# GPT-5.5 vs Claude Opus 4.8

On provider list prices, Claude Opus 4.8 costs $5 per million input tokens against $5 for GPT-5.5: effectively level. Output is $25 against $30 (1.2x).

## Specifications

| | GPT-5.5 | Claude Opus 4.8 |
| --- | --- | --- |
| Lab | OpenAI | Anthropic |
| Access | API only | API only |
| Context window | 400K tokens | 200K tokens |
| List price, input | $5 / M tokens | $5 / M tokens |
| List price, output | $30 / M tokens | $25 / M tokens |
| Cached input | $0.50 / M tokens | n/a |
| 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 $14,750 a month on Claude Opus 4.8 and $16,500 on GPT-5.5 at list: a gap of $1,750.

GPT-5.5 reads 400K tokens per request against 200K for Claude Opus 4.8, 2.0x the window. That decides which one can take whole documents without splitting them.

## Choose GPT-5.5 for

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

## Choose Claude Opus 4.8 for

- Hardest reasoning problems
- High-stakes analysis
- Escalation tier for agents

## Common questions

### Which is cheaper, GPT-5.5 or Claude Opus 4.8?

Claude Opus 4.8, on this workload shape. At list prices it is $5/$25 per million tokens in and out against $5/$30 for GPT-5.5. Billed on Allocate: $5.35/$26.75 against $5.35/$32.10.

### Which has the bigger context window?

GPT-5.5: 400,000 tokens (400K) against 200,000 (200K) for Claude Opus 4.8.

### Can I fine-tune GPT-5.5 or Claude Opus 4.8?

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/gpt-5-5-vs-opus-4-8) · [GPT-5.5](https://allocate.network/models/gpt-5-5.md) · [Claude Opus 4.8](https://allocate.network/models/opus-4-8.md) · [Machine-readable catalog](https://allocate.network/catalog.json)