Is GPT-5 cheaper than Claude Opus 4.7?

On input tokens, GPT-5 is cheaper ($1.25 vs $5.00 per million). On output tokens, GPT-5 is cheaper. Total cost on a typical workload depends on your input:output ratio.

Which has the larger context window, GPT-5 or Claude Opus 4.7?

Claude Opus 4.7 has a 1,000,000-token context window. GPT-5 fits 400,000 tokens.

How do I switch from GPT-5 to Claude Opus 4.7?

Update the model id in your API call: replace "gpt-5" with "claude-opus-4.7". Then verify cost impact on a sample of your real prompts using a token counter.

GPT-5 vs Claude Opus 4.7

Side-by-side comparison of GPT-5 (OpenAI) and Claude Opus 4.7 (Anthropic). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.

Specifications

Spec	GPT-5	Claude Opus 4.7
Provider	OpenAI	Anthropic
Model id	gpt-5	claude-opus-4.7
Input price (per 1M tokens)	$1.25	$5.00
Output price (per 1M tokens)	$10.00	$25.00
Context window	400,000	1,000,000
Output speed (tokens/sec)	~110	~30

Cost on real prompts

Total cost = (input tokens × input price) + (output tokens × output price). Numbers below use the exact pricing tables published by each provider.

Scenario	Input	Output	GPT-5	Claude Opus 4.7	Cheaper
Short question + answer	50	150	$0.001563	$0.004000	GPT-5
Code review on one file	500	1,500	$0.015625	$0.040000	GPT-5
Long document summary	5,000	500	$0.011250	$0.037500	GPT-5
Heavy reasoning task	2,000	8,000	$0.082500	$0.210000	GPT-5
Full codebase analysis	50,000	10,000	$0.162500	$0.500000	GPT-5

Want the exact cost for your prompt instead of these examples? Open the cost calculator pre-loaded with both models →

When to pick which

Heuristics derived from the spec table above. Always validate on your own prompts before committing — these are starting points, not verdicts.

Pick GPT-5 for

•output-heavy workloads (long-form generation, code, summaries) — gpt-5 is meaningfully cheaper per output token
•input-heavy workloads (long context, RAG, document QA) — gpt-5 is cheaper per input token
•latency-sensitive UX (chat, autocompletion) — gpt-5 streams faster (~110 vs ~30 tok/s)

Pick Claude Opus 4.7 for

•tasks needing a larger context window — claude-opus-4.7 fits 3x more tokens than gpt-5
•complex multi-step reasoning — claude-opus-4.7 uses chain-of-thought internally

Switching between them

For most use cases, switching providers means updating the model id and the request shape if the providers differ. Within the same provider, it's usually a single-line change.

From GPT-5 to Claude Opus 4.7

# Before
model = "gpt-5"

# After
model = "claude-opus-4.7"

If the providers differ (OpenAI vs Anthropic), you'll also need to swap the SDK / endpoint URL. Cross-provider migrations usually take 30 minutes to a few hours depending on how many features (streaming, function calling, tool use) you depend on.

Calculate cost on your own prompt

The examples above use generic input/output ratios. For an exact comparison, paste your real prompt into the calculator — it counts tokens with the right tokenizer for each model and shows side-by-side cost.

Open the calculator with both models →