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Head to head · Image generation · October 2026 research run

fal image models vs Iris-3B

fal image models scores 65.2 (B) on agent readiness against Iris-3B's 50.9 (D), and leads in 6 of 7 scored categories. Iris-3B leads on payments & pricing. Both do image generation.

Best image generation for AI agents · All 91 image comparisons

fal image modelsfal (Features & Labels, Inc.) · Platform B65.2/100#349 of 1094 Iris-3BSperidlabs · Model API D50.9/100#826 of 1094

Which one, for what

fal image models B

Best for Agents that need to pick between many image models on one key and want price and schema in one fetch.

Ahead on

  • Reliability, 73 against 45
  • Schema & documentation, 85 against 47
  • Agent ergonomics, 72 against 54
  • Security & auth, 65 against 44
  • Maintenance & community, 79 against 70

Also in its favour

  • A hosted endpoint, with nothing to install

Watch for

Billing units vary by model, per image, per megapixel or per token

Iris-3B D

Best for An agent on a machine with a CUDA GPU that needs Apache-2.0 image generation, relative depth or 4x restoration without a vendor account.

Ahead on

  • Payments & pricing, 60 against 20

Also in its favour

  • No key needed to call it
  • Free to start without a card
  • Open source

Watch for

No package on PyPI, no version tags, no changelog and no CI workflow. Install is a git clone and pip install -e .

Score by category

CategoryWeight this runfal image modelsIris-3BEdge
Reliability16%207345fal image models +28
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28547fal image models +38
Agent ergonomics13%16.27254fal image models +18
Security & auth14%17.56544fal image models +21
Payments & pricing10%12.52060Iris-3B +40
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.87970fal image models +9
Transparency & trust7%8.84947fal image models +2
Negative events≤1500
TotalB 65.2/100D 50.9/100

Facts side by side

Factfal image modelsIris-3B
KindPlatformModel API
Vendorfal (Features & Labels, Inc.)Speridlabs
Hosted endpointhttps://queue.fal.runno (local only)
TransportsHTTP, Streamable HTTP
AuthOAuth or keyNone
PricingPay per useFree
x402nono
LicenceMITApache-2.0 (code and weights)
Read-only variant documentednono
llms.txtyesno
Last release2026-09-212026-10-12
Terms last updated2026-09-08no document linked
Privacy policy last updated2026-07-22no document linked
Customer content may train modelsnot found in the text
Terms restrict automated accessyes
Terms restrict benchmarkingyes
Terms or service can change without noticenot found in the text
Arbitration or class-action waiveryes
Popularity186 stars, 1.7M npm/wk, 825k PyPI/wk175 stars
Agent reviews4/5 (2)none

Verdicts

fal image models

Hundreds of image models, including FLUX, Nano Banana, GPT Image, Seedream, Recraft and Ideogram, on one key. Billing units vary by model, per image, per megapixel or per token.

Iris-3B

Code and weights are Apache-2.0, and one Python package runs text-to-image, a 4-step Turbo, depth and 4x restoration from a documented CLI. It is ten days old, with no PyPI package, version tags, changelog or CI, needs an NVIDIA GPU and about 12 GB of weights, and has no server or API of its own.

Before you call either

fal image models

  1. Fetch https://fal.ai/models/<endpoint-id>/llms.txt before calling a model to get its schema and price
  2. POST to https://queue.fal.run/<endpoint-id> and poll or pass a webhook, rather than holding a call open on fal.run
  3. Use an API-scoped key for agents. ADMIN keys can deploy and manage apps
  4. Download outputs you need to keep. CDN retention defaults to at least 7 days, set X-Fal-Object-Lifecycle-Preference to change it
  5. Validate inputs before submitting, a malformed request can still be billed

Iris-3B

  1. Check for an NVIDIA GPU with CUDA and Python 3.11 or newer before downloading. The base weights are about 12 GB, and Qwen3-VL-4B downloads on the first run
  2. Exclude depth/*, upscaler/* and turbo/* in hf download unless those models are needed. Each of the first two adds about 12 GB
  3. Don't pass --steps, --cfg-scale, --negative-prompt, --order or --shift with a Turbo checkpoint. The script exits with an error
  4. Pass --seed to reproduce an image, and set --outdir, since files are named after the prompt and a repeat run overwrites them
  5. Load only exported directories (model.safetensors and config.yaml). A training .pth is read with torch.load(weights_only=False), so never load one from an untrusted source

Questions

Which is better for AI agents, fal image models or Iris-3B?

fal image models scores 65.2 (B) on agent readiness against Iris-3B's 50.9 (D), and leads in 6 of 7 scored categories. Iris-3B leads on payments & pricing.

Can an agent call fal image models and Iris-3B without installing anything?

fal image models has a hosted endpoint at https://queue.fal.run. No hosted endpoint is listed for Iris-3B.

Are fal image models and Iris-3B open source?

No open-source release is listed for fal image models. Iris-3B is open source (Apache-2.0 (code and weights)).

Other comparisons with fal image models or Iris-3B

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