Sonnet 5 vs GPT-6 Astra vs Gemini 4 Argon: Which Fits Your Work?

The names in this comparison refer to Claude Sonnet 5, OpenAI GPT-6 Astra and Google Gemini 4 Argon. They are different offerings with different lifecycle and pricing context. Avoid comparing a chat interface's default setting with a pinned API model as though they were the same configuration.

Anthropic's Sonnet 5 documentation now labels it a legacy model and recommends considering Sonnet 5.5. Sonnet 5 can still matter for an existing workflow, but a new evaluation should include the current Sonnet option too. Record the exact model identifier, date and settings so your result remains interpretable after an update.

Anthropic lists Sonnet 5 with a 1M-token context window and a 128K-token maximum output. OpenAI positions Astra for demanding reasoning, software engineering and tool use. Google documents Argon with a unified 1M-token context window, long output and configurable thinking levels.

Those descriptions establish features and intended use; they do not prove which model will produce your best landing page, code change or research brief. Tool access, task instructions, context quality and review standards can all change the outcome. Compare complete workflows when those are what the team will actually use.

The rates below were checked against the linked provider pages. They exclude caching discounts and additional charges. Do not use them as a fixed project quote.

A cheaper token rate does not automatically produce a cheaper completed task. Track retries, billed reasoning or output, tool calls, human review and corrections. A model that needs several attempts may use more resources than its first response suggests.

For illustration, a task using 20,000 uncached input tokens and 5,000 billed output tokens would have a token-only cost of $0.09 at the $2/$10 rates, $0.45 at $10/$50 and $0.18 at $4/$20. These are arithmetic examples, not forecasts of real usage. Actual task length and additional billing can differ substantially.

Build a set of representative tasks before choosing a model. For coding, include a bug fix with a meaningful regression check. For creative work, use a real brief with explicit brand constraints. For research, require sources that actually support the claims.

Use the same essential inputs and acceptance criteria. Record whether the result was accepted, what the reviewer corrected, the time to a usable outcome and the billed cost. Repeat important tasks because one response can be unusually strong or weak. Label untested capabilities as untested rather than turning a vendor claim into your own result.

A team does not need one universal winner. It can use a familiar, economical default for ordinary work and a different model for difficult cases, provided the routing is simple enough to maintain. Set an escalation trigger such as repeated failure against a specific acceptance test.

Our recommendation is to choose the model that delivers the lowest dependable cost per accepted outcome in your environment. Where results are close, integration and review simplicity can decide the choice. Revisit the decision when models, rates or your task mix change. No independent trial of these three models is claimed in this article.