# Choosing an image model for `llm_connect.images`

`llm_connect.images` still requires an explicit model on a one-off render.
`llm-connect image render-hall --entry` applies the tree to the single seat
about to be published. A plain hall portrait — square technical illustration,
no logos, no readable text — is general / Efficient. The corpus is not
rendered ahead of that publication.

**Source:** intelligence-radar topic digest
`digests/topics/2026-10-08-1527_Image_Generation_with_Generative_AI_Digest.md`
(evidence observed 2026-10-08). Rankings and prices in this field change
monthly. Re-derive the table from the digest's sources before relying on it
after about 2026-11.

## Picking a model

All IDs below are OpenRouter model IDs. Prices are for one 1024² image. The
quote is the per-image estimate plus some margin, and the cap leaves room for
the provider-reported cost to come in above the quote.

| Need | Model | ≈ $/img | `--quote-usd` | `--max-cost-usd` |
|---|---|---|---|---|
| Best result, cost secondary (Frontier) | `openai/gpt-image-2.5-sunburst` | 0.21 | 0.25 | 0.30 |
| Same quality, faster | `openai/gpt-image-2.5-flare` | 0.21 | 0.25 | 0.30 |
| Near-frontier at scale (Efficient) | `google/gemini-nano-banana-2.1` | 0.034 | 0.04 | 0.08 |
| Efficient, better-evidenced fallback | `microsoft/mai-image-2.6` | 0.039 | 0.045 | 0.08 |
| Routine imagery (Budget) | `meta/muse-image` | 0.010 | 0.012 | 0.03 |
| Drafts, thumbnails, bulk variants | `recraft/recraft-v4.1-flash` | 0.007 | 0.008 | 0.02 |
| Text-heavy (posters, infographics, CJK) | `openai/gpt-image-2.5-sunburst` | 0.21 | 0.25 | 0.30 |
| Brand / consistent style set | `recraft/recraft-v4-styles` | 0.035 | 0.04 | 0.08 |
| Vector-style output | `recraft/recraft-v4.1-vector` | 0.08 | 0.09 | 0.15 |

Decision order:

1. Need vector output, brand style, or heavy text? Take that row.
2. Otherwise decide how much an excellent image is worth compared with a good
   one:
   - the image is the product → Frontier;
   - many images, and quality matters → Efficient;
   - illustrative or routine → Budget;
   - exploration → bulk.
3. For important assets, **explore cheap, then finish expensive**. Render 10–20
   Budget variants, then re-render the chosen one or two with the Frontier
   model. 20 × Muse plus 2 × Sunburst costs about the same as three Frontier
   attempts. This is a hypothesis to test with quality-ledger rows, not a
   measured result.

Not available on OpenRouter, and therefore not reachable from this module:
self-hostable open weights (Qwen-Image-2.1, FLUX.2 [dev]), Ideogram,
Midjourney and Reve.

## Scoring rule behind the classes

Image leaderboards publish pairwise-preference **Elo**. Elo is an interval
scale, so "within 10% of the best model" cannot be computed as a ratio of Elo
values. The digest makes the scores usable as follows:

```
task_score(m) = P(m beats anchor) = 1 / (1 + 10^(-(Elo_m - Elo_anchor)/400))
```

Here the anchor is a fixed reference model. On the Artificial Analysis board
that is FLUX.2 [dev], Elo 1000.

- **Frontier:** the model with the highest `task_score`.
- **Topnotch:** `task_score >= 0.90 × task_score(frontier)`. With the
  Frontier at Elo 1198, this means Elo ≥ about 1133.
- **Efficient:** the cheapest Topnotch model.
- **Sufficient / Budget:** a per-task floor. For routine imagery the digest uses
  a provisional floor of Elo ≳ 1100, the level of the frontier from about ten
  months earlier. Budget is the cheapest model above that floor.

Callers that already use `AdaptiveRoutingPolicy` quality floors can use the
same mapping. Convert Elo to an anchor win rate first, then apply a ratio
floor. Do not threshold raw Elo differences across leaderboards: the scales
differ. Arena's top gap is about 95 points where Artificial Analysis shows
about 40.

## Caveats

- OpenRouter bills most image models through a per-token rate. For Recraft,
  Krea, Muse, Qwen, Seedream and FLUX 3, about 4,175 image tokens reproduces
  the vendor's per-image price. For OpenAI and Google, cost depends on the
  quality tier. Let the receipt's `cost_source=provider` correct the quotes
  after the first runs.
- It is not verified here that every listed model accepts OpenRouter's
  `/images` endpoint with explicit `size` and `aspect_ratio=1:1`. Do one
  capped single render per model before running a batch.
- Nano Banana 2.1 had only a few thousand arena votes on 2026-10-08. If its
  score drops, MAI-Image-2.6 becomes the Efficient pick.