# DeepSeek-R1 vs GPT-5 Pro

> GPT-5 Pro is the stronger model overall, scoring 46.4 to 42.3 on the Noometry Index. DeepSeek-R1 costs 45× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-gpt-5-pro
- Last updated: 2026-10-10
- Shared benchmarks: 7

## Summary

- They share 7 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and GPT-5 Pro in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5 Pro leads 38.9 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-1: 21.2% for DeepSeek-R1 and 70.2% for GPT-5 Pro.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $15 / $120 for GPT-5 Pro.
- GPT-5 Pro accepts more context: 400K tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 46.4 |
| Rank | 115 | 64 |
| Context | 164K | 400K |
| Input $/M | $0.50 | $15 |
| Output $/M | $2.15 | $120 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- GPT-5 Pro: 44.0 (#80)

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| WeirdML | 41.6% | 60.4% |
| AlgoTune | 1.7 | 1.31 |
| Aider Polyglot | 71.4% | — |
| SciCode | 35.7% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| ALE-Bench | 804.12 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- GPT-5 Pro: —

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- GPT-5 Pro: 38.9 (#62)

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| ARC-AGI-2 | 1.3% | 18.3% |
| SimpleBench | 40.8% | 61.6% |
| Kagi LLM Benchmark | 69.4% | 76.8% |
| ARC-AGI-1 | 21.2% | 70.2% |
| Epoch Capabilities Index | 141.29 | 150.28 |
| CritPt | 1.1% | — |
| EnigmaEval | — | 18.8% |
| LiveBench Reasoning | 83.2% | — |
| LMArena Hard Prompts | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- GPT-5 Pro: 48.5 (#63)

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 19.5% |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath Tier 4 (v1) | — | 14.6% |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- GPT-5 Pro: 56.7 (#42)

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| Humanity's Last Exam | — | 31.6% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
| LMArena Expert | 1394 | — |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- GPT-5 Pro: —

| Benchmark | DeepSeek-R1 | GPT-5 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

- DeepSeek-R1: 72.0 (#143)
- GPT-5 Pro: —

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- GPT-5 Pro: —

| Benchmark | DeepSeek-R1 | GPT-5 Pro |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- GPT-5 Pro: —

| Benchmark | DeepSeek-R1 | GPT-5 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% | — |

## FAQ

### Is DeepSeek-R1 better than GPT-5 Pro?

GPT-5 Pro is the stronger model overall, scoring 46.4 to 42.3 on the Noometry Index. DeepSeek-R1 costs 45× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or GPT-5 Pro?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5 Pro lists at $15 and $120.

### Is DeepSeek-R1 or GPT-5 Pro better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 44.0 in the Noometry coding category.

### Which has the bigger context window?

GPT-5 Pro does, with 400K tokens against 164K.

### How many benchmarks do DeepSeek-R1 and GPT-5 Pro share?

7 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5 Pro has 12.