Model comparison
DeepSeek-R1 vs GPT-6.1 Sol
DeepSeek-R1 has enough public results to be ranked (#110); GPT-6.1 Sol does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in coding, where GPT-6.1 Sol leads 51.4 to 45.9.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-R1 | GPT-6.1 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.0 | 42.1 |
| Released | 2025-01-20 | 2026-09-29 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 64K | 128K |
| Input $ / M tokens | $0.50 | $2 |
| Output $ / M tokens | $2.15 | $10 |
| Results tracked | 52 | 2 |
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Category by category
Coding GPT-6.1 Sol leads
DeepSeek-R1: 45.9 (#63), GPT-6.1 Sol: 51.4
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| DeepSWE | — | 75.2% |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use GPT-6.1 Sol leads
DeepSeek-R1: 30.4 (#68), GPT-6.1 Sol: 34.3
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 32% |
| METR Time Horizons | 53.8% | — |
Reasoning Not comparable
DeepSeek-R1: 18.7 (#253), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| 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% | — |
| LMArena Hard Prompts | 1415 | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.3 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math Not comparable
DeepSeek-R1: 42.0 (#70), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1401 | — |
| MATH Level 5 | 96.6% | — |
Knowledge Not comparable
DeepSeek-R1: 44.3 (#81), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |
Multilingual Not comparable
DeepSeek-R1: 52.6 (#80), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1412 | — |
| LMArena Chinese | 1443 | — |
| LMArena French | 1415 | — |
| LMArena German | 1405 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1359 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1410 | — |
Instruction Following Not comparable
DeepSeek-R1: 72.2 (#138), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |
Long Context Not comparable
DeepSeek-R1: 45.4 (#36), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |
Writing & Preference Not comparable
DeepSeek-R1: 61.5 (#84), GPT-6.1 Sol: —
| Benchmark | DeepSeek-R1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1428 | — |
| LMArena Creative Writing | 1406 | — |
| Short-Story Creative Writing | 83% | — |
| EQ-Bench Creative Writing | 1500 | — |
| WildBench | 82.8% | — |
| LMArena Multi-Turn | 1404 | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than GPT-6.1 Sol?
DeepSeek-R1 has enough public results to be ranked (#110); GPT-6.1 Sol does not yet, so treat this comparison as directional.
Which is cheaper, DeepSeek-R1 or GPT-6.1 Sol?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is DeepSeek-R1 or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 51.4 versus 45.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-R1 and GPT-6.1 Sol share?
0 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-6.1 Sol has 2.