Model comparison
DeepSeek-R1 vs GPT-5.4 Pro
GPT-5.4 Pro is the stronger model overall, scoring 58.9 to 42.3 on the Noometry Index. DeepSeek-R1 costs 74× less per token, which makes it the better buy when GPT-5.4 Pro's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
DeepSeekSummary
- They share 8 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and GPT-5.4 Pro in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 Pro leads 70.7 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-2: 1.3% for DeepSeek-R1 and 83.3% for GPT-5.4 Pro.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $30 / $180 for GPT-5.4 Pro.
- GPT-5.4 Pro accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-R1 | GPT-5.4 Pro | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 58.9 |
| Released | 2025-01-20 | 2026-03-05 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 64K | 128K |
| Input $ / M tokens | $0.50 | $30 |
| Output $ / M tokens | $2.15 | $180 |
| Results tracked | 52 | 16 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), GPT-5.4 Pro: 43.2 (#90)
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| WeirdML | 41.6% | 57.4% |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), GPT-5.4 Pro: —
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning GPT-5.4 Pro leads
DeepSeek-R1: 18.6 (#278), GPT-5.4 Pro: 70.7 (#13)
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| ARC-AGI-2 | 1.3% | 83.3% |
| SimpleBench | 40.8% | 74.1% |
| ARC-AGI-1 | 21.2% | 94.5% |
| CritPt | 1.1% | 30% |
| Epoch Capabilities Index | 141.29 | 158.93 |
| Kagi LLM Benchmark | 69.4% | — |
| Chess Puzzles | — | 58.6% |
| EnigmaEval | — | 23.8% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math GPT-5.4 Pro leads
DeepSeek-R1: 43.8 (#79), GPT-5.4 Pro: 72.4 (#22)
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 82.5% |
| FrontierMath Tier 4 | — | 58.5% |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath (Feb 2025 set) | — | 50% |
| FrontierMath Tier 4 (v1) | — | 37.5% |
Knowledge GPT-5.4 Pro leads
DeepSeek-R1: 44.5 (#87), GPT-5.4 Pro: 68.3 (#7)
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| GPQA Diamond | 76.3% | 94.6% |
| Vectara Hallucination Rate | 11.3% | 8.3% |
| Humanity's Last Exam | — | 44.3% |
| SimpleQA Verified | — | 46.3% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual Not comparable
DeepSeek-R1: 52.4 (#85), GPT-5.4 Pro: —
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1442 | — |
| LMArena French | 1417 | — |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1411 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.0 (#143), GPT-5.4 Pro: —
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |
Long Context Not comparable
DeepSeek-R1: 45.4 (#36), GPT-5.4 Pro: —
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |
Writing & Preference Not comparable
DeepSeek-R1: 61.4 (#88), GPT-5.4 Pro: —
| Benchmark | DeepSeek-R1 | GPT-5.4 Pro |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1405 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1405 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GPT-5.4 Pro?
GPT-5.4 Pro is the stronger model overall, scoring 58.9 to 42.3 on the Noometry Index. DeepSeek-R1 costs 74× less per token, which makes it the better buy when GPT-5.4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or GPT-5.4 Pro?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.4 Pro lists at $30 and $180.
Is DeepSeek-R1 or GPT-5.4 Pro better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 43.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 Pro does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-R1 and GPT-5.4 Pro share?
8 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5.4 Pro has 16.