Claude Sonnet 4.5 and the original GPT-5 API model are both legacy choices with near-term retirement dates. Sonnet 4.5 is scheduled to retire November 30, 2026; GPT-5 is scheduled to shut down December 11, 2026. Existing users should compare migration paths before spending time declaring an overall winner between the old endpoints. Claude deprecations, GPT-5 documentation. OpenAI’s dated retirement notices.
Researched and written by GPT, an AI, from cited primary sources. Recommendations are editorial assessments; no hands-on performance test is claimed. Numerical examples are calculations under stated assumptions.
Sources checked November 3, 2026.
Those dates concern the named models, not the closure of Claude or ChatGPT as products. Nor does an older GPT-5 benchmark describe every later GPT-5-series model. Preserve the version and serving environment when interpreting results.
Token prices favor GPT-5 under the stated assumptions
Sonnet 4.5 lists $3 per million input tokens and $15 per million output tokens. GPT-5 lists $1.25 and $10. For two million uncached input tokens plus 500,000 billed output tokens, the calculated bills are $13.50 and $7.50 respectively. Cache operations, tool charges, discounts, taxes and retries are excluded. Count billable reasoning as well as visible answer text. Sonnet 4.5 pricing, GPT-5 pricing.
This $6 difference is meaningful only alongside successful output. A model that needs more attempts or introduces a subtle regression may cost more per accepted patch even with lower rates. The same source text can also tokenize differently across vendors, so identical token totals are an illustration rather than a claim about identical requests.
The larger window is not a coding score
Sonnet 4.5 documents 200,000 context tokens and 64,000 maximum output tokens. GPT-5 lists a 400,000-token context and 128,000 maximum output. Both accept text and images and produce text. These ceilings describe capacity; they do not prove which model can understand a complicated repository.
For code changes, retrieval and the tool environment can dominate what the model actually sees. Comparing an agent with search, tests and a persistent workspace against a single pasted prompt is not a clean model comparison. Label those as different systems if that is what you are evaluating.
A useful migration test is a difficult bug
Select a bug with a reproducible failing case and an acceptance test. Include a misleading nearby implementation so the model must trace the cause rather than patch the first plausible file. Give each supported successor the same repository revision and permissions, and record its tool calls, test outcomes and unrelated edits.
Have a reviewer check correctness and maintainability separately from whether the response sounds confident. Preserve failed attempts; otherwise retries disappear from the cost comparison. A second test should cover a routine change, because a model that helps on rare hard bugs may be unnecessary for every edit.
The recommendation is to use this historical comparison to document what your old system depended on, then move to supported candidates with fresh checks. Do not assume a successor inherits the same prices, reasoning defaults or tool behavior. The best migration is the one that meets your actual quality threshold and remains supportable beyond the next retirement date.
Model references
Anthropic · Model library
Claude Sonnet 4.5
DeprecatedExact legacy Sonnet model, with a November 30 retirement date.
Author’s assessment; check the guide’s evidence and limitations.- Access
- API
- Input
- text, image
- Output
- text
- Context window
- 200,000 tokens
Catalog checked . Model specifications do not establish access through a particular app or subscription.
OpenAI · Model library
GPT-5
DeprecatedOriginal GPT-5 API endpoint, with a December 11 retirement date.
Author’s assessment; check the guide’s evidence and limitations.- Access
- API
- Input
- text, image
- Output
- text
- Context window
- 400,000 tokens
Catalog checked . Model specifications do not establish access through a particular app or subscription.
