Three-Model Daily Routing After Three Months of Practice: Sonnet 4.6 / GPT-5.5 / Gemini 3.1

One-sentence takeaway

The author routes among three tabs by task type: Sonnet 4.6 for long documents, writing tone, and code edge-case reasoning; GPT-5.5 for structured output and tool calling; Gemini 3.1 for recency and extra-long context; Sonnet covers about 90% of their work, and Opus is needed only about once a week.

Use cases

• Suitable tasks: Handling document analysis, writing, code edge cases, tables/reports, and recent-event retrieval at once, and accepting switching cost.

• Unsuitable tasks: Work that needs continuous context in a single session or cannot re-paste materials; not a reproducible controlled benchmark.

• Applicable model versions: The author explicitly names Claude Sonnet 4.6, GPT-5.5, and Gemini 3.1 Pro; Opus only as a low-frequency upgrade.

• Applicable clients, agents, or APIs: Three separate chat tabs; the author stresses re-pasting context on every switch.

• Recommended reasoning tier and parameters: The post does not specify effort / thinking settings.

Ready-to-use content

This is a routing checklist, not a model prompt. Pick a model by task; do not send the content below to a model as a system prompt:

Routing rules (copy and check off by task; pick one primary model per task)

1. Choose Claude Sonnet 4.6 when the task is:
   - Long documents that need close reading
   - Writing where tone and word choice precision matter
   - Reasoning through edge cases in code
   - Problems where your instinct is to "think carefully first"

2. Choose GPT-5.5 when the task is:
   - Tables, reports, fixed-format summaries
   - Explaining complex things to non-technical people
   - You care more about predictable output than depth
   - Tool / function calling is needed

3. Choose Gemini 3.1 Pro when the task is:
   - Obviously dependent on recent events
   - Extra-long context without noticeable quality drop
   - You need a different retrieval angle from Claude / GPT

4. Only escalate to Opus when the task is clearly open-ended and exploratory.
   Author self-report: Sonnet handles about 90% of input; Opus about once a week.

5. Before switching:
   - Three models in three separate tabs
   - Re-paste the same context every time you switch
   - Accept switching overhead; worth it only for complex tasks

Comment-thread layering (not the original author's checklist, but worth trying alongside):

Haiku fits (vezwyx): summaries without analysis, boilerplate, small well-defined code chunks, writing PRs.
Haiku does not fit: conversations that need to remember information from two turns ago.
Haiku sub-agent (Fun-Fish3077): when you need to read a lot of context only to write a plan, delegate a Haiku sub-agent.

Test/workflow steps

1. Prepare 10–20 real tasks, labeled in advance as long document / structured output / recent-event lookup / code edge case / open exploration.

2. For each task, pick one primary model from the table above; save the full input, output, and whether context was re-pasted.

3. Record switch count, re-paste time, first-pass rate, and number of rewrites needed.

4. Retry failed tasks with Opus; measure the share that truly need Opus per week, rather than defaulting to Opus for the whole run.

Original evidence and data

• The original post states "Not benchmarks — actual tasks, actual results." with no public per-task logs, repository, or score sheet.

• In a reply, the author says Sonnet handles about 90% of input and Opus about once a week.

• war4peace79 confirms they use Sonnet in the vast majority of cases and are satisfied.

• Timo425 finds Sonnet's cost-performance middling; their routing is Composer 2.5 for miscellaneous work, GPT-5.5 for harder tasks, and Opus for the hardest exploration; they consider Sonnet too expensive for daily tasks.

• vezwyx says Haiku drops basic inferences from two turns back and is only suited to light tasks without reasoning.

Scope and limitations

• This is personal routing experience; the sample is the author's own daily work and does not generalize to every team.

• No public prompts, effort settings, client versions, or controlled comparison experiments.

• "Three tabs + re-paste context" itself sacrifices continuous memory; products with a unified workspace need not copy this verbatim.

• Commenters disagree on whether Sonnet is "too expensive"; pair the routing table with your own unit pricing and failure cost.

Source excerpt or observation (short compliant paraphrase only)

The author uses Sonnet 4.6 for "Long documents that need nuanced analysis" and "Reasoning through edge cases in code" (short compliant paraphrase).