For most developers, Claude Sonnet 4.6 is the right default: it handles 95% of coding, analysis, and generation tasks at $3.00 per million input tokens — 1.67× cheaper than Opus 4.7. Opus 4.7 ($5.00 / $25.00 per MTok) earns its higher price tag in a narrow but important band of tasks — complex multi-step agentic coding, frontier research synthesis, and long-context reasoning where missing a connection costs more than the token difference.
This article breaks down the actual tradeoffs: specs, pricing, performance in coding contexts, and how to make the model choice fit your workflow rather than your assumptions.
Quick Comparison: Sonnet 4.6 vs Opus 4.7
| Feature | Claude Sonnet 4.6 | Claude Opus 4.7 |
|---|---|---|
| Model ID | claude-sonnet-4-6 | claude-opus-4-7 |
| Context Window | 1M tokens | 1M tokens |
| Input Price | $3.00 / MTok | $5.00 / MTok |
| Output Price | $15.00 / MTok | $25.00 / MTok |
| Image Input | Yes | Yes |
| Extended Thinking | Yes | Yes |
| Best For | Everyday coding, content, agents | Complex reasoning, frontier analysis |
Pricing sourced from Anthropic API documentation, verified July 2026.
What Changed Across the Claude 4 Family
The Claude 4 lineup from Anthropic now spans three tiers: Haiku 4.5 for high-speed, low-cost tasks; Sonnet 4.6 as the balanced workhorse; and Opus 4.7 at the frontier. Sonnet replaced the widely-used Sonnet 3.5 as the default in Claude Code and most API integrations.
Sonnet 4.6 builds on Sonnet 3.7 with stronger coding precision, better multi-step instruction following, and improved performance on software engineering benchmarks. It matches or beats many models that were flagship tier in 2024.
Opus 4.7 is Anthropic’s most capable model. It targets tasks where Sonnet hits a ceiling: extended agentic loops that require sustained coherence across dozens of tool calls, scientific analysis requiring cross-domain synthesis, and creative projects where nuanced judgment matters as much as raw output.
Both models support extended thinking — the ability to reason through a problem before generating a final answer. This narrows the gap between tiers for math and structured reasoning tasks, though Opus retains an advantage in open-ended, judgment-heavy work.
Pricing: Sonnet vs Opus at Scale
| Model | Input | Output | Context Window | Notes |
|---|---|---|---|---|
| Claude Haiku 4.5 | $1.00 / MTok | $5.00 / MTok | 200K | Fastest, cheapest |
| Claude Sonnet 4.6 | $3.00 / MTok | $15.00 / MTok | 1M | Default for most workloads |
| Claude Opus 4.7 | $5.00 / MTok | $25.00 / MTok | 1M | Frontier capability |
Verified from Anthropic pricing page, July 2026. See Claude API Price vs Subscription Plans for full breakdown.
The pricing gap between Sonnet and Opus is less dramatic than many assume. Sonnet is 1.67× cheaper on input and 1.67× cheaper on output. If you’re doing bulk document processing or high-volume API calls, that gap adds up. If you’re running a few hundred complex queries per day, the absolute dollar difference is small.
Where it matters most is in Claude Code usage patterns. A coding session with Opus running hundreds of agentic turns can accumulate significant output tokens. Sonnet keeps those sessions affordable while still completing most tasks without noticeable quality loss.
Prompt caching changes the equation further: both models support cached input tokens at significantly lower rates, making repeated context reads cheaper. For codebases where you’re sending the same file context on every turn, caching should be your first cost-reduction lever before switching models.
Coding Performance
The most practical comparison for developers is SWE-bench Verified — a benchmark of real GitHub issues that tests whether a model can autonomously understand a bug report, locate the relevant code, and produce a correct fix.
Claude Sonnet 4.5 (predecessor to 4.6) scored 77.2% on SWE-bench Verified in Anthropic’s evaluation — a benchmark of 500 human-verified GitHub issues measuring autonomous code repair. Opus targets the top of this benchmark, with Anthropic positioning it for the most demanding software engineering tasks.
In practice, the difference shows up in specific scenarios:
Where Sonnet holds its own:
- Single-file bugs and standard refactors
- Test generation and documentation
- API integration tasks with clear patterns
- Most everyday feature development
Where Opus earns the premium:
- Multi-repository agentic tasks requiring cross-system reasoning
- Architecture decisions requiring judgment across large codebases
- Debugging complex race conditions or performance issues where subtle misdiagnosis is expensive
- Code generation for novel domains with limited training signal
For a typical feature sprint, Sonnet is the better economic choice. For a critical production incident where hours of debugging might cost more than a higher token bill, Opus can pay for itself in decision accuracy.
Speed and Latency
Opus is noticeably slower than Sonnet, especially for long outputs. This matters in interactive coding sessions where you’re waiting for a 500-line diff to generate.
In Claude Code, Anthropic ships a Fast mode that keeps Claude Opus running but prioritizes output speed — it’s available on Opus 4.6 and 4.7 via /fast. If latency is your reason for sticking with Sonnet, test Fast mode with Opus on your actual workflow before assuming Sonnet is the only option.
For batch or non-interactive workloads (overnight code reviews, scheduled analysis, CI integration), Opus’s slower generation speed is irrelevant. The throughput difference only matters when a human is waiting.
Claude Code: Opus vs Sonnet
Claude Code defaults to Claude Sonnet for most tasks and automatically selects the right model tier based on task complexity. You can override this:
# Use Sonnet for this session
claude --model sonnet
# Use Opus for this session
claude --model opus
# Specify exact model ID
claude --model claude-opus-4-7Inside a session, switch with /model opus or /model sonnet.
For most coding sessions, Sonnet is the practical default. Where Opus adds clear value in Claude Code:
- Large-scale refactors across many files where missing a pattern costs debugging time
- Architecture reviews where the model needs to hold many system components in context and reason about their interactions
- /batch operations on complex tasks where the model decomposes and parallelizes — here Opus’s stronger planning helps
One practical hybrid: use Sonnet for exploratory work and implementation, switch to Opus for final review or before committing significant changes. The model switch takes one command.
For teams who want to reduce API costs further, Novita AI’s LLM API provides an Anthropic-compatible endpoint that lets you route Claude Code to alternative models. Setting ANTHROPIC_BASE_URL=https://api.novita.ai/anthropic gives access to DeepSeek, Kimi, Qwen, and other models via the same CLI interface — useful when budget matters more than using Claude specifically. See Claude Code CLI Documentation for setup details.
Best Use Cases
Choose Sonnet 4.6 when:
- You’re running a production API with high request volume
- Tasks are well-defined: summarization, extraction, generation from templates
- You’re doing standard development work in Claude Code (feature implementation, debugging, test writing)
- Budget predictability matters more than marginal capability gains
- Speed is important for interactive sessions
Choose Opus 4.7 when:
- You’re doing frontier research synthesis or cross-domain analysis
- The task requires sustained reasoning across many steps and the context cannot be simplified
- You’re doing high-stakes code generation where correctness matters more than cost
- You’re building or testing agentic systems where the model acts autonomously over long sequences
- You need maximum performance for a small number of high-value requests
Consider Haiku 4.5 when:
- You need a fast, cheap model for routing, classification, or short completions
- You’re building internal tooling where throughput matters more than quality
- You want a lightweight model for
ANTHROPIC_SMALL_FAST_MODELin Claude Code
Claude Haiku vs Sonnet
The haiku-to-sonnet jump is more significant than sonnet-to-opus for coding tasks. Haiku 4.5 is excellent for structured tasks with clear outputs, but it struggles with nuanced judgment, complex debugging, and multi-file reasoning. Most developers who evaluate Haiku for coding end up using Sonnet as their baseline and Haiku only for preprocessing or routing layers.
API Access and Integration
Both models use the standard Anthropic SDK pattern. Switch models by changing the model ID:
import anthropic
client = anthropic.Anthropic()
# Sonnet for most tasks
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": "Review this code for bugs."}]
)
# Opus for complex analysis
response = client.messages.create(
model="claude-opus-4-7",
max_tokens=4096,
messages=[{"role": "user", "content": "Analyze the architecture of this service and propose refactoring priorities."}]
)For production applications that need to control costs, consider routing by task type: use Sonnet for high-volume inference, Opus for a small set of high-value decisions. This is a common pattern in agent pipelines where a planner (Opus) breaks down work and workers (Sonnet or Haiku) execute it.
If you’re building agentic systems that need to run code in isolated environments alongside LLM calls, Novita AI’s Agent Sandbox provides secure execution infrastructure. See Claude Code SDK: Python and TypeScript Developer Guide for building autonomous agents with full tool access.
Conclusion
Default to Claude Sonnet 4.6. It handles the overwhelming majority of development tasks with strong quality and predictable cost. Start with Sonnet, profile your actual task quality, and upgrade specific use cases to Opus only if Sonnet’s output falls short in a way that affects outcomes.
Move to Claude Opus 4.7 when:
- You’ve tested Sonnet on your task and found concrete quality gaps
- The task has high enough stakes that the cost difference is irrelevant
- You’re doing agentic work where Opus’s planning and coherence across long sequences matters
Switching is a one-line change (claude --model opus or changing the model string in your API call), so you don’t need to commit upfront. The real risk is the opposite: defaulting to Opus everywhere because it’s the “best” model, and paying 1.67× more across workloads where Sonnet would produce identical results.
FAQ
Is Claude Opus always better than Sonnet?
For general coding and content tasks, not noticeably. Opus’s advantage is clearest in complex agentic tasks, extended reasoning chains, and judgment-heavy analysis. For standard development work, Sonnet produces comparable results.
What’s the best model for Claude Code?
Sonnet 4.6 is the practical default. Use Opus for specific sessions where you’re working on complex architecture, multi-repository tasks, or high-stakes code generation. Claude Code’s Fast mode can make Opus more responsive if latency is the issue.
How does Claude Haiku compare to Sonnet for coding?
Haiku is significantly weaker for complex coding tasks. Use it for preprocessing, classification, or simple completions where cost matters more than quality. Most developers use Sonnet as their coding baseline.
Can I switch between Sonnet and Opus mid-session in Claude Code?
Yes. Use /model sonnet or /model opus inside Claude Code to switch models during a session without restarting.
What is opus vs sonnet 4?
Claude 4 is Anthropic’s current model family. Within it, Sonnet 4.6 is the balanced, cost-effective option and Opus 4.7 is the flagship. Both support a 1M token context window, extended thinking, and multimodal input.
Can I use alternative models in Claude Code instead of Sonnet or Opus?
Yes. Pointing ANTHROPIC_BASE_URL to Novita AI’s Anthropic-compatible endpoint lets you use DeepSeek, Kimi, GLM, and other models through the same Claude Code interface. This is useful for teams with strict per-token budgets.
Recommended Articles
- Claude API Price vs Subscription Plans: Complete 2026 Pricing Guide
- Claude Code CLI Documentation: Setup, Slash Commands, and LLM API Integration
- Claude Code SDK: Python and TypeScript Developer Guide
