Reviewing an old AI image tutorial without its date can lead to paying for the wrong model—or calling an endpoint that no longer exists.
Scenario: A publisher updating a 2024–2026 image-generation tutorial, asset pipeline or course while preserving historical attribution.
Scope and cutoff: Historical dates taken from official version and retirement pages; checked 25 October 2026. Not a quality leaderboard.
Evidence standard: These are vendor documents, not independent product tests. Unavailable claims are left unknown; plan prices are delegated to the AI 10 dated price ledger.
Evidence matrix
| Option | What is established | Evidence |
|---|---|---|
| Midjourney release history | Official chronology differentiates V6, V6.1, V7 and default V8.2 | Official documentation |
| OpenAI gpt-image-1 | Documentation explicitly reports 23 October 2026 shutdown | Official documentation |
| Migration output equivalence | No evidence of output equivalence across retired and current models | Unknown / not tested |
Keep release and availability distinct
Midjourney’s official version reference dates V6 to 20 December 2023, V6.1 to 30 July 2024, V7 to 3 April 2025, and V8.2 becoming the default on 24 July 2026. These are useful anchors for a historical guide, but a version’s original release is not the same as the date it became default. Tutorials should identify both when known. Midjourney’s v8.x Edit Model workflow also changes which reference and editing instructions make sense. A 2024 prompt technique may still teach composition while no longer documenting the latest UI.
An especially important retirement example
OpenAI’s current image API documentation explicitly notes that `gpt-image-1` was shut down on 23 October 2026 and lists newer GPT Image model identifiers. An archival article should not silently replace the old model name with the latest one, because that destroys the historical record; a buyer guide should warn that the obsolete endpoint can no longer be selected. Preserve the original tutorial date, add a migration note and independently verify newer model support, parameters and rate units in current documentation.
Migration exercise for creators
Build a small asset archive: original prompt, reference image license, target format, source model ID, generation date and finished output. Re-run three representative briefs under the currently offered model only when the source assets can lawfully be reused. Compare generated content for prompt adherence, subject consistency, revision effort and any text errors. Historical performance claims should not be rewritten as new test results. Where reproduction is impossible because a model was retired, label the comparison ‘not reproducible on cutoff date’ rather than scoring it as failure.
The buyer decision today
A team starting new work should prioritize supported models with actively documented edit paths; a museum or tutorial archive should preserve the historical model identity and add a prominent retirement note. Cross-vendor quality judgments still need blinded tests. Prices and account restrictions change separately from releases, so the exact price history belongs in AI 10’s time-stamped records. A stable canonical ID must distinguish image model versions from their broader family label and from the consumer application name.
What the decision depends on
Preserve exact historical model IDs and release dates; choose currently supported APIs for new work after rechecking compatibility.
Buyer verification checklist
- Was the version released or merely defaulted on that date?
- Is the original model still callable?
- Are the input assets and output rights retained?
- Has the tutorial been rechecked against current parameters?
What remains unknown
No independent hands-on scores, standardized ratings, guarantees of region-wide availability or current billable price comparisons are established here. Country, tier, device and actual contractual controls must be verified for the intended purchase.
Sources and Further Reading
- Midjourney: Version model history (accessed 2026-10-25)
- OpenAI: Image generation API model reference (accessed 2026-10-25)
Editorial note: source claims are mapped separately; no performance or personal testing is implied.