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Claude Sonnet vs GPT-4o

Anthropic reasoning vs OpenAI multimodal versatility

Claude Sonnet 4 and GPT-4o are the most deployed general-purpose models. Head-to-head on cost, token limits, reasoning, vision, and which teams standardize on each.

The contenders

Each pick links through to its full Directories entry.

anthropic-api-official-documentation

not yet in the index

Teams that prioritise strong reasoning and consistent output shapes in production agents.

openai-api-documentation

not yet in the index

Teams that want multimodal flexibility and tight integration with ChatGPT or Canvas workflows.

Side by side

Same criteria, three answers. The verdict is opinionated and lives below the table.

Criterion anthropic-api-official-documentationopenai-api-documentation
Base pricing (input/output tokens) $3/$15 per 1M tokens$2.50/$10 per 1M tokens
Context window 200k tokens128k tokens
Reasoning strength Extended thinking mode available; excels at multi-step logicStrong reasoning; less explicit chain-of-thought overhead
Vision capabilities Image input only; no videoImage and video input; stronger visual reasoning
Token predictability Accurate counting; cache-friendly for long contextsMore variable output length; smaller context budget
API maturity Batch processing, vision, extended thinkingBatch processing, vision, audio input, structured output enforcement
Falls over when Video processing or tight OpenAI ecosystemLong-context document analysis without token bloat

Verdict

Claude Sonnet excels when reasoning is the bottleneck. Extended thinking mode trades tokens for chain-of-thought explainability, and the larger context window makes it the default for document-heavy workflows, agent state machines, or long-running conversations. Teams building agents that reason through complex multi-step problems, or that process long documents and need to stay within a token budget, pick Sonnet.

GPT-4o wins for multimodal agility and closed-loop workflows. The vision-to-text-to-action loop is tighter, video input matters for certain use cases (video summaries, visual QA), and the ecosystem lock-in with ChatGPT, Canvas, and GPT Store integration can be a feature rather than a constraint. Teams with canvas-native workflows or that need tight OpenAI integrations pick GPT-4o.

Most production teams run both. Use Sonnet for reasoning-heavy inference, long contexts, and agent state. Use GPT-4o for multimodal tasks and ChatGPT integration. The pricing delta is narrow enough that the architecture wins pay for themselves in 2-3 days of compute. Pick one only if budget forces a choice; otherwise, the agents pick their own model based on the job shape.

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