Grok Imagine Image 2.0 vs GPT Image 2.5: flat price or tokens?

Grok Imagine Image 2.0 has one price per image no matter how long your prompt is, so budgeting is multiplication. GPT Image 2.5 on Sume is priced from token rates, so its cost moves with the quality level and size you choose, and the real number is whatever the endpoint's pricing lines say.

Two vendor statements frame this. xAI's Imagine guide says image generation has "flat per-image pricing regardless of prompt length," and its models page lists grok-imagine-image-2.0 at "$0.04 / image." Sume's Image API docs describe the GPT Image 2.5 rates in tokens.

The comparison matters most when you generate in volume. A flat price lets finance multiply and move on. A token-based price asks engineering to fix the size and quality in code, so nobody changes them by accident and moves the bill.

What does flat pricing mean in practice?

A long, detailed prompt costs the same as a short one. Editing is different: the xAI guide says an edit is billed for both the input image and the generated output image, and allows up to 5 source images in one request. So an edit with several sources costs more than a plain generation.

The xAI price above is xAI's own list. Sume's price for the same model is a separate catalog line with Sume's margin already applied, so do not reuse the $0.04 as your Sume cost.

How does GPT Image 2.5 get priced on Sume?

The Sume docs state that Flare and Sunburst use the same Fal token rates: $30 per million output image tokens, $8 per million input image tokens and $5 per million input text tokens. Output estimates use OpenAI's size and quality calculator. At 1024x1024, xhigh output is $0.09366 and max output is $0.21072 before input tokens and Sume pricing.

Two Sume behaviours matter for budgets. auto quality reserves the max amount, and omitted quality defaults to high. So a request that leaves quality out is not the cheapest one; pick low or medium deliberately for drafts.

How do you compare them for your own workload?

Do not compare list prices from two vendors. Ask Sume for each endpoint's pricing and multiply by your volume. The endpoints call returns pricing lines with cost_usd, and the amount is already what your wallet is charged.

import os, requests
H = {"Authorization": f"Bearer {os.environ['SUME_API_KEY']}"}
r = requests.get("https://api.sume.com/v1/images/models", headers=H, timeout=30)
for m in r.json()["data"]:
    if "grok-imagine-image" in m["id"] or "gpt-image-2.5" in m["id"]:
        e = requests.get("https://api.sume.com" + m["endpoints"], headers=H, timeout=30)
        for ep in e.json()["endpoints"]:
            print(m["id"], ep["pricing"])

Which one should you pick?

If your jobs are many short drafts, the flat model is easier to forecast. If you need fine control of cost versus detail, the quality ladder on GPT Image 2.5 gives you that. Neither page says which output looks better for your product, so run both on ten of your own prompts before committing.

One more check before you commit: confirm that the model you want is actually in your catalog. Sume's docs say upstream provider identity is not disclosed and one sume endpoint serves each model, so the catalog call above is the only reliable view of what you can send and what it costs. If a row is missing, the answer to "can I use it" is no, whatever a vendor page says.