GPT-4o mini Audio vs Claude 4.5 Opus

1. Affordable multimodal audio model

• Extremely low-cost audio + text model for production-scale usage.
• Ideal for startups and high-volume traffic apps.

2. Fast real-time performance

• Low latency suitable for responsive voice assistants, AI phone bots, IVR flows, and audio chat apps.
• Great when speed matters more than deep reasoning.

3. Audio input and audio output

• Accepts raw audio (speech, recordings, commands).
• Generates natural audio responses via the REST API.

4. Large 128K context window

• Handles long conversations, transcriptions, and extended instructions.
• Supports multi-step voice workflows or multi-part inputs.

5. Great for lightweight reasoning workloads

• Performs well for classification, instructions, Q&A, rewriting, and audio-driven tasks.
• Good for voice agents that don't need high-end reasoning like GPT-5.1.

6. Works across major endpoints

• Chat Completions, Responses API, Realtime API, Assistants, Batch.
• Supports streaming and function calling.

7. Scalable for commercial production

• Perfect for customer support hotlines, appointment bots, FAQ voice agents, or embedded voice UI in apps.
• Reliable and predictable output behavior given its price.

8. Preview model designed for experimentation

• Lets teams prototype voice-first features with minimal cost.
• Useful stepping-stone before upgrading to GPT-4o Audio or GPT-5 audio models.

1. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.