How to pick the best AI model API

Q: Can I use GeniusRelay with the OpenAI SDK?

Yes. Set the base URL to `https://api.geniusrelaygpt.com/v1`.

Q: Does it support tool calling?

Yes, on models tagged `tools`. Many also support structured output.

Q: How many models can I compare at once?

Four on the [comparison page](https://geniusrelaygpt.com/compare). The evaluation harness is yours to run.

Choose by task, context, modality and budget instead of leaderboards, with a short evaluation loop you can run this week.

2026-11-14

There is no single best model. Roleplay, coding, document processing, agents and research pull in opposite directions on price, context, format reliability and refusal behavior. The useful question is which API lets you hold several options and switch without a rewrite.

GeniusRelay puts 599 models from 37 providers behind one OpenAI-compatible endpoint, so the evaluation is cheap and the decision is reversible. This page is a rubric, not a leaderboard.

Score candidates on five axes

Task fit. Does the model actually do the job, on your prompts? Public benchmarks are a filter, not a verdict.
Format reliability. Valid JSON, valid tool calls, correct headings. Measure the failure rate over 100+ real calls.
Price at your volume. Input and output separately. Output is 4–20× input on most models.
Context. Enough window for the real input, including history and retrieved passages.
Availability. Latency, 429 rate, and whether a fallback exists.

Score 1–5 each, weight by what breaks the product. A coding agent should weight format reliability heavily; a fiction tool should weight task fit and refusal rate.

What to avoid

Leaderboard-only selection. A one-point benchmark gap rarely survives contact with real prompts.
Testing with clean inputs. Production prompts are messier, longer and bilingual.
Ignoring retries. A model that fails 8% of the time is not half-price; it is double work.
Maxing context by default. A 1M window costs more per call, not less.

Recommendations by use case

Roleplay and character chat

Refusal tolerance and long context matter most. Start with the roleplay models and prefer uncensored variants.

DeepSeek V4.1 Flash Uncensored — $0.429/M input, 1M context
GLM 5.3 Flash Uncensored — bilingual, 1M context

Setup: SillyTavern guide.

Coding and refactoring

Format reliability and instruction-following outrank price.

Claude Sonnet 5.5 — $2.60/M input, 1M context
Claude Haiku 5.5 — $0.130/M input for autocomplete-style calls

Clients: Cursor, Cline.

High-volume extraction

Cheap, bounded output, strict schema.

DeepSeek V4.1 Flash — $0.0503/M input
GPT-OSS 20B — $0.0234/M input

See the cheap LLM API guide for the cost math.

Long documents

Filter by context, then check price.

GLM 5.3 Flash Uncensored — 1M context
DeepSeek V4.1 Flash — 1.05M context, cheapest in class

Browse long-context models.

Vision

Gemini 4 Hydrogen — $1.95/M input, 1M context
Qwen3.7 Flash — $0.039/M input, 991K context

Browse vision models.

Agents and tool calling

Malformed tool calls are the failure mode that matters.

DeepSeek V4.1 Flash
Claude Sonnet 5.5 for the hard steps

Browse tool-calling models.

Run a two-hour evaluation

Collect 30–50 real prompts, including the ugly ones.
Fix a rubric: pass/fail per field, plus a 1–5 quality score.
Run each candidate through all prompts, twice, to catch variance.
Cost it: tokens × rate, plus retries at the observed failure rate.
Pick the cheapest model that clears the bar. Keep a stronger fallback configured.

Two hours of evaluation beats a month of rework. Keep the harness; model quality moves and you will run it again.

Why one endpoint matters

No integration per provider. One SDK, one key shape, one error envelope.
Side-by-side pricing. The comparison page shows four models at once.
Failover. A second route on 5xx/429 keeps traffic up.
Spend control. Per-key caps make experiments bounded.

All requests go to https://api.geniusrelaygpt.com/v1; the API reference and quickstart cover the format.

FAQ

Can I use GeniusRelay with the OpenAI SDK?

Yes. Set the base URL to https://api.geniusrelaygpt.com/v1.

Does it support tool calling?

Yes, on models tagged tools. Many also support structured output.

Is the most expensive model the best?

Only for some tasks. Long-context and frontier models win on hard work and lose on cost for bulk jobs.

How many models can I compare at once?

Four on the comparison page. The evaluation harness is yours to run.

What if a chosen model is unavailable?

Configure a fallback in the same price class and retry on 5xx/429. See error codes.

Do I need separate keys per model?

No. One key reaches all models; separate keys are for spend attribution, not access.

Start

Shortlist from the model list, score with the rubric, and run your prompts before committing. Set a key cap, then read the pricing page for the live rates at your projected volume.

Keep an evaluation record

Write the result down per prompt, not per model. A table like this survives the next model release and turns a one-off test into a baseline:

Prompt	Model	Pass	Quality 1–5	Tokens in/out	Cost
refund-01	candidate A	yes	4	620/180	$0.00007
refund-01	candidate B	no	2	610/240	$0.00019

Two columns do the real work: the pass flag and the token count. A model that passes everything at twice the tokens is not the cheaper option, and a model that saves a cent but fails one prompt in ten is not saving anything.

Keep the harness runnable with one command. Store prompts in a file, models in a list, and output to CSV. Re-run it after any price change, context change or provider update.

Common misjudgments

"It answered well once." One call is noise. Run each prompt twice at minimum.
"The output looked long, so it must be thorough." Long output is billed output. Score brevity where brevity matters.
"The cheaper model passed 90%." Price the remaining 10% at retry cost plus the fallback rate.
"It works in English." If your users write Chinese, test in Chinese; token counts and quality both shift.

After you choose

Write the model ID into config, not code. Add a fallback, a per-key cap, and a validation check on the response. Log the model with every request so a silent provider change shows up in cost and failure dashboards. Revisit the choice when the evaluation set grows or a new model lands in the same price class.

The rubric is the product; the model is a parameter.

A harness skeleton

The loop is short enough to write in one file:

import csv, json
from openai import OpenAI

client = OpenAI(base_url="https://api.geniusrelaygpt.com/v1", api_key="KEY")
prompts = [json.loads(line) for line in open("prompts.jsonl")]
models = ["deepseek-v4.1-flash", "claude-haiku-5.5", "glm-5.3-flash-uncensored"]

with open("results.csv", "w", newline="") as fh:
    out = csv.writer(fh)
    out.writerow(["prompt", "model", "output", "in", "out", "pass"])
    for prompt in prompts:
        for model in models:
            for _ in range(2):
                r = client.chat.completions.create(
                    model=model,
                    messages=[{"role": "user", "content": prompt["text"]}],
                    max_tokens=600,
                )
                text = r.choices[0].message.content
                out.writerow([
                    prompt["id"], model, text,
                    r.usage.prompt_tokens, r.usage.completion_tokens,
                    prompt["check"](text),
                ])

Replace prompt["check"] with your own assertion: parses as JSON, contains the required field, matches a regex. Score quality by hand afterward. The point is a repeatable number, not a perfect harness.