#Microsoft

AIAnthropic has released Claude Haiku 5.5, its cheapest and fastest small model, priced at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens. It keeps a 1M token context window, up to 128K output tokens, and is generally available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS. Anthropic reports 72.4% on OSWorld 2.1 (offline subset) versus 15.7% for Haiku 4.5, and the article notes that non-default temperature, top_p or top_k values return a 400 error.

AIAnthropic has released Claude Haiku 5.5, available now across all platforms including Amazon Web Services, Google Cloud, and Microsoft Azure. The post links to Anthropic's announcement page for further details.

AIMicrosoft AI launched MAI-Transcribe-2-Streaming, which Artificial Analysis ranks #1 of 38 models for final transcript accuracy at 2.5% WER, returned 0.13s after end of speech. Artificial Analysis lists its streaming price at $0.54 per hour of audio, at the higher end among leading streaming models. Microsoft's post claims the model is 55% faster and 60% cheaper than ElevenLabs and invites developers to build agents on its platform.

AIMustafa Suleyman says Microsoft launched eight new models in two months, and five debuted at #1, including MAI-Transcribe-2 and MAI-Image 2.5, 2.6, and 2.6 Flash on the Artificial Analysis leaderboard. He says MAI-Cyber in MDASH ranked #1 on CyberGym while cutting costs by 50% versus other frontier models. He adds that MAI-Code-1.1-Flash, launched in GitHub Copilot four weeks ago, now accounts for a third of small-model traffic there.

AIMicrosoft AI's MAI-Image-2.6 and the new MAI-Image-2.6-Flash are reported to set a new quality/price Pareto frontier for image editing on Artificial Analysis. Both models are now available in Microsoft Foundry.

AIMicrosoft Research released GigaPath-Flash and GigaTIME-Flash, efficient pathology foundation models built on a distilled ViT-S backbone and released under the Apache 2.0 license. GigaPath-Flash, with 22M-parameter tile and 21M-parameter slide encoders, reportedly scores within 3% of the original GigaPath on PANDA and EBRAINS benchmarks at roughly 50 times less compute. The models are research tools, not validated for clinical use.