Short answer: GPT-6 Astra is not a drop-in “quality upgrade” for GPT-5.5 or GPT-5.4. The practical comparison has three separate questions: what the older model can still do on its current endpoint, what Astra changes in capability and price, and whether a particular surface retired an older model. OpenAI’s current model pages list GPT-5.5 at $5 input/$0.50 cached/$30 output per million tokens and GPT-5.4 at $2.50/$0.25/$15, while Astra is $10/$1/$50 before its long-context and mode rules. Test a fixed workload and keep rollback available.
This page is an older-OpenAI-model comparison, not a migration runbook. Use the migration checklist for code changes, the Astra price guide for arithmetic and the cross-vendor comparison for Claude Fable 5.1 and Gemini 3.1 Pro.
Current documented model rows
| Model | Current documented fit | Context / max output | Standard API price per 1M | Reasoning |
|---|---|---|---|---|
| GPT-6 Astra | Hardest end-to-end reasoning, coding, research, documents and computer use. | 1,050,000 / 128,000 | $10 input / $1 cached / $12.50 cache write / $50 output | Low through max; no none setting. |
| GPT-5.5 | Current OpenAI professional/coding route when its capability and price fit. | 1,050,000 / 128,000 | $5 input / $0.50 cached / $30 output | None through xhigh. |
| GPT-5.4 | General-purpose reasoning, coding and agentic work on its documented route. | 1,050,000 / 128,000 | $2.50 input / $0.25 cached / $15 output | None through xhigh. |
Verify the current rows on the official Astra, GPT-5.5 and GPT-5.4 model pages. Rates, access, regional availability and product-surface behavior can differ; do not turn a model-page price into a ChatGPT or Codex subscription forecast.
Capability comparison, without a made-up ranking
| Dimension | Astra | GPT-5.5 | GPT-5.4 | Evaluation question |
|---|---|---|---|---|
| Context and output | 1.05M context, 128K output. | 1.05M context, 128K output. | 1.05M context, 128K output. | Does the task need the window, or only a relevant source pack? |
| Reasoning control | Low through max. | None through xhigh. | None through xhigh. | Which setting clears the rubric at acceptable latency? |
| Responses/tool direction | Current guidance emphasizes Responses, async tools, steering, compaction and hosted tools. | Current guidance supports Responses, tools, compaction and reasoning controls. | Current guidance supports Responses, computer use, tool search and compaction. | Do request fields, tool results and retries pass a contract test? |
| Modality | Text and image input; text output. | Text and image input; text output. | Text and image input; text output. | Is a provider’s modality relevant to the workflow? |
| Fine-tuning | Not supported | Not supported | Not supported | Can prompting and retrieval meet the acceptance bar? |
Equivalent context lengths do not mean equivalent behavior. Preserve the same instructions, source pack, tools and acceptance rubric, then compare correctness, groundedness, tool-call validity, latency, retries, output tokens and reviewer time. Do not quote an unqualified “smarter” or “faster” claim.
Published performance evidence is task-specific
OpenAI’s GPT-5.5 announcement reports vendor results against GPT-5.4: Terminal-Bench 2.0, 82.7 versus 75.1; Expert-SWE, 73.1 versus 68.5; GDPval wins/ties, 84.9 versus 83.0; OSWorld Verified, 78.7 versus 75.0; Toolathlon, 55.6 versus 54.6; BrowseComp, 84.4 versus 82.7; and SWE-Bench Pro, 58.6 versus 57.7. These are OpenAI-reported results on the stated evaluations, not a guarantee for your workload and not a direct Astra comparison. The announcement also notes memorization concerns around SWE-Bench Pro.
OpenAI’s Astra launch announcement publishes separate Astra comparisons, including Terminal-Bench 4 at 57.9 versus GPT-5.6 Sol at 37.3 and OSWorld 2.0 at 72.6 versus 65.7. The task names, harnesses and model generations differ from GPT-5.5’s announcement; do not merge their numbers into one leaderboard. The frontier comparison keeps the benchmark attribution and caveats visible.
What changed on August 31, 2026?
OpenAI’s Codex Help Center says GPT-5.4 and GPT-5.4 mini stopped being available in Codex for users signed in with a ChatGPT account on August 31, 2026. The same guidance says that retirement does not affect API-key traffic or Codex authenticated with your own API key. This is a surface-specific availability rule, not evidence that every GPT-5.4 endpoint disappeared.
In a ChatGPT-account Codex workspace, inspect saved defaults and team policy and use the documented GPT-5.6 Terra or Luna direction where applicable. For an API application, check the current model catalog and endpoint rather than applying the Codex notice automatically.
Price and long-context trade-offs
At base rates, Astra costs more per token than GPT-5.5 and GPT-5.4. Astra also documents a 2× input/cache and 1.5× output multiplier for requests above 272,000 input tokens, plus 50% Batch/Flex and 2× Fast where available. GPT-5.4 has its own long-context and regional rules; read its current model page. Google’s unrelated pricing tiers are covered in the Google pricing documentation. Compare a complete accepted-result job, including retries, tool calls and review, using the Astra arithmetic guide.
Compatibility checks before a switch
- Record the old model ID, endpoint, SDK version, prompt, tool schema and output parser.
- Run a model-ID-only comparison before changing the prompt or tool policy.
- Check sampling, log-probability, reasoning, structured-output and tool fields against current guidance.
- Verify output event handling and preserve response/tool-call IDs where the loop needs them.
- Compare quality, safety, latency, usage and human review on a fixed set.
- Keep a rollback route and publish only after the owner signs off.
Frequently asked questions
Does Astra automatically replace GPT-5.4?
No. The right route depends on endpoint, task, price, data controls and evaluation. The August 31 notice applies to a specific Codex sign-in surface.
Is GPT-5.5 cheaper than Astra?
Its documented base input and output rates are lower, but compare cache, long-context, mode, retries and review costs for the actual task.
Is this page a migration guide?
No. It explains compatibility, model differences and availability. Use the dedicated migration page for an implementation sequence and rollback checklist.
Bottom line
Keep three records separate: documented capability, published benchmark evidence and your own acceptance results. GPT-5.5 and GPT-5.4 remain meaningful baselines where their endpoint and economics fit; Astra is a premium candidate to test, not a universal replacement.
Model specifications and prices reflect the linked OpenAI pages accessed September 5, 2026. Benchmark figures are attributed to the cited OpenAI announcements.
