# DeepSeek-R1 vs GPT-5.2 Codex

> DeepSeek-R1 and GPT-5.2 Codex score almost the same on the Noometry Index (42.3 vs 42.6), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-gpt-5-2-codex
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. DeepSeek-R1 scores higher in 1 category and GPT-5.2 Codex in 1 category; one gap is clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 41.0 to 30.7.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.3 | 42.6 |
| Rank | 115 | 111 |
| Context | 164K | 400K |
| Input $/M | $0.50 | $1.75 |
| Output $/M | $2.15 | $14 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- GPT-5.2 Codex: 45.5 (#71)

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| ALE-Bench | 804.12 | 1,300 |
| SWE-bench Verified (bash only) | — | 72.8% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1339 |
| SWE-bench Multilingual | — | 66.3% |
| SciCode | 35.7% | — |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| LMArena Coding | 1427 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- GPT-5.2 Codex: 41.0 (#22)

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | — | 66.5% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| 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 | 1416 | — |
| LiveBench Data Analysis | 69.8% | — |
| Epoch Capabilities Index | 141.29 | — |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1400 | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| 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-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| 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-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
| LMArena Instruction Following | 1382 | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| Fiction.LiveBench | 75% | — |
| LMArena Longer Query | 1391 | — |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek-R1 | GPT-5.2 Codex |
|---|---|---|
| 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-5.2 Codex?

DeepSeek-R1 and GPT-5.2 Codex score almost the same on the Noometry Index (42.3 vs 42.6), so choose on price, context window or the category you care about most.

### Which is cheaper, DeepSeek-R1 or GPT-5.2 Codex?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

### Is DeepSeek-R1 or GPT-5.2 Codex better for coding?

They score almost the same on coding (46.3 vs 45.5); test both on your own repository before choosing.

### Which has the bigger context window?

GPT-5.2 Codex does, with 400K tokens against 164K.

### How many benchmarks do DeepSeek-R1 and GPT-5.2 Codex share?

1 benchmark has published results for both models. DeepSeek-R1 has 52 scored results on Noometry and GPT-5.2 Codex has 5.