LLM Best Practices

Claude vs GPT

Last updated 5 min read

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Overview

Pick Claude for agentic coding and multi-cloud buying; pick GPT for image generation and realtime voice. List prices now match tier for tier, down to Claude Haiku 5.5 and GPT-6 Luna at 0.50. Anthropic and OpenAI now match on window size (about 1M tokens) and maximum output (128K), so the differences are tiers and prices, hosted capabilities, clouds, and tuning options. Long prompts bill differently: Fable 5.1, Opus 5.5, and Sonnet 5.5 charge the full 1M window at the standard rate, Haiku 5.5 charges 5x on the whole request once a prompt exceeds 100K input tokens, and the GPT-6 models charge 2x input and 1.5x output above 272K input tokens. Prices are per million input and output tokens, checked on 2026-10-18 against each vendor’s documentation. Route each task to whichever model wins your own eval.

Compare models

TierAnthropicOpenAI
Most capableClaude Fable 5.1, claude-fable-5-1, 50, 1M contextGPT-6 Astra, gpt-6-astra, 50, 1.05M context
Balanced flagshipClaude Opus 5.5, claude-opus-5-5, 20, 1M context (Anthropic’s suggested default)GPT-5.6 Sol, 20 (previous generation)
Fast and capableClaude Sonnet 5.5, claude-sonnet-5-5, 10, 1M contextGPT-6.1 Sol, gpt-6.1-sol, 10, 1.05M context, near-Astra performance (GPT-6 Sol, gpt-6-sol, has the same price and is one release older)
CheapestClaude Haiku 5.5, claude-haiku-5-5, 0.50 up to 100K input tokens, 2.50 above, 1M contextGPT-6 Luna, gpt-6-luna, 0.50, 0.75 above 272K input tokens, 1.05M context

Compare platforms

DimensionAnthropicOpenAI
Primary APIMessages API; the Agent SDK for Python and TypeScriptResponses API; the Assistants API shut down on 2026-08-26
Hosted toolsServer tools for web search, web fetch, and code execution; client tools for bash, text editor, computer use, and memory; Agent Skills; tool searchWeb search, file search, shell, computer use, remote MCP, image generation, tool search, skills
Prompt cachingExplicit cache_control breakpoints (5-minute or 1-hour TTL) or automatic top-level caching; reads cost 10 percent of input, 5 percent on Opus 5.5 and Sonnet 5.5, 2.5 percent on Fable 5.1Implicit by default, explicit mode on GPT-5.6 and later; reads cost 0.1x input (0.05x on GPT-6.1 Sol)
Batch50 percent discount50 percent discount
Image generationNoneGPT Image 2 and 2.5
VoiceNo speech APIRealtime and audio models (gpt-realtime-2.1, gpt-live-1, gpt-audio-1.5) plus transcription and text-to-speech
Fine-tuningNot in the API feature listFine-tuning platform is being wound down and is closed to new users
CloudsClaude API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, Claude Platform on AWSOpenAI API and Azure; check current listings for others
Data controlsZero data retention eligibility per feature; inference_geo routing (global or us, on the Claude API and Claude Platform on AWS; us costs 1.1x); Fable models are not available under ZDRZero data retention for eligible customers; review current terms

Pick Claude when

Pick Claude for agent loops and for procurement flexibility.

  • Long-horizon, tool-using agents and coding (claude-code): the Messages API supports strict tool schemas and structured outputs through output_config. Start on Opus 5.5 and move to Fable 5.1 only when evals at higher effort still fall short.
  • Whole-codebase or long-document work: on Fable 5.1, Opus 5.5, and Sonnet 5.5 the full 1M window bills at the standard rate. Measure recall late in the window on your own documents for either vendor before relying on it.
  • You need the model through Bedrock, Vertex AI, or Foundry to satisfy existing cloud contracts; on Bedrock and Vertex AI the region follows the endpoint you call.
  • Repeated large contexts: explicit cache breakpoints make costs predictable; see cost-control.

Pick GPT when

Pick GPT when the task needs a capability Anthropic does not ship.

  • Image generation or voice products: GPT Image models and the realtime audio models have no Anthropic counterpart.
  • High-volume classification or extraction with prompts between 100K and 272K input tokens, where GPT-6 Luna stays at its base rate and Haiku 5.5 pays 5x on the whole request; compare cost per completed task, not list price (tokenizers differ by vendor, and Claude 4.7 and later models emit about 30 percent more tokens than older Claude models for the same text).
  • Existing Codex workflows, or Azure-centered procurement.

Port between them

Keep the vendor behind a thin router (LiteLLM, OpenRouter, or an interface you own) and re-run evals before moving traffic.

  • Tool-call formats, structured-output config, and stop behavior differ; retest schemas per provider (structured-output).
  • Parameters differ by model. On Claude, assistant prefill returns 400 on the 4.6 family and later; non-default temperature, top_p, and top_k values are rejected with 400 on Opus 4.7 and later, Sonnet 5 and 5.5, and the Fable models; forced tool_choice returns 400 on Fable 5.1, Opus 5.5, and Sonnet 5.5. GPT-6 Astra rejects custom temperature and top_p and has no none reasoning effort. Control behavior through prompts and effort settings.
  • Rewrite prompts per model; Anthropic documents XML tags for structure (prompt-design).
  • Do not assume either vendor is always up: configure a fallback provider and see multi-agent and evaluation.

FAQ

Is Claude or GPT cheaper per token?

Neither on list price: every tier matches, down to Claude Haiku 5.5 and GPT-6 Luna at 0.50 per million tokens. Haiku 5.5 bills a prompt over 100K input tokens at 5x on the whole request, and Luna’s surcharge starts above 272K, so compare cost per completed task.

Which is better for coding agents, Claude or GPT?

Pick Claude for long-horizon, tool-using agents and coding, starting on Opus 5.5 and moving to Fable 5.1 only when evals at higher effort fall short. Route each task to whichever model wins your own eval.

Does Claude charge more for long prompts?

Only on Haiku 5.5, where a prompt over 100K input tokens pays 5x on the whole request; Fable 5.1, Opus 5.5, and Sonnet 5.5 bill the full 1M window at the standard rate. The GPT-6 models charge 2x input and 1.5x output above 272K input tokens.

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