OpenAI, Claude, Qwen, DeepSeek Pricing and Use Case Comparison

Compare cost tendencies and use cases across common model families, then verify real usage in Model Plaza and operation records.

Model prices can change by provider, model version, input tokens, output tokens, and your account plan. Use Model Plaza, model capability notes, and operation records as the source of truth.

How to check real pricing

Open Model Plaza.
Filter OpenAI, Claude, Qwen, or DeepSeek.
Check pricing, capability tags, and context length.
Copy the model name you want to test.
Send a small test request.
Open operation records and check actual tokens and cost.

Do not compare by price alone

Final cost also depends on input length, output length, retries, and whether the model fits the task. Test with real samples before production use.

Cost and scenario comparison

Model family	Cost tendency	Best for	Avoid using first for
OpenAI	Usually medium-high to high	Complex reasoning, tool use, production-critical answers, high-quality general generation	Large volumes of simple classification or repeated summaries
Claude	Usually medium-high to high	Long-context reading, code review, writing and editing, complex requirement breakdown	Very short classification or low-value bulk cleanup
Qwen	Usually cost-effective	Chinese tasks, summarization, classification, translation, format conversion, routine coding help	High-stakes decisions that need very strong consistency
DeepSeek	Usually cost-effective	Reasoning, coding, batch analysis, cost-sensitive production tasks	Critical production paths without prior testing

This table is for model selection, not a fixed price list. Different model versions can vary a lot in both price and quality.

Selection suggestions

Scenario	Starting choice	Why
Intent detection, tag classification	Qwen / DeepSeek	Short output and clear rules make cost control easier.
Summarization, rewriting, translation	Qwen / DeepSeek	Prompt templates can constrain quality, good for batches.
Complex code generation	Claude / OpenAI	Reasoning quality and context understanding matter more.
Long document understanding	Claude / OpenAI, test Qwen if cost-sensitive	Start with quality, then optimize cost.
Batch data analysis	DeepSeek / Qwen	More cost-controlled and works well with structured outputs.
Critical production path	Primary model plus fallback in your code	Switch models in your own code when the primary model fails.

Cost control practices

Send simple tasks directly to Qwen / DeepSeek.
Use OpenAI / Claude or stronger models for complex tasks.
Set max_tokens carefully, especially for classification and extraction.
Ask for only the required output format to reduce unnecessary explanation.
Add fallback models in your own code for critical paths.
Filter operation records by model and token to watch real usage over time.

Pre-launch checklist

Check	Pass condition
Model name	Copied from Model Plaza or `/v1/models`, not guessed.
Price	Checked in Model Plaza.
Quality	Tested with real samples, not only one demo prompt.
Fallback	Critical paths have fallback logic in client code.
Usage	Operation records show expected tokens and cost.