How to choose a model
The short answer: start with a cheap model, test it on your own examples, and move to a stronger one only if the quality isn't enough. Below is what to use for specific tasks, and why.
1The main rule: cheap first, then stronger
Prices differ between models by tens of times, while on simple tasks the quality gap is often invisible. claude-haiku-5-5 answers "what time do you open?" just as well as claude-opus-5-5 — for a twentieth of the price.
Test on real customer questions or real data, not made-up ones. Switching models is easy: you change one word in the model field and the rest of the code stays the same.
2What to use for each task
Prices are in US dollars per 1M tokens, input / output.
| Task | Model | Why | Price |
|---|---|---|---|
| Chatbot, simple questions | claude-haiku-5-5 | fast and cheap, understands images | 0.05 / 0.20 |
| Support bot over a knowledge base | claude-haiku-5-5; if answers are off — claude-sonnet-5 | Sonnet follows long instructions better, and a repeated knowledge base is read from the cache 10× cheaper | 0.4 / 2 |
| Code, Claude Code, Cursor | claude-sonnet-5-5; for hard tasks — claude-opus-5-5 | strength index 56 and 58, the strongest in the catalog | 0.4 / 2 and 0.8 / 4 |
| Extracting data to JSON | deepseek-v4.1-flash or claude-haiku-5-5 | verified: both follow a JSON schema, both are very cheap | 0.03 / 0.10 |
| Long documents: contracts, reports | claude-sonnet-5 | 1M-token window; follow-up questions on the same document go through the cache | 0.4 / 2 |
| Understanding an image: screenshot, photo, receipt | claude-haiku-5-5; for hard cases — claude-sonnet-5 | every Claude model accepts images | 0.05 / 0.20 |
| Agent with tools | deepseek-v4.1-flash or claude-haiku-5-5; for complex agents — claude-sonnet-5-5 | tool calling is verified on these models | 0.03 / 0.10 |
| Grading answers (judge) | claude-opus-5-5, or the cheaper gpt-6.1-sol | a judge should be stronger than the model it grades; index 58 and 52 | 0.8 / 4 and 0.27 / 1.32 |
| Hard reasoning, planning | claude-opus-5-5 or kimi-k3.1-pro | index 58 and 55; Kimi is about three times cheaper | 0.8 / 4 and 0.26 / 1.29 |
| Cheap bulk processing | qwen3.8-flash or deepseek-v4.1-flash | the cheapest in the catalog | 0.03 / 0.07 and 0.03 / 0.10 |
Generating a picture from a description is a separate job — see Images: understand and generate. The full table of every model is in Models & pricing.
3Strength index versus price
In the model catalog every model has a "strength index" — a combined score across a set of benchmarks from Artificial Analysis. A higher number means a smarter model on average. The catalog is sorted by it by default.
| Model | Strength index | Output price per 1M tokens |
|---|---|---|
claude-opus-5-5 | 58 | $4 |
claude-sonnet-5-5 | 56 | $2 |
kimi-k3.1-pro | 55 | $1.29 |
gpt-6-astra | 53 | $3.05 |
deepseek-v4.1-flash-1017 | 45 | $0.10 |
claude-haiku-5-5 | 43 | $0.20 |
How to read it: Opus costs 20 times more than Haiku and scores 15 points higher. For hard code and multi-step reasoning those points show. For "where is my order?" they don't. And at a similar index the price can differ several times over: kimi-k3.1-pro and claude-sonnet-5-5 are nearly equal in strength, but Kimi is cheaper.
The index is a general guide, not a score for your task. Testing on your own examples is what decides.
4Slow reasoning models
5More ways to save
- Prompt cache. Put a long, unchanging instruction or knowledge base at the start of the request. With
claude-sonnet-5andclaude-opus-5-5the repeated part is read from the cache 10 times cheaper. - The Standard channel. Claude models with the
-standardsuffix cost the same for any token:claude-sonnet-5-standardis $0.2, Opus is $0.3. There is no cache discount, so it pays off in pipelines where the context doesn't repeat. - Different models for different steps. A cheap one sorts and extracts; a strong one writes the final result or checks it.
More in How to pay less.