Model comparison
DeepSeek-R1 vs GPT-5.3 Chat
DeepSeek-R1 and GPT-5.3 Chat score almost the same on the Noometry Index (42.3 vs 42.8), so choose on price, context window or the category you care about most.
Last verified . 18 shared benchmarks.
DeepSeekSummary
- They share 18 benchmarks with published results for both. DeepSeek-R1 scores higher in 5 categories and GPT-5.3 Chat in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.3 Chat leads 28.5 to 18.6.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- DeepSeek-R1 accepts more context: 164K tokens versus 128K.
Side by side
| DeepSeek-R1 | GPT-5.3 Chat | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 42.8 |
| Released | 2025-01-20 | 2026-03-03 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 128K |
| Max output | 64K | 16K |
| Input $ / M tokens | $0.50 | $1.75 |
| Output $ / M tokens | $2.15 | $14 |
| Results tracked | 52 | 18 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), GPT-5.3 Chat: 41.4 (#124)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Coding | 1427 | 1408 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), GPT-5.3 Chat: —
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning GPT-5.3 Chat leads
DeepSeek-R1: 18.6 (#278), GPT-5.3 Chat: 28.5 (#102)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1399 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| ARC-AGI-1 | 21.2% | — |
| CritPt | 1.1% | — |
| LiveBench Reasoning | 83.2% | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), GPT-5.3 Chat: 38.2 (#142)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Math | 1400 | 1389 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), GPT-5.3 Chat: 38.8 (#140)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Expert | 1394 | 1397 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), GPT-5.3 Chat: 50.3 (#124)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Non-English | 1412 | 1382 |
| LMArena Chinese | 1442 | 1432 |
| LMArena French | 1417 | 1397 |
| LMArena German | 1404 | 1384 |
| LMArena Japanese | 1391 | 1352 |
| LMArena Korean | 1360 | 1346 |
| LMArena Russian | 1423 | 1400 |
| LMArena Spanish | 1411 | 1371 |
Instruction Following Too close to call
DeepSeek-R1: 72.0 (#143), GPT-5.3 Chat: 72.8 (#129)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Instruction Following | 1382 | 1378 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), GPT-5.3 Chat: 42.6 (#120)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Longer Query | 1391 | 1396 |
| Fiction.LiveBench | 75% | — |
Writing & Preference GPT-5.3 Chat leads
DeepSeek-R1: 61.4 (#88), GPT-5.3 Chat: 63.1 (#68)
| Benchmark | DeepSeek-R1 | GPT-5.3 Chat |
|---|---|---|
| LMArena Text | 1428 | 1389 |
| LMArena Creative Writing | 1405 | 1355 |
| EQ-Bench Creative Writing | 1500 | 1690 |
| LMArena Multi-Turn | 1405 | 1412 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GPT-5.3 Chat?
DeepSeek-R1 and GPT-5.3 Chat score almost the same on the Noometry Index (42.3 vs 42.8), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-R1 or GPT-5.3 Chat?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is DeepSeek-R1 or GPT-5.3 Chat better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 41.4 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-R1 and GPT-5.3 Chat share?
18 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5.3 Chat has 18.