Claude Sonnet 5 vs GPT-4.1

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

GPT-4.1, released by OpenAI in April 2025, is a multimodal large language model that advances the GPT-4 series with major improvements in coding, reasoning, and instruction following. It accepts both text and images, supports tool calling and structured outputs, and features an expanded context window of up to ~1 million tokens—enabling it to process very large documents, multi-file codebases, or long conversations in a single prompt. Its knowledge is current through June 2024.

The GPT-4.1 family includes standard, mini, and nano variants, offering trade-offs between performance, cost, and latency. While parameter counts remain undisclosed, the series improves efficiency and responsiveness compared to GPT-4, making it suitable for both enterprise-scale tasks and cost-sensitive applications. Common use cases include software development, technical research, knowledge management, multimodal analysis, and high-context enterprise assistants.