# DeepSeek-R1 vs GPT-5.4

> GPT-5.4 is the stronger model overall, scoring 59.4 to 42.3 on the Noometry Index. DeepSeek-R1 costs 6.2× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-gpt-5-4
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. DeepSeek-R1 scores higher in 0 categories and GPT-5.4 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.4 leads 61.8 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-2: 1.3% for DeepSeek-R1 and 74% for GPT-5.4.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $2.50 / $15 for GPT-5.4.
- GPT-5.4 accepts more context: 1.05M tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 59.4 |
| Rank | 115 | 16 |
| Context | 164K | 1.05M |
| Input $/M | $0.50 | $2.50 |
| Output $/M | $2.15 | $15 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- GPT-5.4: 52.6 (#33)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| SciCode | 35.7% | 56.6% |
| WeirdML | 41.6% | 77.7% |
| LMArena Coding | 1427 | 1497 |
| ALE-Bench | 804.12 | 1,607 |
| AlgoTune | 1.7 | 1.85 |
| SWE-bench Verified | — | 76.9% |
| DeepSWE | — | 51.8% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1465 |
| GSO | — | 31.4% |
| LiveBench Coding | 66.7% | — |
| MirrorCode | — | 15.6% |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- GPT-5.4: 46.5 (#13)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| DeepResearch Bench | 35.1% | 35.1% |
| METR Time Horizons | 53.8% | 74.3% |
| Terminal-Bench | — | 81.8% |
| APEX-Agents | — | 52.4% |
| τ²-bench Banking | — | 39.4% |
| PostTrainBench | — | 19% |
| BALROG | 34.9% | — |
| GBAEval | — | 45.1% |
| LMArena Search | — | 1197 |
| Vending-Bench 2 | — | 6,144 |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- GPT-5.4: 61.8 (#19)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| ARC-AGI-2 | 1.3% | 74% |
| Kagi LLM Benchmark | 69.4% | 63.8% |
| ARC-AGI-1 | 21.2% | 93.7% |
| CritPt | 1.1% | 23.4% |
| LMArena Hard Prompts | 1416 | 1485 |
| Epoch Capabilities Index | 141.29 | 156.81 |
| ForecastBench | 60 | 59.5 |
| SimpleBench | 40.8% | — |
| NYT Connections (extended) | — | 91.3% |
| Chess Puzzles | — | 44% |
| EnigmaEval | — | 16% |
| Thematic Generalization | — | 80% |
| EBR-Bench | — | 25.4% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 37% |
| DTBench | — | 94.4% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 52% |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- GPT-5.4: 73.5 (#19)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 97.8% |
| LMArena Math | 1400 | 1488 |
| FrontierMath (Tiers 1-3) | — | 78.6% |
| FrontierMath Tier 4 | — | 49% |
| MathArena Final-Answer Competitions | — | 83.1% |
| ProofBench | — | 56% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
| FrontierMath (Feb 2025 set) | — | 47.6% |
| FrontierMath Tier 4 (v1) | — | 27.1% |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- GPT-5.4: 65.3 (#14)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| GPQA Diamond | 76.3% | 93.3% |
| Vectara Hallucination Rate | 11.3% | 7% |
| LMArena Expert | 1394 | 1507 |
| Humanity's Last Exam | — | 36.2% |
| SimpleQA Verified | — | 45.1% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- GPT-5.4: 43.7 (#20)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| LMArena Vision | — | 1303 |
| Blueprint-Bench 2 | — | 27.1% |
| Furniture Assembly | — | 37.5% |
| LMArena Document | — | 1471 |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- GPT-5.4: 56.2 (#23)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| LMArena Non-English | 1412 | 1465 |
| LMArena Chinese | 1442 | 1519 |
| LMArena French | 1417 | 1493 |
| LMArena German | 1404 | 1472 |
| LMArena Japanese | 1391 | 1485 |
| LMArena Korean | 1360 | 1448 |
| LMArena Russian | 1423 | 1480 |
| LMArena Spanish | 1411 | 1454 |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- GPT-5.4: 77.1 (#27)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1469 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- GPT-5.4: 50.3 (#8)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| LMArena Longer Query | 1391 | 1473 |
| Fiction.LiveBench | 75% | — |
| CL-bench | — | 27.9% |
| CL-bench Life | — | 21.7% |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- GPT-5.4: 71.9 (#17)

| Benchmark | DeepSeek-R1 | GPT-5.4 |
|---|---|---|
| LMArena Text | 1428 | 1469 |
| LMArena Creative Writing | 1405 | 1439 |
| EQ-Bench Creative Writing | 1500 | 1840 |
| LMArena Multi-Turn | 1405 | 1482 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| EQ-Bench 4 | — | 1272 |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than GPT-5.4?

GPT-5.4 is the stronger model overall, scoring 59.4 to 42.3 on the Noometry Index. DeepSeek-R1 costs 6.2× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or GPT-5.4?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.4 lists at $2.50 and $15.

### Is DeepSeek-R1 or GPT-5.4 better for coding?

GPT-5.4 scores higher on coding benchmarks: 52.6 versus 46.3 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.4 does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-R1 and GPT-5.4 share?

33 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5.4 has 68.