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---
title: "Image Generation with Generative AI — State of the Field and Model Choice"
date: 2026-10-08T15:27:00+02:00
project: intelligence-radar
document_type: topic-digest
status: initial-assessment
evidence_observed_at: 2026-10-08
policy_refs:
- model-ranking-v0.1
tags:
- intelligence-radar
- image-generation
- text-to-image
- image-editing
- capability-advisor
- openrouter
- model-choice
---
# Image Generation with Generative AI — State of the Field and Model Choice
**Timestamp:** 2026-10-08 15:27 CEST
**Research context:** `intelligence-radar` / Capability Advisor
**Evidence snapshot:** Artificial Analysis Text-to-Image and Image Editing arenas
(read 2026-10-08), Arena text-to-image leaderboard (last updated 2026-10-07),
OpenRouter `/api/v1/models?output_modalities=image` (61 entries, read 2026-10-08).
> Prices and rankings in this field move monthly. Five of the top six
> text-to-image models were released after June 2026. Treat every number here
> as dated to 2026-10-08. Re-check before you commit to spending money (P7).
---
## 1. Executive summary
- **Frontier (general quality):** OpenAI **GPT Image 2.5 Sunburst** (Sept 2026).
It is #1 on both independent human-preference arenas and on the editing arena.
**GPT Image 2.5 Flare** is statistically tied with it (rank range 1–2 on
Artificial Analysis). The cost is high: about **$0.21 per image** at max quality.
- **Efficient (Topnotch at the lowest cost):** Google **Nano Banana 2.1**
(≈ $0.034/img). It is close to the Frontier at about one sixth of the price, but
it is only days old and its arena scores are preliminary. The well-evidenced
fallback is **Microsoft MAI-Image-2.6** (≈ $0.039/img).
- **Budget (credibly Sufficient for routine work):** Meta **Muse Image**
(≈ $0.010/img). It is #8 overall on Artificial Analysis and #5–8 for editing,
at about one twentieth of the Frontier price. This is the stand-out value
observation in this digest.
- **Open weights:** Alibaba **Qwen-Image-2.1** leads (Elo 1035, under a
non-commercial Qwen Research licence). The open/closed gap is about 160 Elo,
so the best open model is roughly as good as the closed frontier was about a
year ago.
- **OpenRouter:** every model above **except the open-weights ones** is
available, so the main recommendations carry over unchanged. Missing from
OpenRouter: Ideogram, Midjourney, Reve, Luma, HiDream, Imagen 4, and all
self-hostable weights.
- **Structural shift:** the top of the field is now **natively multimodal
LLM-integrated generators** (GPT Image, Nano Banana/Gemini, MAI, Muse, Grok
Imagine). Pure diffusion specialists (FLUX, Ideogram, Recraft, Midjourney) have
moved into niches: vectors, typography, style systems, self-hosting, and very
low cost.
---
## 2. The landscape (2026-10-08)
### 2.1 Text-to-image — Artificial Analysis Arena (AA-Image-T2I v2.0, top 20)
The anchor is FLUX.2 [dev] = 1000. Price is per 1,000 images at 1024², default
settings, from the creator's API. "OR" marks models available on OpenRouter.
| # (range) | Model | Creator | Elo | $/1k | Released | OR |
|---|---|---|---|---|---|---|
| 1 (1–2) | GPT Image 2.5 Sunburst (max) | OpenAI | 1198 | 210.7 | 2026-09 | ✔ |
| 2 (1–2) | GPT Image 2.5 Flare (max) | OpenAI | 1191 | 210.7 | 2026-09 | ✔ |
| 3 | GPT Image 2 (high) | OpenAI | 1172 | 211.0 | 2026-04 | ✔ |
| 4 (4–6) | Nano Banana 2.1 | Google | 1160 | 33.6 | 2026-10 | ✔ |
| 5 (4–6) | Grok Imagine Image 2.0 | SpaceXAI | 1156 | 60.0 | 2026-08 | ✔ |
| 6 (4–6) | MAI-Image-2.6 | Microsoft AI | 1151 | 38.9 | 2026-08 | ✔ |
| 7 | Nano Banana 2 (Gemini 3.1 Flash Image) | Google | 1126 | 67.0 | 2026-02 | ✔ |
| 8 (8–9) | Muse Image | Meta | 1115 | 10.0 | 2026-07 | ✔ |
| 9 (8–13) | GPT Image 1.5 (high) | OpenAI | 1107 | 133.0 | 2025-12 | — |
| 10 (9–13) | MAI-Image-2.6-Flash | Microsoft AI | 1105 | 19.5 | 2026-09 | ✔ |
| 11 | MAI-Image-2.5 | Microsoft AI | 1104 | 48.1 | 2026-06 | ✔ |
| 12 | Nano Banana Pro (Gemini 3 Pro Image) | Google | 1102 | 134.0 | 2025-11 | ✔ |
| 13 | MAI-Image-2.5-Pro | Microsoft AI | 1100 | 108.5 | 2026-07 | ✔ |
| 14 | Nano Banana 2 Lite | Google | 1095 | 33.6 | 2026-06 | ✔ |
| 15 | Qwen-Image-3.0-Pro | Alibaba | 1088 | 40.0 | 2026-07 | ✔ |
| 16 | Seedream 5.0 Pro | ByteDance Seed | 1081 | 90.0 | 2026-07 | ✔ |
| 17 | Qwen-Image-3.0 | Alibaba | 1075 | 30.0 | 2026-07 | ✔ |
| 18 | grok-imagine-image-quality | SpaceXAI | 1046 | 50.0 | 2026-05 | ✔ |
| 19 (19–24) | **Qwen-Image-2.1** (open weights) | Alibaba | 1035 | no API | 2026-09 | — |
| 20 | MAI-Image-2.5-Flash | Microsoft AI | 1032 | 20.0 | 2026-06 | — |
Selected further reference points:
| Model | Elo | $/1k | Note |
|---|---|---|---|
| FLUX.2 [max] / [pro] / [dev] | 1020 / 1004 / 1000 | 70 / 30 / 12 | dev = open weights, arena anchor |
| Ideogram 4.5 (High) / 4.0 (Quality) | 1016 / 1012 | 100 / 100 | 4.0 has open weights; typography specialist |
| Recraft V4.1 Utility / V4.1 / V4.1 Flash | 1022 / 989 / 957 | 35 / 35 / 7 | vector and brand-style specialist |
| FLUX.2 [dev] Flash / Turbo (Fal) | 987 / 998 | 5 / 8 | cheapest strong open-weights serving |
| Z-Image Turbo | 941 | 5 | open weights, very cheap |
| Midjourney v7 Alpha | 879 | no API | no programmatic access; not in contention for API work |
| DALL·E 3 | 777 | 40 | legacy; do not choose |
### 2.2 Independent cross-check — Arena (arena.ai) text-to-image, 2026-10-07
| # | Model | Score | Votes |
|---|---|---|---|
| 1 | gpt-image-2.5-sunburst | 1425 ± 7 (prelim.) | 17,752 |
| 2 | gpt-image-2.5-flare | 1398 ± 7 (prelim.) | 16,889 |
| 3 | gpt-image-2 (**medium**) | 1383 ± 4 | 93,334 |
| 4 | grok-imagine-image-2.0 (canvas) | 1334 ± 12 (prelim.) | 3,113 |
| 5 | mai-image-2.6 | 1332 ± 5 | 25,091 |
| 6 | gemini-nano-banana-2.1 | 1328 ± 9 (prelim.) | 5,310 |
| 7 | reve-2.1 | 1302 ± 8 | 8,249 |
| 9 | muse-image | 1274 ± 5 | 42,262 |
| 12 | seedream-5.0-pro | 1255 ± 4 | 94,181 |
**Agreement.** Both arenas give the same top-6 *set*, and the same ordering of
vendors at the very top. **Disagreement.** On Arena the OpenAI lead is about
95 points, against about 40 on Artificial Analysis. That makes the "near
Frontier" status of Nano Banana 2.1 and MAI-Image-2.6 less certain than the AA
numbers alone suggest. Arena also shows that GPT Image 2 at **medium** quality
still ranks #3. Quality tier is therefore a large cost lever within the OpenAI
family, but neither leaderboard publishes a per-tier price at that setting.
### 2.3 Image editing — Artificial Analysis Editing Arena (top tier)
| Rank | Model | Elo | $/1k | OR |
|---|---|---|---|---|
| 1 | GPT Image 2.5 Sunburst (max) | 1183 | 210.7 | ✔ |
| 2 | GPT Image 2.5 Flare (max) | 1163 | 210.7 | ✔ |
| 3–4 | MAI-Image-2.6 | 1139 | 38.9 | ✔ |
| 3–4 | Nano Banana 2.1 | 1137 | 33.6 | ✔ |
| 5–7 | MAI-Image-2.6-Flash | 1127 | 19.5 | ✔ |
| 5–7 | GPT Image 2 (high) | 1125 | 211.0 | ✔ |
| 5–8 | **Muse Image** | 1120 | **10.0** | ✔ |
| 15–17 | HunyuanImage 3.5 (Preview) | 1087 | 24.0 | ✔ |
| 18–23 | Qwen-Image-2.1 (open) | 1074 | no API | — |
| 19–24 | HunyuanImage 3.0 Instruct (open) | 1068 | no API | — |
Editing is where the cheap, LLM-integrated models are closest to the
Frontier. MAI-Image-2.6-Flash and Muse Image are statistically tied with
GPT Image 2 (high) for editing, at 1/10 and 1/20 of its price.
---
## 3. Benchmark selection (P4) for "general image generation"
| # | Instrument | Weight | Construct | Freshness | Known weaknesses |
|---|---|---|---|---|---|
| 1 | Artificial Analysis T2I Arena | 0.35 | Pairwise human preference, fixed prompt set | 2026-10-08 | Aesthetic bias; prompt set not task-specific; new models have few appearances |
| 2 | Arena (arena.ai) text-to-image | 0.25 | Pairwise preference, user-supplied prompts | 2026-10-07 | Self-selected voters and prompts; correlated with #1 (P5), so it is not an independent fifth of the evidence |
| 3 | Artificial Analysis Editing Arena | 0.20 | Preference on instructed edits | 2026-10-08 | Primary instrument for editing tasks, secondary for generation |
| 4 | OneIG-Bench (or a successor) | 0.10 | Automated alignment, text rendering, reasoning, style and diversity | **not refreshed** | Automated judges; lags new releases |
| 5 | LongText-Bench / text-rendering evals | 0.10 | Text rendering accuracy (Latin + CJK) | **not refreshed** | Narrow construct; vendor-reported numbers dominate |
**Mapping quality: medium.** Instruments #1–#3 are fresh and well populated.
Instruments #4 and #5 were not re-fetched for this digest. The specialist branches
of the decision tree (typography, vector output, photorealism, brand style) therefore
rest mostly on vendor positioning and press coverage, not on measured category
scores. `task-router/BENCHMARK-ATLAS.md` has **no image-generation family yet**.
That is an evidence gap to close (see §7).
---
## 4. Applying the ranking policy (model-ranking-v0.1)
The policy defines Topnotch as `task_score ≥ 0.90 × task_score(frontier)`. Elo is
an interval scale, so a ratio of Elo values is meaningless. This digest
therefore uses a **digest-local operationalisation**, which is not a versioned
policy:
> `task_score` = expected win rate against the arena anchor (FLUX.2 [dev], Elo 1000),
> `p = 1 / (1 + 10^(−(Elo − 1000)/400))`.
Frontier (Sunburst, 1198) → p = 0.758. The Topnotch threshold of 0.90 × 0.758 = 0.683
corresponds to **Elo ≥ ≈1133** on the AA board.
| Model | AA Elo | p vs anchor | ratio to Frontier | Head-to-head vs Frontier | $/img | Class |
|---|---|---|---|---|---|---|
| GPT Image 2.5 Sunburst | 1198 | 0.758 | 1.00 | — | 0.211 | **Frontier** |
| GPT Image 2.5 Flare | 1191 | 0.750 | 0.99 | 0.49 | 0.211 | Topnotch |
| GPT Image 2 (high) | 1172 | 0.729 | 0.96 | 0.46 | 0.211 | Topnotch |
| Nano Banana 2.1 | 1160 | 0.715 | 0.94 | 0.45 | **0.034** | Topnotch → **Efficient** |
| Grok Imagine Image 2.0 | 1156 | 0.711 | 0.94 | 0.44 | 0.060 | Topnotch |
| MAI-Image-2.6 | 1151 | 0.705 | 0.93 | 0.43 | 0.039 | Topnotch (Efficient fallback) |
| Nano Banana 2 | 1126 | 0.674 | 0.89 | 0.40 | 0.067 | Sufficient |
| Muse Image | 1115 | 0.660 | 0.87 | 0.38 | **0.010** | Sufficient → **Budget** |
| MAI-Image-2.6-Flash | 1105 | 0.647 | 0.85 | 0.37 | 0.0195 | Sufficient |
**Sufficient** needs a success threshold per task. No outcome ledger exists for
image tasks yet, so this digest uses a **provisional placeholder**: for routine
illustration and marketing imagery, a model is Sufficient at Elo ≳ 1100. That is
the level of GPT Image 1.5 and Nano Banana Pro, which were the Frontier about ten
months ago. Under that placeholder, **Budget = Muse Image**. For drafts, thumbnails
and exploration (the "exploratory" 0.60 threshold), much cheaper models qualify,
as §5 shows.
The anchor choice affects class membership near the threshold. If Arena's
gap is used instead of AA's, Nano Banana 2.1 and MAI-Image-2.6 fall just outside
Topnotch. **Confidence in the Efficient pick: medium.** **Confidence in the
Frontier and Budget picks: high.**
---
## 5. Decision tree — general (any access route)
```
START: what does the task need?
│
├─ A. Hard constraint: weights must run on own hardware
│ (data residency, offline, fine-tuning/LoRA, no per-image fees)
│ ├─ Best quality, non-commercial use OK ........ Qwen-Image-2.1 (Elo 1035; Qwen Research licence)
│ ├─ Commercial use, quality first .............. Ideogram 4.0 (open) (1012) or FLUX.2 [dev] (1000) — check each licence
│ ├─ Editing / instruction-following ............ Qwen-Image-2.1 (1074 edit) or HunyuanImage 3.0 Instruct (1068)
│ └─ Throughput / small GPU ..................... FLUX.2 [klein] 9B/4B, Z-Image Turbo (≈940 / 863)
│
├─ B. Output must be vector (SVG, logos, icons, scalable assets)
│ └─ Recraft V4.1 Vector (budget) / V4.1 Pro Vector (quality) [unmeasured by arenas → evidence weak]
│
├─ C. Editing an existing image (inpaint, restyle, multi-reference, product shots)
│ ├─ Highest fidelity, cost no object ........... GPT Image 2.5 Sunburst (1183; up to 16 reference images)
│ ├─ Near-frontier, about 6× cheaper ............ MAI-Image-2.6 (1139) ≈ Nano Banana 2.1 (1137)
│ └─ High volume ................................ Muse Image (1120, $0.010) or MAI-Image-2.6-Flash (1127, $0.020)
│
├─ D. Text-heavy images (posters, infographics, UI mockups, CJK text)
│ ├─ Default .................................... GPT Image 2.5 Sunburst (GPT Image line is strongest on text rendering)
│ ├─ Factual infographic needing current data ... Nano Banana 2.1 / 2 with web-search grounding
│ └─ Typography-centric design, lower cost ...... Ideogram 4.5 / 4.0 [specialist; vendor claim, category score not refreshed]
│
├─ E. Brand style systems / consistent illustration style across a set
│ └─ Recraft V4 Styles (Pro), or a top model with a fixed reference-image set
│
└─ F. General generation (photo, illustration, concept art, marketing)
├─ Is the per-image cost irrelevant compared with the value of the best result?
│ └─ YES → FRONTIER: GPT Image 2.5 Sunburst (max)
│ · Flare if latency matters (tied on quality)
│ · try GPT Image 2 at medium quality first: still #3 on Arena
├─ Do you need near-frontier quality at scale?
│ └─ YES → EFFICIENT: Nano Banana 2.1 ($0.034)
│ · fallback while 2.1 is preliminary: MAI-Image-2.6 ($0.039)
│ · alternative look: Grok Imagine Image 2.0 ($0.060)
├─ Is it routine imagery where "good" is good enough?
│ └─ YES → BUDGET: Muse Image ($0.010)
└─ Is it drafts, thumbnails, exploration, or bulk A/B variants?
└─ YES → FLUX.2 [dev] Flash/Turbo ($0.005–0.008), Z-Image Turbo ($0.005),
Recraft V4.1 Flash ($0.007)
```
**Escalation pattern (P12).** For important assets, decomposition usually beats
running one model for everything. Explore with a Budget model at 10–20 variants,
pick a composition, then regenerate or edit only the final one or two with the
Frontier model. At these prices, 20 × Muse ($0.20) + 2 × Sunburst ($0.42) costs
about the same as 3 Frontier attempts, and covers much more of the composition
space. This is a hypothesis to test in the outcome ledger, not a measured result.
---
## 6. Decision tree — limited to OpenRouter
### 6.1 How OpenRouter bills image models
OpenRouter lists 61 image-output entries (including two `openrouter/auto*` routers).
It expresses most per-image vendor prices as a **per-token rate**. For the
image-token-priced vendors (Recraft, Krea, Muse, Qwen, Seedream, FLUX 3), the
rate × ≈4,175 tokens reproduces Artificial Analysis's per-image price exactly.
Examples: Recraft V4.1 → $0.035, Krea 2 Large → $0.060, Muse → $0.010,
Qwen-Image-3 → $0.030. Prices below are derived from that rate. For OpenAI and
Google the token count depends on the quality and resolution tier. Grok Imagine
lists $0.04 output plus $0.01 per input image, against Artificial Analysis's
$0.060 per generated image. Verify actual spend with OpenRouter's generation
stats after the first runs.
Calls go through the normal chat-completions endpoint, with image output
requested through `modalities`. One OpenRouter key therefore covers every branch
below. Data handling then depends on the upstream provider, which matters for
branch A.
### 6.2 Tree
```
START (OpenRouter only)
│
├─ A. Self-hosting / open weights required
│ └─ NOT SATISFIABLE on OpenRouter. Nearest hosted options:
│ black-forest-labs/flux.2-pro or flux.2-klein-4b (open-family models, hosted by BFL)
│ qwen/qwen-image-3 ($0.030) if the requirement is "non-US vendor", not "own hardware"
│
├─ B. Vector output
│ ├─ recraft/recraft-v4-styles-vector ...................... ≈ $0.050
│ ├─ recraft/recraft-v4.1-vector ........................... ≈ $0.080
│ └─ recraft/recraft-v4.1-pro-vector ....................... ≈ $0.300
│
├─ C. Editing
│ ├─ Frontier ..... openai/gpt-image-2.5-sunburst
│ ├─ Efficient .... microsoft/mai-image-2.6 or google/gemini-nano-banana-2.1
│ └─ Budget ....... meta/muse-image (or microsoft/mai-image-2.6-flash)
│
├─ D. Text-heavy images
│ ├─ Default ....................... openai/gpt-image-2.5-sunburst
│ ├─ Grounded / current facts ...... google/gemini-nano-banana-2.1 (web_search priced separately)
│ ├─ Layered, editable design ...... inclusionai/ming-image-0.1-design-layer (Elo ≈997; no price listed — verify)
│ └─ (Ideogram is NOT on OpenRouter; there is no typography specialist)
│
├─ E. Brand / style systems
│ └─ recraft/recraft-v4-styles (≈$0.035) / recraft-v4-styles-pro (≈$0.100)
│
└─ F. General generation
├─ FRONTIER ..... openai/gpt-image-2.5-sunburst (Flare = faster twin; gpt-image-2 at lower quality to save cost)
├─ EFFICIENT .... google/gemini-nano-banana-2.1 (fallback: microsoft/mai-image-2.6)
│ x-ai/grok-imagine-image-2.0 (alternative look)
├─ BUDGET ....... meta/muse-image (≈ $0.010, Elo 1115)
└─ BULK / DRAFT
├─ recraft/recraft-v4.1-flash .................. ≈ $0.007 (Elo 957)
├─ black-forest-labs/flux.2-klein-4b ........... ≈ $0.014 (Elo 863)
├─ bytedance-seed/seedream-5-0-flash ........... ≈ $0.018 (unranked, new 2026-10-01)
└─ black-forest-labs/flux-3-image .............. ≈ $0.021 (unranked, new 2026-10-01 — watch)
```
### 6.3 What OpenRouter costs you
| Need | Best overall | Best on OpenRouter | Loss |
|---|---|---|---|
| Frontier / Efficient / Budget | Sunburst / Nano Banana 2.1 / Muse | same | none |
| Self-hosting, fine-tuning, LoRA | Qwen-Image-2.1, FLUX.2 [dev] | — | **total** |
| Typography specialist | Ideogram 4.5 | GPT Image 2.5 (generalist) | small in quality, but no cheap typography option |
| Ultra-cheap bulk | FLUX.2 [dev] Flash / Z-Image Turbo ($0.005) | Recraft V4.1 Flash ($0.007) | small |
| Distinctive aesthetic | Reve 2.1 (Arena #7), Midjourney | — | depends on taste; not measurable here |
**Verdict:** for API-driven work that does not need self-hosting, limiting yourself
to OpenRouter costs almost nothing at the top of the market in October 2026.
---
## 7. Evidence gaps and confidence
| Item | Confidence | Gap |
|---|---|---|
| Frontier = GPT Image 2.5 Sunburst | **high** | Both arenas agree; 14–18k votes each |
| Efficient = Nano Banana 2.1 | **medium-low** | 5–6k votes, preliminary on Arena; the AA vs Arena gap affects Topnotch membership |
| Budget = Muse Image | **high** for routine work | Strong on both arenas (42k Arena votes); Sufficient threshold is a placeholder |
| Specialist branches (B, D, E) | **low–medium** | No refreshed category or text-rendering scores; vendor claims dominate |
| OpenRouter per-image costs | **medium** | Derived from token rates; OpenAI/Google spend depends on quality tier |
| Price of GPT Image 2/2.5 at medium/low quality | **unknown** | The lever with the most potential savings inside the Frontier family is unmeasured here |
Follow-ups worth doing:
1. Add an **image-generation family** to `task-router/BENCHMARK-ATLAS.md`, with
AA T2I, Arena T2I, AA Editing, a text-rendering benchmark and a compositional
alignment benchmark.
2. Decide on a versioned rule for applying `topnotch.relative_to_frontier_minimum`
to **pairwise-preference (Elo) scores**. The anchor-win-rate mapping in §4 is a
candidate.
3. Run a small, matched **outcome-ledger trial** (P9). Use about 20 real prompts
across branches C, D and F with Sunburst, Nano Banana 2.1, MAI-2.6 and Muse,
with a human verdict per image. This would test both the Efficient pick and
the §5 escalation hypothesis.
4. Watch list: FLUX 3 Image (unranked), Seedream 5.0 Flash (unranked), Nano Banana 2.1
(vote count stabilising), Ideogram 4.5 (too few appearances, 2.4k).
---
## Sources
- Artificial Analysis — Text-to-Image leaderboard: https://artificialanalysis.ai/image/leaderboard/text-to-image (read 2026-10-08)
- Artificial Analysis — Image Editing leaderboard: https://artificialanalysis.ai/image/leaderboard/editing (read 2026-10-08)
- Arena — Text-to-Image leaderboard: https://arena.ai/leaderboard/text-to-image (last updated 2026-10-07)
- OpenRouter models API, image output: https://openrouter.ai/api/v1/models?output_modalities=image (read 2026-10-08)
- GPT Image 2.5 release coverage (2026-09-08/09): https://letsdatascience.com/news/openai-introduces-chatgpt-images-25-with-new-editing-tools-19e7c138 ; https://help.artlist.io/hc/en-us/articles/39018863931165-GPT-Image-2-Family
- FLUX 3 rollout coverage (secondary; dates conflict): https://aipicks.jp/news/20261002-flux-flux-3-image-4k-10
- Secondary aggregators (discovery only, P11): https://llm-stats.com/leaderboards/best-ai-for-image-generation ; https://budgetpixel.com/arena/leaderboard ; https://fal.ai/learn/tools/best-text-to-image-apis-2026