GPT-4.1 Nano vs Claude 4.6 Opus
Compare pricing, context windows, and strengths for GPT-4.1 Nano by OpenAI and Claude 4.6 Opus by Anthropic - and see how to put either to work in Appaca.
GPT-4.1 Nano
Fastest and most cost-efficient GPT-4.1 model with strong instruction following, tool calling, and a 1M-token context window for lightweight, real-time tasks.
View GPT-4.1 NanoClaude 4.6 Opus
Anthropic's most intelligent model for building agents and coding, with stronger reliability and precision for long-horizon engineering and enterprise workflows.
View Claude 4.6 OpusGPT-4.1 Nano vs Claude 4.6 Opus at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-4.1 Nano | Claude 4.6 Opus |
|---|---|---|
| Provider | OpenAI | Anthropic |
| Model type | Text | Text |
| Context window | 1.05M tokens | 1M tokens |
| Input price | $0.1 / 1M tokens | $5 / 1M tokens |
| Output price | $0.4 / 1M tokens | $25 / 1M tokens |
| Status | Superseded by GPT-5 Mini | Current |
How GPT-4.1 Nano and Claude 4.6 Opus differ
What the numbers mean in practice when choosing between GPT-4.1 Nano and Claude 4.6 Opus.
-
GPT-4.1 Nano is 98% cheaper on input tokens ($0.1 vs $5 per million), which adds up quickly in document-heavy workloads.
-
GPT-4.1 Nano is 98% cheaper on output tokens ($0.4 vs $25 per million) - the bigger factor for tools that generate long documents.
-
Context windows are close: GPT-4.1 Nano handles 1.05M tokens and Claude 4.6 Opus handles 1M tokens.
-
GPT-4.1 Nano has been superseded by GPT-5 Mini - for new builds, consider the newer model first.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
GPT-4.1 Nano
1. Ultra-Fast, Low-Latency Performance
- The fastest model in the GPT-4.1 family, ideal for real-time interactions and high-throughput applications.
- Designed for scenarios where speed matters more than complex reasoning.
2. Most Cost-Efficient GPT-4.1 Variant
- Lowest price point among GPT-4.1 models.
- Enables large-scale deployments such as support bots, routing systems, and lightweight assistants without high compute costs.
3. Solid Instruction Following
- Consistent and reliable at following clear instructions.
- Well-suited for:
- Classification
- Simple reasoning
- Data extraction
- Content rewriting
- Chat-style responses
4. Strong Tool Calling Capabilities
- Built with robust support for:
- Function calling
- Structured outputs (e.g., JSON)
- Lightweight automation tasks
- Works well within multi-step agent workflows that rely on simple tools.
5. Basic Multimodal Input
- Supports text and image input.
- Useful for:
- Simple visual recognition
- Alt-text generation
- Reading graphics or screenshots
6. Text-Only Output
- Produces text only, ensuring:
- Clean structured outputs
- High reliability for downstream processing
- Ease of integration into backend systems
7. 1M-Token Context Window
- Supports up to 1,047,576 tokens, allowing:
- Long documents
- Multiple files
- Large prompt memory
- Reduces or eliminates the need for chunking and retrieval in many simple workflows.
8. Ideal Use Cases
- Customer support bots
- Routing and intent detection
- Simple agents and workflow automation
- Content cleanup and rewriting
- Basic Q&A, summaries, and extraction
9. Broad API Integration
- Available across major API endpoints:
- Chat Completions
- Responses
- Realtime
- Assistants
- Fine-tuning
- Supports predicted outputs for reliability and determinism.
Claude 4.6 Opus
1. Anthropic's top model for coding and agents
- Anthropic positions Opus 4.6 as its most intelligent model for building agents and coding.
- It builds on Opus 4.5 with higher reliability and precision for professional software engineering, complex agentic workflows, and high-stakes enterprise tasks.
2. Strong frontier performance on real agent benchmarks
- Anthropic reports state-of-the-art results across coding and agentic evaluations.
- Public benchmark highlights include 65.4% on Terminal-Bench 2.0, 72.7% on OSWorld, and 90.2% on BigLaw Bench.
3. Best fit for long-horizon, high-context work
- Supports up to a 1M token context window in beta and up to 128K output tokens.
- Designed for long-running tasks that need sustained planning, careful debugging, code review, and strong context retention.
4. Advanced reasoning controls and workflow support
- Supports adaptive thinking and the
effortparameter, including the newmaxeffort level. - Anthropic also introduced fast mode, compaction, and dynamic filtering with web search and web fetch for Opus 4.6-era agent workflows.
Use GPT-4.1 Nano or Claude 4.6 Opus - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-4.1 Nano or Claude 4.6 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 GPT-4.1 Nano or Claude 4.6 Opus. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-4.1 Nano, test the same tool on Claude 4.6 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 GPT-4.1 Nano or Claude 4.6 Opus - connected to the tools you already use.
Related comparisons
See how GPT-4.1 Nano and Claude 4.6 Opus stack up against other models in the directory.
FAQs
GPT-4.1 Nano is generally cheaper: $0.1 input / $0.4 output per million tokens, versus $5 / $25 for Claude 4.6 Opus. Actual cost depends on how many tokens your workload reads and writes.
GPT-4.1 Nano has the larger context window at 1.05M tokens, compared to 1M tokens for Claude 4.6 Opus. A larger window means the model can consider more text at once - useful for long contracts, codebases, or months of records.
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 GPT-4.1 Nano, test the same tool on Claude 4.6 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 GPT-4.1 Nano, Claude 4.6 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 GPT-4.1 Nano or Claude 4.6 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.