Model choice guide
GPT-5.5 vs Opus 4.7 vs DeepSeek V4: which model should you use?
GPT-5.5 for speed and tool use, Opus 4.7 for deep reasoning, DeepSeek V4 for cost. Task-by-task breakdown for builders choosing between top frontier models.
Shi Xiang 'Best Ideas' community discussion · 新一轮模型发布：当智能进入月更时代
This guide synthesizes perspectives from a Chinese Shi Xiang 'Best Ideas' discussion on recent GPT-5.5, Opus 4.7, and DeepSeek V4 releases. It is an original whichllm interpretation for builders, not a translation or republication.
TL;DR
- Use Opus 4.7 when the job needs deep planning, long-horizon execution, or broad brainstorming before code.
- Use GPT-5.5 when iteration speed matters: coding agents, test-fix loops, and fast engineering feedback.
- Use DeepSeek V4 when you want strong open-model coding value and can trade away some closed-model frontier depth.
- Use Sonnet-style models when the output is writing, summarization, or crisp communication rather than long execution.
Best model by task
| Task | Best first pick | Why |
|---|---|---|
| Long-horizon coding | Opus 4.7 | It is the safest first pick when the task has many dependent steps and needs planning discipline. |
| Fast coding agent loops | GPT-5.5 | Speed compounds when the workflow is run, fail, inspect logs, patch, and run again. |
| Planning and brainstorm | Opus 4.7 | It remains the better choice when direction-setting matters more than raw response speed. |
| Writing and synthesis | Sonnet | The best execution model is not always the clearest writer; use a writing-optimized model for final prose. |
| Cost-sensitive agentic coding | DeepSeek V4 | It is the most interesting value pick when open-model economics matter more than absolute frontier depth. |
| Multimodal understanding | Opus 4.7 | The latest jump makes it a serious option for visual analysis and design-adjacent workflows. |
Why model choice changed
The old model directory question was simple: which model has the biggest benchmark number? That question is no longer enough. New frontier models now arrive with an implied workflow. Their training mix, RL environment, system behavior, tool-use habits, and serving constraints all shape how they should be used.
That is why a model can improve and still feel worse for a specific user. A harness built around yesterday's weakness can become technical debt when the next model learns the same behavior internally. The practical question is not “which model is smartest?” It is “which model matches this workflow with the least friction?”
Model notes
Opus 4.7
Pick it for hard planning, long task chains, multimodal understanding, and coding tasks where a better initial plan prevents wasted loops. Avoid treating it as the default prose model; the strongest execution model may not be the cleanest communicator.
Search Opus 4.7 specsGPT-5.5
Pick it when engineering speed is the bottleneck. For coding agents, lower latency and stable run-patch-test loops are not just comfort features; they directly increase useful work per hour.
Search GPT-5.5 specsDeepSeek V4
Pick it when open-model economics and inference efficiency matter. The strategic value is not only capability; it is the pressure it puts on token pricing and the engineering path it opens for cheaper serving.
Search DeepSeek V4 specsDo not ignore token price
In the monthly release era, capability and price can move at the same time. A model that is clearly better for long tasks can also become expensive enough that the best production choice changes. Agentic workflows amplify this because a single user can burn many rounds of planning, tool calls, logs, retries, and context compression.
For production work, compare model fit and live pricing together. A slower but cheaper model may win for bulk tasks; a faster or more reliable model may win when human time is the scarce resource.
Compare live specs on whichllm
Use this guide as a starting point, then check current context windows, model IDs, release dates, and token pricing before you wire a model into a product.