Model comparison
DeepSeek-R1-Distill-Qwen-14B vs GPT-5 Nano
DeepSeek-R1-Distill-Qwen-14B and GPT-5 Nano score almost the same on the Noometry Index (32.7 vs 33.5), so choose on price, context window or the category you care about most.
Last verified . 5 shared benchmarks.
DeepSeekSummary
- They share 5 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-14B scores higher in 3 categories and GPT-5 Nano in 1 category; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Nano leads 35.9 to 24.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 50.6% for DeepSeek-R1-Distill-Qwen-14B and 81.1% for GPT-5 Nano.
- DeepSeek-R1-Distill-Qwen-14B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 32.7 | 33.5 |
| Released | 2025-01-20 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | — | 400K |
| Max output | — | 128K |
| Input $ / M tokens | — | $0.05 |
| Output $ / M tokens | — | $0.40 |
| Results tracked | 7 | 49 |
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Category by category
Coding DeepSeek-R1-Distill-Qwen-14B leads
DeepSeek-R1-Distill-Qwen-14B: 36.9 (#200), GPT-5 Nano: 33.6 (#254)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| SWE-bench Verified (bash only) | — | 34.8% |
| WeirdML | — | 38.1% |
| BigCodeBench Instruct | 38.1% | — |
| LMArena Coding | — | 1351 |
| BigCodeBench Complete | 48.4% | — |
| ALE-Bench | — | 718.67 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 25.8 (#106)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
Reasoning DeepSeek-R1-Distill-Qwen-14B leads
DeepSeek-R1-Distill-Qwen-14B: 19.2 (#263), GPT-5 Nano: 16.3 (#306)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| Chess Puzzles | 1% | 27% |
| Epoch Capabilities Index | 135.43 | 139.38 |
| ARC-AGI-2 | — | 2.6% |
| Kagi LLM Benchmark | — | 62.2% |
| ARC-AGI-1 | — | 20.7% |
| LMArena Hard Prompts | — | 1328 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 62.7% |
| LMCA | — | 7.9% |
| ForecastBench | — | 59.1 |
Math DeepSeek-R1-Distill-Qwen-14B leads
DeepSeek-R1-Distill-Qwen-14B: 35.5 (#184), GPT-5 Nano: 29.4 (#241)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 50.6% | 81.1% |
| MATH Level 5 | 87.1% | 95.2% |
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| ProofBench | — | 12% |
| Omni-MATH | — | 54.6% |
| LMArena Math | — | 1317 |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GPT-5 Nano leads
DeepSeek-R1-Distill-Qwen-14B: 24.1 (#270), GPT-5 Nano: 35.9 (#178)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 44.7% | 69.4% |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
| LMArena Expert | — | 1321 |
Multimodal Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 31.3 (#108)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |
Multilingual Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 45.3 (#172)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | — | 1313 |
| LMArena Chinese | — | 1356 |
| LMArena German | — | 1327 |
| LMArena Japanese | — | 1226 |
| LMArena Korean | — | 1269 |
| LMArena Russian | — | 1296 |
| LMArena Spanish | — | 1360 |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 75.0 (#79)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| IFEval | — | 93.2% |
| LMArena Instruction Following | — | 1306 |
Long Context Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 31.3 (#281)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| Fiction.LiveBench | — | 44.4% |
| LMArena Longer Query | — | 1312 |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-14B: —, GPT-5 Nano: 39.1 (#249)
| Benchmark | DeepSeek-R1-Distill-Qwen-14B | GPT-5 Nano |
|---|---|---|
| LMArena Text | — | 1320 |
| LMArena Creative Writing | — | 1249 |
| EQ-Bench Creative Writing | — | 705 |
| WildBench | — | 80.6% |
| LMArena Multi-Turn | — | 1311 |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-14B better than GPT-5 Nano?
DeepSeek-R1-Distill-Qwen-14B and GPT-5 Nano score almost the same on the Noometry Index (32.7 vs 33.5), so choose on price, context window or the category you care about most.
Is DeepSeek-R1-Distill-Qwen-14B or GPT-5 Nano better for coding?
DeepSeek-R1-Distill-Qwen-14B scores higher on coding benchmarks: 36.9 versus 33.6 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-14B and GPT-5 Nano share?
5 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-14B has 7 scored results on Noometry and GPT-5 Nano has 49.