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Claude Opus 4.6 vs GPT-5.5 Pro

Claude Opus 4.6 (2026) and GPT-5.5 Pro (2026) are frontier-tier reasoning models from Anthropic and OpenAI. Claude Opus 4.6 ships a 1M-token context window, while GPT-5.5 Pro ships a 1M-token context window. On pricing, Claude Opus 4.6 costs $5/1M input tokens versus $30/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.

Claude Opus 4.6 is ~500% cheaper at $5/1M; pay for GPT-5.5 Pro only for coding workflow support.

Specs

Released2026-02-052026-04-23
Context window1M1M
Parameters
Architecturedecoder onlydecoder only
LicenseProprietaryProprietary
Knowledge cutoff2025-12-

Pricing and availability

Claude Opus 4.6GPT-5.5 Pro
Input price$5/1M tokens$30/1M tokens
Output price$25/1M tokens$180/1M tokens
Providers

Capabilities

Claude Opus 4.6GPT-5.5 Pro
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint is close: both models cover vision, multimodal input, reasoning mode, function calling, and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Claude Opus 4.6 lists $5/1M input and $25/1M output tokens, while GPT-5.5 Pro lists $30/1M input and $180/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Claude Opus 4.6 lower by about $64 per million blended tokens. Availability is 4 providers versus 1, so concentration risk also matters.

Choose Claude Opus 4.6 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose GPT-5.5 Pro when coding workflow support are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.

FAQ

Which has a larger context window, Claude Opus 4.6 or GPT-5.5 Pro?

Claude Opus 4.6 supports 1M tokens, while GPT-5.5 Pro supports 1M tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Claude Opus 4.6 or GPT-5.5 Pro?

Claude Opus 4.6 is cheaper on tracked token pricing. Claude Opus 4.6 costs $5/1M input and $25/1M output tokens. GPT-5.5 Pro costs $30/1M input and $180/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Claude Opus 4.6 or GPT-5.5 Pro open source?

Claude Opus 4.6 is listed under Proprietary. GPT-5.5 Pro is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, Claude Opus 4.6 or GPT-5.5 Pro?

Both Claude Opus 4.6 and GPT-5.5 Pro expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, Claude Opus 4.6 or GPT-5.5 Pro?

Both Claude Opus 4.6 and GPT-5.5 Pro expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.

Where can I run Claude Opus 4.6 and GPT-5.5 Pro?

Claude Opus 4.6 is available on OpenRouter, Anthropic, AWS Bedrock, and GCP Vertex AI. GPT-5.5 Pro is available on OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.