# DeepSeek-V3.1 vs GPT-6.1 Sol

> DeepSeek-V3.1 has enough public results to be ranked (#106); GPT-6.1 Sol does not yet, so treat this comparison as directional.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-gpt-6-1-sol
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in coding, where GPT-6.1 Sol leads 51.4 to 39.8.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 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.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.2 | 42.1 |
| Rank | 106 | — |
| Context | 164K | 1.05M |
| Input $/M | $0.25 | $2 |
| Output $/M | $0.95 | $10 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- GPT-6.1 Sol: 51.4

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| DeepSWE | — | 75.2% |
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- GPT-6.1 Sol: 34.3

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| GDP.pdf | — | 32% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Math | 1420 | — |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1400 | — |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- GPT-6.1 Sol: —

| Benchmark | DeepSeek-V3.1 | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |

## FAQ

### Is DeepSeek-V3.1 better than GPT-6.1 Sol?

DeepSeek-V3.1 has enough public results to be ranked (#106); GPT-6.1 Sol does not yet, so treat this comparison as directional.

### Which is cheaper, DeepSeek-V3.1 or GPT-6.1 Sol?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GPT-6.1 Sol lists at $2 and $10.

### Is DeepSeek-V3.1 or GPT-6.1 Sol better for coding?

GPT-6.1 Sol scores higher on coding benchmarks: 51.4 versus 39.8 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-V3.1 and GPT-6.1 Sol share?

0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GPT-6.1 Sol has 2.