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o3 vs Claude 4.5 Opus

Compare pricing, context windows, and strengths for o3 by OpenAI and Claude 4.5 Opus by Anthropic - and see how to put either to work in Appaca.

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o3

A powerful reasoning model excelling at complex, multi-step tasks across math, science, coding, and visual reasoning; succeeded by GPT-5.

View o3
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Claude 4.5 Opus

Anthropic's November 2025 flagship model, combining maximum capability with practical performance for coding, agents, computer use, and enterprise workflows.

View Claude 4.5 Opus

o3 vs Claude 4.5 Opus at a glance

Specs and pricing side by side, from the Appaca AI models directory.

Spec o3 Claude 4.5 Opus
Provider OpenAI Anthropic
Model type Text Text
Context window 200K tokens 200K tokens
Input price $2 / 1M tokens $5 / 1M tokens
Output price $8 / 1M tokens $25 / 1M tokens
Status Current Superseded by Claude 4.6 Opus
Key differences

How o3 and Claude 4.5 Opus differ

What the numbers mean in practice when choosing between o3 and Claude 4.5 Opus.

  • o3 is 60% cheaper on input tokens ($2 vs $5 per million), which adds up quickly in document-heavy workloads.

  • o3 is 68% cheaper on output tokens ($8 vs $25 per million) - the bigger factor for tools that generate long documents.

  • Both models offer the same 200K tokens context window.

  • Claude 4.5 Opus has been superseded by Claude 4.6 Opus - for new builds, consider the newer model first.

Strengths side by side

Where each model shines, according to benchmarks and provider positioning.

o3

1. Advanced reasoning capability

  • Designed for multi-step thinking across text, code, and visual inputs.
  • Excels at math, science, logic puzzles, and complex analytical workflows.

2. Strong performance across domains

  • Highly capable in technical writing, data analysis, and structured problem-solving.
  • Useful for research, engineering tasks, and intricate instruction-following.

3. Visual reasoning support

  • Accepts image inputs, enabling tasks such as diagram analysis, chart interpretation, and visual logic assessments.

4. High output capacity

  • Up to 100,000 output tokens, supporting long-form content, technical breakdowns, and multi-part solutions.

5. Excellent instruction following

  • Produces detailed, step-by-step responses for tasks requiring precision and clarity.
  • Ideal for educational explanations, system design reasoning, and code walkthroughs.

6. Large 200K context window

  • Handles long documents, multi-file reasoning, or extended conversations with minimal loss of context.

7. Broad API support

  • Works with Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, Image Generation, and more.
  • Supports streaming and function calling for advanced workflows.

8. Positioned as a legacy reasoning model

  • Remains extremely capable but formally succeeded by GPT-5, which offers stronger reasoning and performance.

Claude 4.5 Opus

1. Maximum capability with more practical pricing

  • Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
  • It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

  • Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
  • It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

  • Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
  • This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

  • Added enhanced computer use with a zoom action for inspecting detailed screen regions.
  • Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.
Appaca

Use o3 or Claude 4.5 Opus - or both

Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by o3 or Claude 4.5 Opus - connected to your real data and ready for your whole team. No code, no deployment.

Describe it, and it's built

Tell the Appaca agent the internal tool you need and it builds a working app powered by o3 or Claude 4.5 Opus. No code, no API keys, no deployment.

Switch models without rebuilding

Start on o3, test the same tool on Claude 4.5 Opus, and keep whichever performs better - the rest of your app stays exactly as it is.

Automated for the whole team

Schedule tools to run on autopilot - daily digests, weekly reports, real-time triggers - and share them with your whole team from one workspace.

Describe it, and it's built

Tell the Appaca agent what your team needs and it builds a working app powered by o3 or Claude 4.5 Opus - connected to the tools you already use.

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FAQs

Is o3 cheaper than Claude 4.5 Opus?

o3 is generally cheaper: $2 input / $8 output per million tokens, versus $5 / $25 for Claude 4.5 Opus. Actual cost depends on how many tokens your workload reads and writes.

Which has the larger context window, o3 or Claude 4.5 Opus?

They are equal: both o3 and Claude 4.5 Opus support a 200K tokens context window.

Should I use o3 or Claude 4.5 Opus?

It depends on the job. Compare the pricing, context window, and strengths above against your workload - and remember the choice isn't permanent. In Appaca you can build a tool on o3, test the same tool on Claude 4.5 Opus, and switch at any time without rebuilding anything.

Can I use o3 and Claude 4.5 Opus without writing code?

Yes. Appaca is a no-code AI workspace: describe the internal tool your team needs and the Appaca agent builds it as a working app powered by o3, Claude 4.5 Opus, or any other model in the directory - with a built-in database, team access, and integrations. No API keys to wire up and nothing to deploy.

Build AI tools with o3 or Claude 4.5 Opus

Describe the tool your team needs and get a working app powered by the model you choose - with a built-in database, team access, and integrations. No code, no deployment.