Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs GPT-5.4 nano
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 26.1 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeekSummary
- They share 3 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-1.5B scores higher in 0 categories and GPT-5.4 nano in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.4 nano leads 41.9 to 16.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 21.4% for DeepSeek-R1-Distill-Qwen-1.5B and 87.8% for GPT-5.4 nano.
- 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-5.4 nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 26.1 | 41.9 |
| Released | 2025-01-20 | 2026-03-17 |
| Weights | Open | Proprietary |
| Context window | — | 400K |
| Max output | — | 128K |
| Input $ / M tokens | — | $0.20 |
| Output $ / M tokens | — | $1.25 |
| Results tracked | 5 | 40 |
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Category by category
Coding GPT-5.4 nano leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), GPT-5.4 nano: 43.6 (#84)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| SciCode | — | 46.9% |
| WeirdML | — | 49.2% |
| BigCodeBench Instruct | 7% | — |
| LMArena Coding | — | 1405 |
| BigCodeBench Complete | 7.9% | — |
| ALE-Bench | — | 1,005 |
Reasoning GPT-5.4 nano leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), GPT-5.4 nano: 23.7 (#173)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| Chess Puzzles | 0% | 30% |
| ARC-AGI-2 | — | 5.7% |
| Kagi LLM Benchmark | — | 39.7% |
| ARC-AGI-1 | — | 51.5% |
| CritPt | — | 9.3% |
| LMArena Hard Prompts | — | 1381 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 80.3% |
| LMCA | — | 36.9% |
| Epoch Capabilities Index | — | 145.81 |
| ForecastBench | — | 57.3 |
Math GPT-5.4 nano leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), GPT-5.4 nano: 40.9 (#88)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | 87.8% |
| FrontierMath (Tiers 1-3) | — | 44.9% |
| FrontierMath Tier 4 | — | 12.2% |
| ProofBench | — | 5% |
| LMArena Math | — | 1406 |
| FrontierMath (Feb 2025 set) | — | 25.9% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge GPT-5.4 nano leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), GPT-5.4 nano: 41.9 (#103)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| GPQA Diamond | 33.6% | 78.5% |
| SimpleQA Verified | — | 11.7% |
| Vectara Hallucination Rate | — | 3.1% |
| LMArena Expert | — | 1396 |
Multimodal Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.4 nano: 36.7 (#78)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| LMArena Vision | — | 1196 |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.4 nano: 48.6 (#140)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| LMArena Non-English | — | 1359 |
| LMArena Chinese | — | 1392 |
| LMArena French | — | 1396 |
| LMArena German | — | 1367 |
| LMArena Japanese | — | 1343 |
| LMArena Korean | — | 1320 |
| LMArena Russian | — | 1363 |
| LMArena Spanish | — | 1371 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.4 nano: 71.9 (#144)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| LMArena Instruction Following | — | 1362 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.4 nano: 41.6 (#137)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| LMArena Longer Query | — | 1366 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, GPT-5.4 nano: 55.7 (#142)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | GPT-5.4 nano |
|---|---|---|
| LMArena Text | — | 1372 |
| LMArena Creative Writing | — | 1314 |
| LMArena Multi-Turn | — | 1382 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than GPT-5.4 nano?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or GPT-5.4 nano better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and GPT-5.4 nano share?
3 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and GPT-5.4 nano has 40.