# DeepSeek V4 vs GPT-5.5

On provider list prices, DeepSeek V4 costs $1.74 per million input tokens against $5 for GPT-5.5: 2.9x apart. Output is $3.48 against $30 (8.6x).

## Specifications

| | DeepSeek V4 | GPT-5.5 |
| --- | --- | --- |
| Lab | Deepseek | OpenAI |
| Access | API only | API only |
| Context window | 512K tokens | 400K tokens |
| List price, input | $1.74 / M tokens | $5 / M tokens |
| List price, output | $3.48 / M tokens | $30 / M tokens |
| Cached input | $0.20 / M tokens | $0.50 / M tokens |
| License | Proprietary API | Proprietary API |
| Fine-tunable | No | No |

Specifications and provider list prices from the Allocate catalog, checked 2026-07-21.

## What the numbers say

Take 1,000,000 requests a month at 1,200 input and 350 output tokens each. That workload costs $3,306 a month on DeepSeek V4 and $16,500 on GPT-5.5 at list: a gap of $13,194, or 5.0x.

DeepSeek V4 reads 512K tokens per request against 400K for GPT-5.5, 1.3x the window. That decides which one can take whole documents without splitting them.

## Choose DeepSeek V4 for

- Reasoning-heavy agents
- Long-document analysis
- Cost-sensitive production routes

## Choose GPT-5.5 for

- Complex tool-using agents
- Code generation
- General assistants

## Common questions

### Which is cheaper, DeepSeek V4 or GPT-5.5?

DeepSeek V4, on this workload shape. At list prices it is $1.74/$3.48 per million tokens in and out against $5/$30 for GPT-5.5. Billed on Allocate: $1.86/$3.72 against $5.35/$32.10.

### Which has the bigger context window?

DeepSeek V4: 512,000 tokens (512K) against 400,000 (400K) for GPT-5.5.

### Can I fine-tune DeepSeek V4 or GPT-5.5?

No. Both are closed models served over API. If you want a model you can train and own, start from an open-weights base in the catalog.

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[HTML page](https://allocate.network/compare/deepseek-v4-vs-gpt-5-5) · [DeepSeek V4](https://allocate.network/models/deepseek-v4.md) · [GPT-5.5](https://allocate.network/models/gpt-5-5.md) · [Machine-readable catalog](https://allocate.network/catalog.json)