Claude Sonnet 4.6: 実践的な概要、比較、効率的なワークフロー

Many people have a similar first experience using LLMs for coding: single-file edits often go smoothly, but once the task becomes a long, multi-step project with multiple files and constraints, the model may miss requirements, repeat logic, or drift mid-way. What I’m watching with Claude sonnet 4.6 isn’t “a slightly higher score,” but whether it behaves like a dependable default model that can collaborate over long tasks and reliably bring work to closure. In this article, I’ll cover three things: what’s new in Claude sonnet 4.6, how it compares with Opus and Qwen 3.5, and a lightweight Sonnet+Qwen workflow that maps to real engineering work.

What Claude sonnet 4.6 Is: The Changes I Actually Care About

Stability and controllable delivery on long tasks

I summarize the value of Claude sonnet 4.6 like this: it’s better suited as a default model for long, constraint-heavy work that requires multiple rounds of collaboration. In real projects, that often means:

• multi-file refactors where you must follow style guides, APIs, tests, and release constraints
• reasoning across documentation and code, with citations or traceable evidence
• tool-assisted work (search, fetch, code execution, file creation) with iterative outputs

If a model stays stable under these conditions, you spend less time re-explaining requirements and more time shipping changes that can actually be merged.

1M-token context (beta)

I treat context window size as the amount of information the model can read and use for reasoning within a single session. With Claude sonnet 4.6 offering a 1M-token context window (beta), I’m more willing to:

• keep more constraints, interface specs, and key files in one continuous task thread
• reduce “rule loss” that happens when inputs are split across multiple rounds
• carry a workflow from design → implementation → audit without manual summarization between steps

My focus is not only “can it fit,” but “can it reason reliably and stay consistent after it fits.” Anthropic also positions Sonnet 4.6 around searching large codebases and delivering more consistent agentic coding outcomes.

Thinking controls and compaction

In practice, I don’t want every request to run at maximum reasoning depth. I use “thinking effort” as a knob:

• use lower effort for quick triage and drafts
• increase effort at decision points (architecture choices, audits, high-risk changes)

And when long sessions approach context limits, context compaction (beta) is valuable because it reduces the manual work of rewriting history into summaries.

Cost and default availability

When a model becomes a default in a workflow, cost structure and accessibility matter. Anthropic keeps Sonnet 4.6 価格設定 at $3 / $15 per million input/output tokens and rolls it out broadly in its products, which makes it easier to rely on for high-frequency calls in real pipelines.

Claude sonnet 4.6 vs オーパス vs Qwen 3.5: How I Choose

Sonnet 4.6 vs オーパス: the difference is mostly “ceiling” and cost structure

I think about the relationship like this:

• Claude sonnet 4.6 is the better default for most coding and knowledge-work tasks.
• オーパス is the stronger “escalation” option when you need deeper reasoning, longer outputs, or stricter consistency.

So if I need a model that can collaborate over a long task and bring it to closure, I start with Sonnet. If the task is high-stakes and low-tolerance for error, I’m more likely to switch to Opus.

クウェン 3.5: I use it as “implementation and fix capacity”

For Qwen3.5-397B-A17B specifically, the model card lists a default context length of 262,144 tokens (~256K). In my workflow, that fits well for:

• modular implementation work that can be parallelized
• filling test coverage and edge cases against a checklist
• targeted fixes based on audit findings, delivered as patch-style changes

I don’t force Qwen 3.5 to own global architecture or final audit closure. Instead, I constrain outputs with explicit specs and task cards so it can maximize implementation throughput.

My decision rule in one sentence

• I need a model for architecture alignment, staying on track in long tasks, and audit closure → Claude sonnet 4.6 is the better fit.
• I need deeper reasoning or very long final outputs → Opus is the better fit.
• I need 1つの parallelized coding and fixing pipeline → Qwen 3.5 is the better fit, especially when it follows 1つの strict spec.

Benchmark snapshot: Sonnet 4.6 vs Opus 4.5 vs Qwen 3.5

To make the comparison more concrete, here’s a table of publicly citable numbers.

Note: coverage differs by source, so I only include metrics that are explicitly listed; anything else is marked as “—”.

Claude sonnet 4.6 + Qwen 3.5 Workflow: What I Do and Why It Works

This is a minimal “what happens” workflow, without getting lost in implementation details.

What I do (a four-step loop)

1. Claude sonnet 4.6 aligns the architecture: interface contracts, module boundaries, key constraints, and acceptance criteria.
2. Qwen 3.5 implements to spec: I split work into module task cards and require strict contract compliance.
3. Claude sonnet 4.6 performs audit closure: issues ranked by severity (security, correctness, edge cases, maintainability, test coverage) plus concrete fix instructions.
4. Qwen 3.5 applies targeted fixes: patch-style changes, plus regression tests or minimum validation steps.

Why I split it this way (two conclusions)

• I need a model for architecture alignment, staying on track in long tasks, and audit closure → Claude sonnet 4.6 fits better. This work requires cross-module reasoning and consistent rule-following over long contexts, with an end state that is genuinely shippable.
• I need a parallelized coding and fixing pipeline → Qwen 3.5 fits better, especially under a strict spec. Implementation and fixes can be split into clear task cards and run in parallel as long as the spec is explicit.

If you want a model that can move beyond “it looks correct” and consistently support real workflows—long tasks, multiple constraints, multi-round collaboration, and a clean end state—I see Claude sonnet 4.6 as a strong default choice. When you need deeper reasoning or unusually long final outputs, Opus remains a sensible escalation. And if you want higher throughput for implementation and fixes, using クウェン 3.5 as a spec-driven coding line is a practical way to scale.