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Which LLM should you use?
Pick your task and what you optimize for — quality, cost, or speed — and get a recommended model, a resilient cross-provider fallback chain, and a monthly cost estimate. No signup, nothing leaves your browser.
Recommended chain
GPT-4o · OpenAI
Primary~$2.50/$10.00 per 1M in/out · 128K context · $775/mo at your volume
Claude Sonnet 4.5 · Anthropic
Fallback 1~$3.00/$15.00 per 1M in/out · 200K context · $1050/mo at your volume
DeepSeek V3 · DeepSeek
Fallback 2~$0.27/$1.10 per 1M in/out · 128K context · $84.50/mo at your volume
Drop this into a VernaOne prompt to run the chain with automatic failover:
{
"model": "openai/gpt-4o",
"fallbackModelCodes": [
"anthropic/claude-sonnet-4.5",
"deepseek/deepseek-v3"
]
}A sensible starting point — not gospel. Confirm with your own evals, and treat prices as approximate public list prices. VernaOne lets you run all three side by side and fall back automatically.
How to read the recommendation
Primary
The best fit for your task and priority. For "best quality" it leans frontier; for "lowest cost" it finds the cheapest capable model.
Fallbacks
Two equivalents on different providers, so one outage or rate limit never takes you fully down.
Verify with evals
This is a starting point. Run the candidates side by side on your real inputs before you commit.
Run the whole chain, not just one model
VernaOne runs your prompt across the primary and fallbacks as one named endpoint — automatic failover, side-by-side comparison, and cost analytics. No code change to switch models.
Try VernaOne free →Frequently asked
Which LLM should I use?
It depends on the task and what you optimize for. For most chat and extraction, a mid-tier model like Claude Sonnet, GPT-4o, or Gemini Flash is the best value; for hard reasoning or coding, a frontier model (Claude Opus, o3, Gemini 2.5 Pro) earns its cost; for high volume where quality is "good enough," GPT-4o mini, Gemini Flash, or DeepSeek are far cheaper. Use the picker above to get a recommendation plus a fallback chain for your specific case, and always confirm with your own evals.
What is the best cheap LLM?
For low cost with solid quality, GPT-4o mini, Gemini 2.0 Flash, Claude Haiku, and DeepSeek V3 are typically 10–100× cheaper per token than frontier models. The right one depends on task fit — set the picker to "Lowest cost" to see the cheapest capable option and a fallback.
Why does the tool suggest a fallback chain, not just one model?
Because any single provider can be down, rate-limited, or deprecate a model. A cross-provider fallback chain — a primary plus equivalents on other providers — keeps your app answering when the primary fails. The picker chooses fallbacks on different providers on purpose.
How do I actually run across several models?
You need a model-agnostic prompt and a router that fails over automatically. VernaOne runs your prompt as one named endpoint with an ordered fallback chain across OpenAI, Anthropic, Google and more, retrying the next model on timeout, 429, or 5xx — no code change. You can also run all candidates side by side to compare quality before you commit.