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Build more natural voice experiences with GPT‑Live‑1 in the API
OpenAI releases GPT-Live-1 in the API, a full-duplex voice model that handles interruptions natively and delegates reasoning to backend models.
OpenAI launched GPT-Live-1 in the API, a single-model full-duplex voice system that listens and speaks simultaneously, replacing chained STT-LLM-TTS architectures. It improves Full Duplex Bench performance by 30 percentage points over GPT-Realtime-2.1 and ranks #1 on Tau3 when paired with GPT-6 Astra at medium reasoning effort. Early partner Speak reported nearly 80% fewer interruptions in language tutoring. The API release costs $0.05 per minute for the front-end voice layer and supports telephony, native ASR transcripts, keyword biasing, and expanded voice and language options.
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ChatGPT Images 2.5: Faster, more precise, but not the same for everyone
OpenAI released GPT-Image-2.5 (Flare and Sunburst variants), cutting image generation latency up to 50% and improving multi-round edit consistency.
OpenAI launched GPT-Image-2.5 in two API variants: Flare, the faster default with higher quality than GPT-Image-2 at up to 50% lower latency, and Sunburst, built for precise multi-round edits. Both cost $8 per million input and $30 per million output tokens, with new xhigh and max quality tiers; a max-tier 1024x1024 image runs roughly $0.21. Testing found edit consistency strong in ChatGPT Work but inconsistent in Chat, and OpenAI has not documented how ChatGPT routes users between the models.
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HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving
HoneyRoute detects malicious LLM serving requests and diverts them to a honeypot model, reaching F1 0.911 with 38 ms median added latency.
HoneyRoute is an inference-serving layer pairing a streaming router (a frozen 0.8B embedding backbone with per-domain MLP heads) with a dual-implementation honeypot and an analysis loop that converts trapped interactions into attacker fingerprints for router retraining. On a production trace plus a seven-domain attack corpus it matches 96% of a two-tier guard-LLM cascade's F1 at 1/385th of its latency with 0% evasion under 13 adversarial transformations. Diverting malicious traffic cuts production token consumption under GCG-suffix flooding by 97.8%, and loop training raises detection F1 to 0.933.
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PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents
PARSER uses parallel reader subagents and an RL-trained lead agent for long-context QA, beating baselines and cutting latency up to 11x.
The PARSER paper decouples reading from reasoning: frozen subagents each read one document chunk in parallel while an RL-optimized lead agent iteratively broadcasts queries and aggregates evidence in scatter-gather rounds. On multi-hop QA with 7K to 896K token contexts, a 4B-backbone PARSER beats the strongest sequential memory baseline by 5.7 points on average and 12.0 points at 896K tokens, and a 9B version surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments show robustness to evidence position, order, and distance perturbations, with inference latency reduced by up to 11x.
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ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding
ShallowStream builds streaming-video retrieval indexes from shallow MLLM layers, cutting per-frame prefill latency by up to 52.1x.
ShallowStream is a framework for streaming video understanding with multimodal LLMs that uses the model's shallow layers to simultaneously encode frames and maintain an always-on lightweight retrieval index via shallow-layer KV caches, avoiding full-depth prefill for every incoming frame. At query time, shallow-layer attention scores plus a diversity-aware selection strategy retrieve relevant context frames. It reports performance on par with the strongest existing streaming methods while reducing per-frame prefill latency by up to 52.1x and 10-second end-to-end latency by up to 11.9x, with code released on GitHub.
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