# DeepSeek V4.1 Flash vs GPT-5.2 Codex

> DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 42.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v4-1-flash-vs-gpt-5-2-codex
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 1 category and GPT-5.2 Codex in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where DeepSeek V4.1 Flash leads 52.8 to 45.4.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 400K.
- DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 50.3 | 42.2 |
| Rank | 40 | 107 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $1.75 |
| Output $/M | $0.60 | $14 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek V4.1 Flash: 52.8 (#29)
- GPT-5.2 Codex: 45.4 (#66)

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena WebDev | 1620 | 1339 |
| ALE-Bench | 1,092 | 1,300 |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.3% |
| SciCode | 51.9% | — |
| LMArena Coding | 1511 | — |

## Agentic & Tool Use

- DeepSeek V4.1 Flash: —
- GPT-5.2 Codex: 40.7 (#20)

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | — | 66.5% |

## Reasoning

- DeepSeek V4.1 Flash: 46.2 (#39)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| LMArena Hard Prompts | 1485 | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| Epoch Capabilities Index | 155.01 | — |

## Math

- DeepSeek V4.1 Flash: 48.4 (#52)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| ProofBench | 54% | — |
| LMArena Math | 1493 | — |

## Knowledge

- DeepSeek V4.1 Flash: 41.6 (#97)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Expert | 1505 | — |

## Multilingual

- DeepSeek V4.1 Flash: 55.5 (#29)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Non-English | 1452 | — |
| LMArena Chinese | 1506 | — |
| LMArena French | 1448 | — |
| LMArena Russian | 1473 | — |
| LMArena Spanish | 1499 | — |

## Instruction Following

- DeepSeek V4.1 Flash: 77.6 (#24)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Instruction Following | 1475 | — |

## Long Context

- DeepSeek V4.1 Flash: 45.2 (#46)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Longer Query | 1476 | — |

## Writing & Preference

- DeepSeek V4.1 Flash: 65.8 (#43)
- GPT-5.2 Codex: —

| Benchmark | DeepSeek V4.1 Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Text | 1465 | — |
| LMArena Creative Writing | 1436 | — |
| EQ-Bench Creative Writing | 1540 | — |
| LMArena Multi-Turn | 1460 | — |

## FAQ

### Is DeepSeek V4.1 Flash better than GPT-5.2 Codex?

DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 42.2 on the Noometry Index.

### Which is cheaper, DeepSeek V4.1 Flash or GPT-5.2 Codex?

DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

### Is DeepSeek V4.1 Flash or GPT-5.2 Codex better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.8 versus 45.4 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 400K.

### How many benchmarks do DeepSeek V4.1 Flash and GPT-5.2 Codex share?

2 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and GPT-5.2 Codex has 5.