Model comparison
DeepSeek-V3.1 vs GPT-4.5
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.2 on the Noometry Index.
Last verified . 22 shared benchmarks.
DeepSeekSummary
- They share 22 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 5 categories and GPT-4.5 in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 13.9.
- The biggest single-benchmark swing is Fiction.LiveBench: 52.8% for DeepSeek-V3.1 and 63.9% for GPT-4.5.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | GPT-4.5 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.8 | 37.2 |
| Released | 2025-08-21 | 2025-02-27 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 42 |
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Category by category
Coding GPT-4.5 leads
DeepSeek-V3.1: 40.3 (#144), GPT-4.5: 42.2 (#109)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| WeirdML | 38.4% | 39.4% |
| LMArena Coding | 1417 | 1396 |
| Aider Polyglot | — | 44.9% |
| LiveBench Coding | — | 75.2% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, GPT-4.5: 27.9 (#97)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| Cybench | — | 17.5% |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), GPT-4.5: 13.9 (#330)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| SimpleBench | 40% | 34.5% |
| LMArena Hard Prompts | 1417 | 1403 |
| Epoch Capabilities Index | 139.92 | 136.74 |
| ForecastBench | 58 | 61.7 |
| ARC-AGI-2 | — | 0.8% |
| Kagi LLM Benchmark | 53.2% | — |
| ARC-AGI-1 | — | 10.3% |
| EnigmaEval | — | 3.2% |
| LiveBench Reasoning | — | 71.1% |
| DTBench | 82.7% | — |
| LiveBench Data Analysis | — | 64.3% |
| LMCA | 24.3% | — |
| LiveBench | — | 69% |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), GPT-4.5: 32.6 (#211)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Math | 1420 | 1412 |
| OTIS Mock AIME 2024-2025 | — | 37.8% |
| LiveBench Math | — | 69.3% |
| MATH Level 5 | — | 78.6% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), GPT-4.5: 32.5 (#211)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Expert | 1405 | 1394 |
| GPQA Diamond | — | 68.7% |
| Humanity's Last Exam | — | 5.4% |
| Confabulations | — | 13.6% |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, GPT-4.5: 37.6 (#71)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Vision | — | 1195 |
| VPCT | — | 45% |
Multilingual Too close to call
DeepSeek-V3.1: 51.6 (#106), GPT-4.5: 52.5 (#83)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Non-English | 1400 | 1413 |
| LMArena Chinese | 1469 | 1421 |
| LMArena French | 1447 | 1418 |
| LMArena German | 1411 | 1457 |
| LMArena Japanese | 1378 | 1416 |
| LMArena Korean | 1337 | 1392 |
| LMArena Russian | 1405 | 1419 |
| LMArena Spanish | 1431 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), GPT-4.5: 72.6 (#134)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1404 |
| LiveBench Instruction Following | — | 72.3% |
Long Context GPT-4.5 leads
DeepSeek-V3.1: 36.3 (#232), GPT-4.5: 40.4 (#155)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| Fiction.LiveBench | 52.8% | 63.9% |
| LMArena Longer Query | 1422 | 1406 |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), GPT-4.5: 56.9 (#134)
| Benchmark | DeepSeek-V3.1 | GPT-4.5 |
|---|---|---|
| LMArena Text | 1420 | 1417 |
| LMArena Creative Writing | 1401 | 1394 |
| EQ-Bench Creative Writing | 1436 | 1258 |
| LMArena Multi-Turn | 1408 | 1444 |
| Short-Story Creative Writing | — | 75.6% |
| LiveBench Language | — | 61.5% |
Frequently asked questions
Is DeepSeek-V3.1 better than GPT-4.5?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.2 on the Noometry Index.
Is DeepSeek-V3.1 or GPT-4.5 better for coding?
GPT-4.5 scores higher on coding benchmarks: 42.2 versus 40.3 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and GPT-4.5 share?
22 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GPT-4.5 has 42.