image model comparison
GPT Image 2.6 Flare vs Nano Banana 2.1
GPT Image 2.6 Flare and Nano Banana 2.1 are both current image models, from OpenAI and Google. On Arena Text-to-Image, GPT Image 2.6 Flare scores 1384 Elo and Nano Banana 2.1 1325 Elo (Arena, measured November 4, 2026).
- Higher Arena Text-to-Image
- GPT Image 2.6 Flare1384 Elo against 1325 Elo
- Newer release
- GPT Image 2.6 FlareNov 2026 against Oct 2026
Data verified November 6, 2026 · Dataset 2026-11-06
Side by side
GPT Image 2.6 Flare and Nano Banana 2.1, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | GPT Image 2.6 Flare | Nano Banana 2.1 |
|---|---|---|
| Price | ||
| Price per image | Not listed | $0.0336 (1K) |
| Price per 1M input tokens | $5 | Not listed |
| Independent benchmarks | ||
| Arena Text-to-Image | 1384 Elo (#4 of 10) (better) | 1325 Elo (#6 of 10) |
| Arena Image Edit | 1462 Elo (#4 of 9) (better) | 1427 Elo (#6 of 9) |
| Specs | ||
| Provider | OpenAI | |
| Release date | November 4, 2026 | October 6, 2026 |
| Access | API only | API only |
| Context window | Not reported | Not reported |
| Max output | Not reported | Not reported |
| Input | text, image | text, image, video |
| Output | image | image, text |
Scores:Arena · 2026-11-06
Charts
Against every current image model
Where GPT Image 2.6 Flare and Nano Banana 2.1 land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
On Arena Text-to-Image, GPT Image 2.6 Flare ranks #4 and Nano Banana 2.1 #6 of 10 current models. Each bar is the share of current models it beats: on score, price and context.
Independent benchmarks
Arena Text-to-Image
GPT Image 2.6 Flare:#4of 10
1,384 Elo · ahead of 67%
Nano Banana 2.1:#6of 10
1,325 Elo · ahead of 44%
Arena Image Edit
GPT Image 2.6 Flare:#4of 9
1,462 Elo · ahead of 63%
Nano Banana 2.1:#6of 9
1,427 Elo · ahead of 38%
Price and context
Price per image
GPT Image 2.6 Flare: Not listed
Nano Banana 2.1:#2of 5
$0.0336 · cheaper than 50%
Quality against price
Nano Banana 2.1 scores 1,325 at $0.0336 (price per image). The line joins the current models no other beats on both price and Arena Text-to-Image score; hover any point to name it.
Quality against price: the data
| Model | Provider | Price per image | Arena Text-to-Image (Elo) |
|---|---|---|---|
| Nano Banana 2.1 | $0.034 | 1,325 | |
| Gemini 3.1 Flash Lite Image | $0.034 | 1,249 | |
| Gemini 3 Pro Image | $0.13 | 1,246 | |
| grok-imagine-image | xAI | $0.020 | 1,169 |
| FLUX1.1 [pro] | Black Forest Labs | $0.040 | 1,016 |
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
| Workload or rate | GPT Image 2.6 Flare | Nano Banana 2.1 |
|---|---|---|
| Example workloads | ||
| 1,000 images | Not listed | $33.60 (1K) |
| Other listed rates | ||
| 1M input tokens, cached | $1.25 | Not listed |
| 1M input tokens, image input | $8 | Not listed |
| 1M output tokens, image output | $30 | $30 |
Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.
Launch price $100, regular price $150
Verdict
When to choose each
GPT Image 2.6 Flare
Where it leads
- Higher Arena Text-to-Image score, 1384 Elo vs 1325 Elo
- Higher Arena Image Edit score, 1462 Elo vs 1427 Elo
- A cached-input rate, $1.25 per 1M tokens
Best for
- Fast, high-volume image generation and quick edits
Watch out for
- Arena's first ratings put it below GPT Image 2.5 Flare: 1384 against 1399 for generation and 1462 against 1482 for editing, on fewer than 4,000 votes so far
- OpenAI's pricing page lists no batch rate for 2.6 yet, while GPT Image 2.5 costs half in batch
Nano Banana 2.1
Where it leads
- Accepts video input
- Also outputs text
Best for
- High-volume product, marketing and social imagery where cost per image matters
- Iterative editing that must keep characters and objects consistent across turns
Watch out for
- Google prices output by image tokens; the per-image figures are Google's own equivalents and batch is half price
From benchmark to production
Building on GPT Image 2.6 Flare or Nano Banana 2.1?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
AI Model Cost AuditLaunch price $100, regular price $150What the audit includes
FAQ
GPT Image 2.6 Flare vs Nano Banana 2.1 FAQ
Do GPT Image 2.6 Flare and Nano Banana 2.1 discount cached input?
GPT Image 2.6 Flare lists a cached-input rate of $1.25 per 1M tokens, 75% below its standard input rate. No cached-input rate is listed for Nano Banana 2.1.
Which scores higher on independent benchmarks, GPT Image 2.6 Flare or Nano Banana 2.1?
GPT Image 2.6 Flare leads on Arena Text-to-Image, 1384 Elo to 1325 Elo; GPT Image 2.6 Flare leads on Arena Image Edit, 1462 Elo to 1427 Elo. Scores are independent evaluations, not either vendor's launch figures.
How do GPT Image 2.6 Flare and Nano Banana 2.1 differ in features?
GPT Image 2.6 Flare takes text and image input; Nano Banana 2.1 takes text, image and video. GPT Image 2.6 Flare produces image; Nano Banana 2.1 produces image and text.
Further reading
More on GPT Image 2.6 Flare and Nano Banana 2.1
Related comparisons
- Gemini 3 Pro Image vs GPT Image 2.6 Flare
- GPT Image 2.6 Sunburst vs Nano Banana 2.1
- GPT Image 2.5 Flare vs GPT Image 2.6 Flare
- Gemini 3.1 Flash Image vs Nano Banana 2.1
- GPT Image 2.5 Sunburst vs GPT Image 2.6 Flare
- Gemini 3.1 Flash Lite Image vs Nano Banana 2.1
- GPT Image 2.6 Flare vs GPT Image 2.6 Sunburst
- Gemini 3 Pro Image vs Nano Banana 2.1
- GPT Image 2 vs GPT Image 2.6 Flare
- GPT Image 2.5 Flare vs Nano Banana 2.1
- GPT Image 2.5 Sunburst vs Nano Banana 2.1
- grok-imagine-image vs Nano Banana 2.1
Production evaluation
Choosing between GPT Image 2.6 Flare and Nano Banana 2.1?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty