o1 vs Claude 4.5 Opus
Compare pricing, context windows, and strengths for o1 by OpenAI and Claude 4.5 Opus by Anthropic - and see how to put either to work in Appaca.
o1
A full-size o-series reasoning model trained with RL to think before answering, producing strong multi-step reasoning across math, code, and analysis tasks.
View o1Claude 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 Opuso1 vs Claude 4.5 Opus at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | o1 | Claude 4.5 Opus |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Model type | Text | Text |
| Context window | 200K tokens | 200K tokens |
| Input price | $15 / 1M tokens | $5 / 1M tokens |
| Output price | $60 / 1M tokens | $25 / 1M tokens |
| Status | Current | Superseded by Claude 4.6 Opus |
How o1 and Claude 4.5 Opus differ
What the numbers mean in practice when choosing between o1 and Claude 4.5 Opus.
-
Claude 4.5 Opus is 67% cheaper on input tokens ($5 vs $15 per million), which adds up quickly in document-heavy workloads.
-
Claude 4.5 Opus is 58% cheaper on output tokens ($25 vs $60 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Claude 4.5 Opus has been superseded by Claude 4.6 Opus - for new builds, consider the newer model first.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.
o1
1. Full-scale reasoning model
- Uses reinforcement learning to generate long internal chains of thought.
- Suitable for tasks requiring deep logic, multi-step planning, and rich analytical reasoning.
2. Strong performance across domains
- Excellent at math, science, coding, and structured analytical work.
- Handles multi-step workflows and complex problem-solving with high consistency.
3. High output capacity (100K tokens)
- Enables long, detailed explanations, large documents, and multi-part analyses.
4. Image-understanding capable
- Accepts text + image inputs for visual reasoning and mixed-modality tasks.
- Output is text only, optimized for clear explanations.
5. Advanced API compatibility
- Works with Chat Completions, Responses, Realtime, Assistants, and more.
- Supports streaming, function calling, and structured outputs.
6. Stable long-context performance
- 200K-token context window supports large files, multi-document analysis, and extended conversations.
7. Designed for correctness-oriented workloads
- Prioritizes rigorous reasoning over speed.
- Useful in auditing, verification, scientific thinking, policy analysis, and legal-style reasoning.
8. Powerful but expensive
- High token costs make it suitable for selective, mission-critical reasoning rather than high-volume usage.
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
effortparameter, 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.
Use o1 or Claude 4.5 Opus - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by o1 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 o1 or Claude 4.5 Opus. No code, no API keys, no deployment.
Switch models without rebuilding
Start on o1, 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 o1 or Claude 4.5 Opus - connected to the tools you already use.







Related comparisons
See how o1 and Claude 4.5 Opus stack up against other models in the directory.
FAQs
Claude 4.5 Opus is generally cheaper: $5 input / $25 output per million tokens, versus $15 / $60 for o1. Actual cost depends on how many tokens your workload reads and writes.
They are equal: both o1 and Claude 4.5 Opus support a 200K tokens context window.
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 o1, test the same tool on Claude 4.5 Opus, and switch at any time without rebuilding anything.
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 o1, 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 o1 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.