.2: 51.3 (#7), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| LMArena Vision | 1268 | — |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual Not comparable
GPT-5.2: 53.4 (#67), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| LMArena Non-English | 1425 | — |
| LMArena Chinese | 1460 | — |
| LMArena French | 1455 | — |
| LMArena German | 1448 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1392 | — |
| LMArena Russian | 1440 | — |
| LMArena Spanish | 1433 | — |
Instruction Following Not comparable
GPT-5.2: 74.7 (#89), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| LMArena Instruction Following | 1417 | — |
Long Context Not comparable
GPT-5.2: 44.0 (#78), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| CL-bench | 18.2% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
GPT-5.2: 66.8 (#32), phi-3-medium 14B: —
| Benchmark | GPT-5.2 | phi-3-medium 14B |
|---|---|---|
| LMArena Text | 1439 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1703 | — |
| LMArena Multi-Turn | 1458 | — |
Frequently asked questions
Is GPT-5.2 better than phi-3-medium 14B?
GPT-5.2 is the stronger model overall, scoring 54.1 to 29.7 on the Noometry Index.
Is GPT-5.2 or phi-3-medium 14B better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 36.8 in the Noometry coding category.
How many benchmarks do GPT-5.2 and phi-3-medium 14B share?
2 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and phi-3-medium 14B has 13.