LLM Reference

GPT-4o (11-20) vs o1 (12-17)

GPT-4o (11-20) (2024) and o1 (12-17) (2024) are frontier reasoning models from OpenAI. GPT-4o (11-20) ships a 128k-token context window, while o1 (12-17) ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.

o1 (12-17) is safer overall; choose GPT-4o (11-20) when coding workflow support matters.

Decision scorecard

Local evidence first
SignalGPT-4o (11-20)o1 (12-17)
Best formultimodal appsreasoning-heavy apps and provider-routed production
Decision fitCoding, Agents, and Long contextCoding, Agents, and Long context
Context window128k128k
Cheapest output-$60/1M tokens
Provider routes0 tracked2 tracked
Shared benchmarks0 shared0 shared

Decision tradeoffs

Choose GPT-4o (11-20) when...
  • GPT-4o (11-20) uniquely exposes Vision in local model data.
  • Local decision data tags GPT-4o (11-20) for Coding, Agents, and Long context.
Choose o1 (12-17) when...
  • o1 (12-17) has broader tracked provider coverage for fallback and procurement flexibility.
  • o1 (12-17) uniquely exposes Reasoning in local model data.
  • Local decision data tags o1 (12-17) for Coding, Agents, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

GPT-4o (11-20)

Unavailable

No complete token price in local provider data

o1 (12-17)

$27,000

Cheapest tracked route/tier: Replicate API

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

GPT-4o (11-20) -> o1 (12-17)
  • No overlapping tracked provider route is sourced for GPT-4o (11-20) and o1 (12-17); plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Vision before moving production traffic.
  • o1 (12-17) adds Reasoning in local capability data.
o1 (12-17) -> GPT-4o (11-20)
  • No overlapping tracked provider route is sourced for o1 (12-17) and GPT-4o (11-20); plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Reasoning before moving production traffic.
  • GPT-4o (11-20) adds Vision in local capability data.

Specs

Specification
Released2024-11-202024-12-17
Context window128k128k
Parameters1.76T (8x222B MoE)*
ArchitectureMixture of ExpertsDecoder Only
LicenseProprietaryProprietary
OpennessProprietaryProprietary
Commercial useCommercial use: conditionalCommercial use: conditional
Knowledge cutoff2023-10-

Pricing and availability

Pricing attributeGPT-4o (11-20)o1 (12-17)
Input price-$15/1M tokens
Output price-$60/1M tokens
Providers-

Capabilities

CapabilityGPT-4o (11-20)o1 (12-17)
VisionYesNo
MultimodalNoNo
ReasoningNoYes
Function callingNoNo
Tool useNoNo
Structured outputsNoNo
Code executionYesYes
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark scores are currently available for this pair.

Deep dive

The capability footprint differs most on vision: GPT-4o (11-20) and reasoning mode: o1 (12-17). Both models share code execution, 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.

Pricing coverage is uneven: GPT-4o (11-20) has no token price sourced yet and o1 (12-17) has $15/1M input tokens. Provider availability is 0 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GPT-4o (11-20) when coding workflow support are central to the workload. Choose o1 (12-17) when coding workflow support and broader provider choice 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-4o (11-20) or o1 (12-17)?

GPT-4o (11-20) supports 128k tokens, while o1 (12-17) supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is GPT-4o (11-20) or o1 (12-17) open source?

GPT-4o (11-20) is listed under Proprietary. o1 (12-17) 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-4o (11-20) or o1 (12-17)?

GPT-4o (11-20) has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for reasoning mode, GPT-4o (11-20) or o1 (12-17)?

o1 (12-17) has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for code execution, GPT-4o (11-20) or o1 (12-17)?

Both GPT-4o (11-20) and o1 (12-17) expose code execution. 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-4o (11-20) and o1 (12-17)?

GPT-4o (11-20) is available on the tracked providers still being sourced. o1 (12-17) is available on Replicate API and OpenAI API. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.