Model comparison
DeepSeek-R1 vs GPT-5.4 mini
GPT-5.4 mini is the stronger model overall, scoring 44.8 to 42.0 on the Noometry Index. DeepSeek-R1 costs 1.8× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
DeepSeekSummary
- They share 31 benchmarks with published results for both. DeepSeek-R1 scores higher in 4 categories and GPT-5.4 mini in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 mini leads 30.3 to 18.7.
- The biggest single-benchmark swing is ARC-AGI-1: 21.2% for DeepSeek-R1 and 63.7% for GPT-5.4 mini.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $0.75 / $4.50 for GPT-5.4 mini.
- GPT-5.4 mini accepts more context: 400K tokens versus 164K.
Side by side
| DeepSeek-R1 | GPT-5.4 mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.0 | 44.8 |
| Released | 2025-01-20 | 2026-03-17 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 400K |
| Max output | 64K | 128K |
| Input $ / M tokens | $0.50 | $0.75 |
| Output $ / M tokens | $2.15 | $4.50 |
| Results tracked | 52 | 46 |
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Category by category
Coding Too close to call
DeepSeek-R1: 45.9 (#63), GPT-5.4 mini: 45.0 (#67)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| SciCode | 35.7% | 49.9% |
| WeirdML | 41.6% | 60.3% |
| LMArena Coding | 1427 | 1438 |
| ALE-Bench | 804.12 | 1,189 |
| FrontierCode | — | 27% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1397 |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Too close to call
DeepSeek-R1: 30.4 (#68), GPT-5.4 mini: 29.6 (#74)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| DeepResearch Bench | 35.1% | 36.3% |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning GPT-5.4 mini leads
DeepSeek-R1: 18.7 (#253), GPT-5.4 mini: 30.3 (#77)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| ARC-AGI-2 | 1.3% | 18.9% |
| Kagi LLM Benchmark | 69.4% | 37.9% |
| ARC-AGI-1 | 21.2% | 63.7% |
| CritPt | 1.1% | 10% |
| LMArena Hard Prompts | 1415 | 1424 |
| Epoch Capabilities Index | 141.3 | 148.83 |
| ForecastBench | 60 | 57 |
| SimpleBench | 40.8% | — |
| NYT Connections (extended) | — | 61.8% |
| Chess Puzzles | — | 24% |
| Thematic Generalization | — | 61.7% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 11% |
| DTBench | — | 80% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 40.8% |
| LiveBench | 71.6% | — |
Math GPT-5.4 mini leads
DeepSeek-R1: 42.0 (#70), GPT-5.4 mini: 45.4 (#63)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 88.9% |
| LMArena Math | 1401 | 1418 |
| FrontierMath (Tiers 1-3) | — | 51.2% |
| FrontierMath Tier 4 | — | 9.8% |
| ProofBench | — | 21% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath (Feb 2025 set) | — | 28.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge GPT-5.4 mini leads
DeepSeek-R1: 44.3 (#81), GPT-5.4 mini: 51.4 (#62)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| GPQA Diamond | 76.3% | 86.9% |
| Vectara Hallucination Rate | 11.3% | 5.5% |
| LMArena Expert | 1394 | 1434 |
| SimpleQA Verified | — | 29.4% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, GPT-5.4 mini: 39.0 (#51)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Too close to call
DeepSeek-R1: 52.6 (#80), GPT-5.4 mini: 52.1 (#94)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| LMArena Non-English | 1412 | 1405 |
| LMArena Chinese | 1443 | 1446 |
| LMArena French | 1415 | 1440 |
| LMArena German | 1405 | 1407 |
| LMArena Japanese | 1394 | 1375 |
| LMArena Korean | 1359 | 1369 |
| LMArena Russian | 1423 | 1417 |
| LMArena Spanish | 1410 | 1403 |
Instruction Following GPT-5.4 mini leads
DeepSeek-R1: 72.2 (#138), GPT-5.4 mini: 74.3 (#99)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| LMArena Instruction Following | 1382 | 1405 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), GPT-5.4 mini: 42.9 (#107)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| LMArena Longer Query | 1391 | 1407 |
| Fiction.LiveBench | 75% | — |
Writing & Preference GPT-5.4 mini leads
DeepSeek-R1: 61.5 (#84), GPT-5.4 mini: 64.2 (#56)
| Benchmark | DeepSeek-R1 | GPT-5.4 mini |
|---|---|---|
| LMArena Text | 1428 | 1411 |
| LMArena Creative Writing | 1406 | 1370 |
| EQ-Bench Creative Writing | 1500 | 1665 |
| LMArena Multi-Turn | 1404 | 1429 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GPT-5.4 mini?
GPT-5.4 mini is the stronger model overall, scoring 44.8 to 42.0 on the Noometry Index. DeepSeek-R1 costs 1.8× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or GPT-5.4 mini?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.4 mini lists at $0.75 and $4.50.
Is DeepSeek-R1 or GPT-5.4 mini better for coding?
They score almost the same on coding (45.9 vs 45.0); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5.4 mini does, with 400K tokens against 164K.
How many benchmarks do DeepSeek-R1 and GPT-5.4 mini share?
31 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5.4 mini has 46.