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GPT-5 vs GPT-5.5

GPT-5 (2025) and GPT-5.5 (2026) are frontier reasoning models from OpenAI. GPT-5 ships a 128K-token context window, while GPT-5.5 ships a 1M-token context window. On pricing, GPT-5 costs $1.25/1M input tokens versus $5/1M 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. The goal is to make the tradeoff clear before deeper testing.

GPT-5 is ~300% cheaper at $1.25/1M; pay for GPT-5.5 only for coding workflow support.

Specs

Released2025-01-212026-04-23
Context window128K1M
Parameters
Architecturedecoder onlydecoder only
LicenseProprietaryProprietary
Knowledge cutoff--

Pricing and availability

GPT-5GPT-5.5
Input price$1.25/1M tokens$5/1M tokens
Output price$10/1M tokens$30/1M tokens
Providers

Capabilities

GPT-5GPT-5.5
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 differs most on reasoning mode: GPT-5.5. Both models share vision, multimodal input, function calling, and tool use, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, GPT-5 lists $1.25/1M input and $10/1M output tokens, while GPT-5.5 lists $5/1M input and $30/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-5 lower by about $8.63 per million blended tokens. Availability is 2 providers versus 1, so concentration risk also matters.

Choose GPT-5 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose GPT-5.5 when coding workflow support and larger context windows 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. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, GPT-5 or GPT-5.5?

GPT-5.5 supports 1M tokens, while GPT-5 supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is cheaper, GPT-5 or GPT-5.5?

GPT-5 is cheaper on tracked token pricing. GPT-5 costs $1.25/1M input and $10/1M output tokens. GPT-5.5 costs $5/1M input and $30/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5 or GPT-5.5 open source?

GPT-5 is listed under Proprietary. GPT-5.5 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, GPT-5 or GPT-5.5?

Both GPT-5 and GPT-5.5 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, GPT-5 or GPT-5.5?

Both GPT-5 and GPT-5.5 expose multimodal input. 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.

Where can I run GPT-5 and GPT-5.5?

GPT-5 is available on Replicate API and OpenRouter. GPT-5.5 is available on OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

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