tegwick
e1bab67bd1
A hall visual that stays on the general branch uses the Efficient model from the 2026-10-08 intelligence-radar tree. render-hall writes only portraits whose files are still absent. Assistant: grok Assistant-Session: 01a11ab6-1534-7d63-8606-662b92a80ed6
89 lines
4.4 KiB
Markdown
89 lines
4.4 KiB
Markdown
# Choosing an image model for `llm_connect.images`
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`llm_connect.images` still requires an explicit model on a one-off render.
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Hall backfill does not: `selection_for_prompt` and `llm-connect image plan`
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apply the tree below. A plain hall portrait — square technical illustration,
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no logos, no readable text, one of many — is general / Efficient.
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**Source:** intelligence-radar topic digest
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`digests/topics/2026-10-08-1527_Image_Generation_with_Generative_AI_Digest.md`
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(evidence observed 2026-10-08). Rankings and prices in this field change
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monthly. Re-derive the table from the digest's sources before relying on it
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after about 2026-11.
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## Picking a model
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All IDs below are OpenRouter model IDs. Prices are for one 1024² image. The
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quote is the per-image estimate plus some margin, and the cap leaves room for
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the provider-reported cost to come in above the quote.
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| Need | Model | ≈ $/img | `--quote-usd` | `--max-cost-usd` |
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|---|---|---|---|---|
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| Best result, cost secondary (Frontier) | `openai/gpt-image-2.5-sunburst` | 0.21 | 0.25 | 0.30 |
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| Same quality, faster | `openai/gpt-image-2.5-flare` | 0.21 | 0.25 | 0.30 |
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| Near-frontier at scale (Efficient) | `google/gemini-nano-banana-2.1` | 0.034 | 0.04 | 0.08 |
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| Efficient, better-evidenced fallback | `microsoft/mai-image-2.6` | 0.039 | 0.045 | 0.08 |
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| Routine imagery (Budget) | `meta/muse-image` | 0.010 | 0.012 | 0.03 |
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| Drafts, thumbnails, bulk variants | `recraft/recraft-v4.1-flash` | 0.007 | 0.008 | 0.02 |
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| Text-heavy (posters, infographics, CJK) | `openai/gpt-image-2.5-sunburst` | 0.21 | 0.25 | 0.30 |
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| Brand / consistent style set | `recraft/recraft-v4-styles` | 0.035 | 0.04 | 0.08 |
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| Vector-style output | `recraft/recraft-v4.1-vector` | 0.08 | 0.09 | 0.15 |
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Decision order:
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1. Need vector output, brand style, or heavy text? Take that row.
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2. Otherwise decide how much an excellent image is worth compared with a good
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one:
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- the image is the product → Frontier;
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- many images, and quality matters → Efficient;
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- illustrative or routine → Budget;
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- exploration → bulk.
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3. For important assets, **explore cheap, then finish expensive**. Render 10–20
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Budget variants, then re-render the chosen one or two with the Frontier
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model. 20 × Muse plus 2 × Sunburst costs about the same as three Frontier
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attempts. This is a hypothesis to test with quality-ledger rows, not a
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measured result.
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Not available on OpenRouter, and therefore not reachable from this module:
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self-hostable open weights (Qwen-Image-2.1, FLUX.2 [dev]), Ideogram,
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Midjourney and Reve.
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## Scoring rule behind the classes
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Image leaderboards publish pairwise-preference **Elo**. Elo is an interval
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scale, so "within 10% of the best model" cannot be computed as a ratio of Elo
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values. The digest makes the scores usable as follows:
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```
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task_score(m) = P(m beats anchor) = 1 / (1 + 10^(-(Elo_m - Elo_anchor)/400))
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```
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Here the anchor is a fixed reference model. On the Artificial Analysis board
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that is FLUX.2 [dev], Elo 1000.
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- **Frontier:** the model with the highest `task_score`.
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- **Topnotch:** `task_score >= 0.90 × task_score(frontier)`. With the
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Frontier at Elo 1198, this means Elo ≥ about 1133.
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- **Efficient:** the cheapest Topnotch model.
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- **Sufficient / Budget:** a per-task floor. For routine imagery the digest uses
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a provisional floor of Elo ≳ 1100, the level of the frontier from about ten
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months earlier. Budget is the cheapest model above that floor.
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Callers that already use `AdaptiveRoutingPolicy` quality floors can use the
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same mapping. Convert Elo to an anchor win rate first, then apply a ratio
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floor. Do not threshold raw Elo differences across leaderboards: the scales
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differ. Arena's top gap is about 95 points where Artificial Analysis shows
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about 40.
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## Caveats
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- OpenRouter bills most image models through a per-token rate. For Recraft,
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Krea, Muse, Qwen, Seedream and FLUX 3, about 4,175 image tokens reproduces
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the vendor's per-image price. For OpenAI and Google, cost depends on the
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quality tier. Let the receipt's `cost_source=provider` correct the quotes
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after the first runs.
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- It is not verified here that every listed model accepts OpenRouter's
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`/images` endpoint with explicit `size` and `aspect_ratio=1:1`. Do one
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capped single render per model before running a batch.
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- Nano Banana 2.1 had only a few thousand arena votes on 2026-10-08. If its
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score drops, MAI-Image-2.6 becomes the Efficient pick.
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