Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs GPT-5.3 Chat
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 26.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in knowledge, where GPT-5.3 Chat leads 38.8 to 16.0.
- DeepSeek-R1-Distill-Qwen-1.5B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 26.1 | 42.8 |
| Released | 2025-01-20 | 2026-03-03 |
| Weights | Open | Proprietary |
| Context window | — | 128K |
| Max output | — | 16K |
| Input $ / M tokens | — | $1.75 |
| Output $ / M tokens | — | $14 |
| Results tracked | 5 | 18 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.3 Chat leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), GPT-5.3 Chat: 41.4 (#124)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| BigCodeBench Instruct | 7% | — |
| LMArena Coding | — | 1408 |
| BigCodeBench Complete | 7.9% | — |
Reasoning GPT-5.3 Chat leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), GPT-5.3 Chat: 28.5 (#102)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | — | 1399 |
Math GPT-5.3 Chat leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), GPT-5.3 Chat: 38.2 (#142)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | — |
| LMArena Math | — | 1389 |
Knowledge GPT-5.3 Chat leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), GPT-5.3 Chat: 38.8 (#140)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| GPQA Diamond | 33.6% | — |
| LMArena Expert | — | 1397 |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.3 Chat: 50.3 (#124)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | — | 1382 |
| LMArena Chinese | — | 1432 |
| LMArena French | — | 1397 |
| LMArena German | — | 1384 |
| LMArena Japanese | — | 1352 |
| LMArena Korean | — | 1346 |
| LMArena Russian | — | 1400 |
| LMArena Spanish | — | 1371 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.3 Chat: 72.8 (#129)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | — | 1378 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.3 Chat: 42.6 (#120)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | — | 1396 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.3 Chat: 63.1 (#68)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | — | 1389 |
| LMArena Creative Writing | — | 1355 |
| EQ-Bench Creative Writing | — | 1690 |
| LMArena Multi-Turn | — | 1412 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than GPT-5.3 Chat?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or GPT-5.3 Chat better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and GPT-5.3 Chat share?
0 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and GPT-5.3 Chat has 18.