Model comparison
DeepSeek-R1 vs GPT-4.1 mini
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 33.6 on the Noometry Index.
Last verified . 35 shared benchmarks.
DeepSeekSummary
- They share 35 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and GPT-4.1 mini in 2 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-R1 leads 43.8 to 24.1.
- The biggest single-benchmark swing is Aider Polyglot: 71.4% for DeepSeek-R1 and 32.4% for GPT-4.1 mini.
- GPT-4.1 mini is cheaper at $0.40 / $1.60 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-R1 | GPT-4.1 mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 33.6 |
| Released | 2025-01-20 | 2025-04-14 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 64K | 33K |
| Input $ / M tokens | $0.50 | $0.40 |
| Output $ / M tokens | $2.15 | $1.60 |
| Results tracked | 52 | 47 |
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Category by category
Coding DeepSeek-R1 leads
DeepSeek-R1: 46.3 (#68), GPT-4.1 mini: 30.6 (#293)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| Aider Polyglot | 71.4% | 32.4% |
| SciCode | 35.7% | 40.4% |
| WeirdML | 41.6% | 37.6% |
| LMArena Coding | 1427 | 1367 |
| SWE-bench Verified (bash only) | — | 23.9% |
| BigCodeBench Instruct | — | 48.9% |
| LiveBench Coding | 66.7% | — |
| CadEval | — | 16% |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use GPT-4.1 mini leads
DeepSeek-R1: 30.7 (#75), GPT-4.1 mini: 33.3 (#55)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 50.5% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek-R1 leads
DeepSeek-R1: 18.6 (#278), GPT-4.1 mini: 10.8 (#340)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| ARC-AGI-2 | 1.3% | 0% |
| Kagi LLM Benchmark | 69.4% | 48.6% |
| ARC-AGI-1 | 21.2% | 3.5% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1416 | 1349 |
| Epoch Capabilities Index | 141.29 | 135.01 |
| SimpleBench | 40.8% | — |
| Chess Puzzles | — | 7% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 21.1% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek-R1 leads
DeepSeek-R1: 43.8 (#79), GPT-4.1 mini: 24.1 (#270)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 44.7% |
| Omni-MATH | 42.4% | 49.1% |
| LMArena Math | 1400 | 1343 |
| MATH Level 5 | 96.6% | 87.3% |
| FrontierMath (Tiers 1-3) | — | 6.7% |
| LiveBench Math | 80.7% | — |
| FrontierMath (Feb 2025 set) | — | 4.5% |
Knowledge DeepSeek-R1 leads
DeepSeek-R1: 44.5 (#87), GPT-4.1 mini: 34.7 (#194)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| GPQA Diamond | 76.3% | 65.8% |
| MMLU-Pro | 79.3% | 78.3% |
| GPQA (HELM) | 66.6% | 61.4% |
| LMArena Expert | 1394 | 1338 |
| SimpleQA Verified | — | 12.7% |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
Multimodal Not comparable
DeepSeek-R1: —, GPT-4.1 mini: 35.8 (#82)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | — | 1181 |
Multilingual DeepSeek-R1 leads
DeepSeek-R1: 52.4 (#85), GPT-4.1 mini: 45.7 (#166)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | 1412 | 1318 |
| LMArena Chinese | 1442 | 1329 |
| LMArena French | 1417 | 1358 |
| LMArena German | 1404 | 1351 |
| LMArena Japanese | 1391 | 1290 |
| LMArena Korean | 1360 | 1298 |
| LMArena Russian | 1423 | 1324 |
| LMArena Spanish | 1411 | 1319 |
Instruction Following GPT-4.1 mini leads
DeepSeek-R1: 72.0 (#143), GPT-4.1 mini: 73.7 (#118)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| IFEval | 78.4% | 90.4% |
| LMArena Instruction Following | 1382 | 1333 |
| LiveBench Instruction Following | 80.5% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), GPT-4.1 mini: 31.8 (#275)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| Fiction.LiveBench | 75% | 44.4% |
| LMArena Longer Query | 1391 | 1344 |
Writing & Preference DeepSeek-R1 leads
DeepSeek-R1: 61.4 (#88), GPT-4.1 mini: 48.6 (#199)
| Benchmark | DeepSeek-R1 | GPT-4.1 mini |
|---|---|---|
| LMArena Text | 1428 | 1340 |
| LMArena Creative Writing | 1405 | 1300 |
| EQ-Bench Creative Writing | 1500 | 1147 |
| WildBench | 82.8% | 83.8% |
| LMArena Multi-Turn | 1405 | 1354 |
| Short-Story Creative Writing | 83% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GPT-4.1 mini?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 33.6 on the Noometry Index.
Which is cheaper, DeepSeek-R1 or GPT-4.1 mini?
GPT-4.1 mini is cheaper. It lists at $0.40 per million input tokens and $1.60 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or GPT-4.1 mini better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-R1 and GPT-4.1 mini share?
35 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-4.1 mini has 47.