Model comparison
DeepSeek-V3.1 vs phi-3-medium 14B
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 29.7 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeek
MicrosoftSummary
- They share 1 benchmark with published results for both. DeepSeek-V3.1 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 DeepSeek-V3.1 leads 43.7 to 9.1.
Side by side
| DeepSeek-V3.1 | phi-3-medium 14B | |
|---|---|---|
| Provider | DeepSeek | Microsoft |
| Noometry Index | 42.8 | 29.7 |
| Released | 2025-08-21 | 2024-04-23 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 13 |
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Category by category
Coding DeepSeek-V3.1 leads
DeepSeek-V3.1: 40.3 (#144), phi-3-medium 14B: 36.8 (#201)
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| WeirdML | 38.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1417 | — |
| BigCodeBench Complete | — | 48.7% |
Reasoning Not comparable
DeepSeek-V3.1: 27.9 (#110), phi-3-medium 14B: —
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| Epoch Capabilities Index | 139.92 | 121.23 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Adversarial NLI | — | 55.8% |
| BIG-Bench Hard | — | 81.4% |
| ForecastBench | 58 | — |
| HellaSwag | — | 82.4% |
| WinoGrande | — | 81.5% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), phi-3-medium 14B: 27.3 (#250)
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| LMArena Math | 1420 | — |
| MATH Level 5 | — | 17.6% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), phi-3-medium 14B: 9.1 (#306)
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| GPQA Diamond | — | 27.6% |
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1405 | — |
| ARC (AI2) Challenge | — | 91.6% |
| MMLU | — | 78% |
| OpenBookQA | — | 87.4% |
| TriviaQA | — | 73.9% |
Multilingual Not comparable
DeepSeek-V3.1: 51.6 (#106), phi-3-medium 14B: —
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1431 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 73.9 (#110), phi-3-medium 14B: —
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#232), phi-3-medium 14B: —
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.3 (#98), phi-3-medium 14B: —
| Benchmark | DeepSeek-V3.1 | phi-3-medium 14B |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than phi-3-medium 14B?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 29.7 on the Noometry Index.
Is DeepSeek-V3.1 or phi-3-medium 14B better for coding?
DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 36.8 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and phi-3-medium 14B share?
1 benchmark has published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and phi-3-medium 14B has 13.