CATCH #1246
DETECTED SEP 9, 2026 00:55 UTC
THE CATCHscore 10/10
EXHIBIT 1246 · FLASH
Sep 9, 2026 00:55 UTC
pypi
openai
OpenAI's latest SDK includes references to new 'gpt-image-2.5-flare' and 'gpt-image-2.5-sunburst' models, suggesting upcoming image generation capabilities, alongside new 'realtime' and 'responses' beta API connections.
READY TO POST:
openai's new sdk hints at upcoming image generation models: gpt-image-2.5-flare and gpt-image-2.5-sunburst. looks like new creative ai is on the horizon!
fresh openai sdk update just dropped, featuring 'realtime' and 'responses' beta connections. also, are those new gpt-image models for real? interesting stuff for devs.
EVIDENCE — the receipts
openai 3.9.0 -> 3.10.0: +91 strings, -571 strings, 42 interesting
[model_id] (5)
+ gpt-image-2.5-flare
+ gpt-image-2.5-flare-2026-09-08
+ gpt-image-2.5-sunburst
+ gpt-image-2.5-sunburst-2026-09-08
+ py
with client.chat.completions.stream(
model="gpt-4o-2024-08-06",
messages=[...],
) as stream:
for event in stream:
if event.type == "content.delta":
print(event.delta, flush=True, end="")
[feature_flag] (4)
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
[sentence] (38)
+ py
with client.chat.completions.stream(
model="gpt-4o-2024-08-06",
messages=[...],
) as stream:
for event in stream:
if event.type == "content.delta":
print(event.delta, flush=True, end="")
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.realtime.connect(...).enter()
# ...
connection.close()
+ py
connection = client.beta.responses.connect(...).enter()
# ...
connection.close()
+ - Based on your data it seems like you're trying to fine-tune a model for {ft_type}
- For classification, we recommend you try one of the faster and cheaper models, such as `ada`
- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used fo
+ - There are {len(long_indexes)} examples
caught by the 20-minute sweep ·
the live wire · all receipts