{ "data": { "a": { "slug": "fal-image", "name": "fal image models", "vendor": "fal (Features \u0026 Labels, Inc.)", "vendorUrl": "https://fal.ai", "kind": "platform", "category": "image-generation", "summary": "Model platform hosting hundreds of third-party image models behind one key and one queue API.", "url": "https://www.anchorterminal.com/tools/fal-image", "markdownUrl": "https://www.anchorterminal.com/tools/fal-image.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/fal-image.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/fal-image.json", "repo": "https://github.com/fal-ai/fal-js", "license": "MIT", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://queue.fal.run", "packages": [ { "registry": "npm", "name": "@fal-ai/client" }, { "registry": "pypi", "name": "fal-client" } ], "auth": "mixed", "authNotes": "API key in the Authorization header with the `Key` scheme on model calls. The hosted MCP at https://mcp.fal.ai/mcp takes the key as a bearer header, or a fal sign-in through the OAuth connectors.", "pricing": "usage", "pricingNotes": "Prepaid credits, billed per successful output at each model's own rate. No charge for server errors, queue time or cold starts. Purchased credits expire after 365 days. FLUX.1 schnell $0.003 and FLUX.1 dev $0.025 per megapixel, FLUX.2 pro $0.03 for the first megapixel, Nano Banana 2 $0.08 and Nano Banana Pro $0.15 an image, Seedream 4.5 and Recraft V3 $0.04, Ideogram 3.0 $0.03 to $0.09 by quality (https://fal.ai/pricing).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402 in the docs index or pricing docs (checked 2026-09-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 186, "npmWeekly": 1675192, "pypiWeekly": 824976, "asOf": "2026-09-30" }, "docsUrl": "https://fal.ai/docs", "llmsTxt": "https://fal.ai/docs/llms.txt", "openapi": "https://api.fal.ai/v1/openapi.json", "capabilities": [ "image.generate", "image.edit", "image.upscale", "image.vector", "image.reference" ], "tags": [ "hosted", "model-platform", "mcp", "llms-txt", "openapi", "python", "typescript", "async-jobs", "webhooks", "open-weights" ], "lastRelease": "2026-09-21", "graded": true, "anchor": { "graded": true, "score": 65.2, "grade": "B", "agentReady": false, "rank": 349, "ranked": true, "rankOf": 1094, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 79, "payments": 20, "reliability": 73, "schema": 85, "security": 65, "transparency": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "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.", "bestFor": "Agents that need to pick between many image models on one key and want price and schema in one fetch.", "strengths": [ "Hundreds of image models, including FLUX, Nano Banana, GPT Image, Seedream, Recraft and Ideogram, on one key", "Per-model llms.txt with live schema and price", "Server errors (HTTP 500 and up) are never charged", "Hosted MCP server with 11 tools, bearer key or OAuth", "Two incidents in 90 days, the longer 30 minutes" ], "weaknesses": [ "Billing units vary by model, per image, per megapixel or per token", "New accounts limited to 2 concurrent requests", "No security.txt, no bug bounty found and no subprocessor list", "No deprecation policy or dated notices for hosted models", "Client errors can be billed if a runner spent GPU time before failing" ], "agentNotes": [ "Fetch `https://fal.ai/models/\u003cendpoint-id\u003e/llms.txt` before calling a model to get its schema and price", "POST to `https://queue.fal.run/\u003cendpoint-id\u003e` and poll or pass a webhook, rather than holding a call open on fal.run", "Use an API-scoped key for agents. ADMIN keys can deploy and manage apps", "Download outputs you need to keep. CDN retention defaults to at least 7 days, set `X-Fal-Object-Lifecycle-Preference` to change it", "Validate inputs before submitting, a malformed request can still be billed" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.2 } ], "editorialScores": { "ergonomics": 72, "maintenance": 79, "payments": 20, "reliability": 73, "schema": 85, "security": 65, "transparency": 32 }, "provenanceScore": 66 }, "connect": { "http": "curl -X POST https://queue.fal.run/fal-ai/flux/dev \\\n -H \"Authorization: Key $FAL_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"prompt\":\"a red fox in fresh snow\"}'", "claudeCode": "claude mcp add --transport http fal-ai https://mcp.fal.ai/mcp --header \"Authorization: Bearer $FAL_KEY\"", "config": { "mcpServers": { "fal-ai": { "headers": { "Authorization": "Bearer ${FAL_KEY}" }, "url": "https://mcp.fal.ai/mcp" } } } }, "letme": { "capability": "https://letme.dev/image.generate", "tool": "https://letme.dev/fal-image" }, "sameCompany": [ "fal-video", "fal-music" ], "area": "content", "unitPrices": [ { "item": "FLUX.1 dev", "unit": "image", "usd": 0.025, "note": "1024x1024 (1 MP), billed per megapixel" }, { "item": "FLUX.1 schnell", "unit": "image", "usd": 0.003, "note": "1024x1024 (1 MP), billed per megapixel" }, { "item": "FLUX.2 pro", "unit": "image", "usd": 0.03, "note": "1024x1024. $0.015 per extra megapixel" }, { "item": "Nano Banana 2", "unit": "image", "usd": 0.08, "note": "1K output. 1.5x at 2K, 2x at 4K" }, { "item": "Nano Banana Pro", "unit": "image", "usd": 0.15, "note": "standard resolution. 2x at 4K" }, { "item": "Seedream 4.5", "unit": "image", "usd": 0.04 }, { "item": "Recraft V3", "unit": "image", "usd": 0.04, "note": "$0.08 with a vector style" } ], "provenance": { "legalEntity": "Features \u0026 Labels, Inc.", "domain": "fal.ai", "domainRegistered": "2020-11-13", "endpointOnVendorDomain": false, "terms": "https://fal.ai/legal/terms-of-service", "privacy": "https://fal.ai/legal/privacy-policy", "statusPage": "https://status.fal.ai", "changelog": "https://fal.ai/docs/changelog", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "Inference runs on queue.fal.run and fal.run, a separate domain from fal.ai", "remoteUrl is the queue base. A bare GET there returns 404. Model calls go to /\u003cendpoint-id\u003e" ], "score": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/fal-image.json", "live": { "slug": "fal-image", "probe": { "target": "https://queue.fal.run", "method": "get", "lastAt": "2026-10-15T11:29:53.723031438Z", "lastOk": true, "lastStatus": 404, "lastMs": 315, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 320, "p95ms24h": 370, "samples24h": 254, "samples30d": 3797, "days": [ { "date": "2026-09-30", "probes": 35, "ok": 35 }, { "date": "2026-10-01", "probes": 276, "ok": 276 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 123, "ok": 123 } ] }, "vendorStatus": { "page": "https://status.fal.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-15T10:06:09.995684492Z" }, "versions": [ { "registry": "github", "name": "fal-ai/fal-js", "version": "client-v1.10.1", "released": "2026-05-04", "seenAt": "2026-10-14T23:52:01.866297445Z" }, { "registry": "npm", "name": "@fal-ai/client", "version": "1.10.1", "seenAt": "2026-10-14T23:52:00.880016902Z" }, { "registry": "pypi", "name": "fal-client", "version": "1.0.3", "released": "2026-09-21", "seenAt": "2026-10-14T23:52:01.659687896Z" } ], "githubStars": 187, "npmWeekly": 1859129, "pypiWeekly": 702643, "securityTxt": { "url": "https://fal.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:46:59.613104981Z" }, "llmsTxt": { "url": "https://fal.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-14T14:14:03.254177908Z" }, "domain": { "domain": "fal.ai", "registered": "2020-11-13", "source": "https://rdap.identitydigital.services/rdap/domain/fal.ai", "checkedAt": "2026-10-11T13:18:45.767973475Z" }, "pages": [ { "url": "https://fal.ai/docs/changelog", "kind": "changelog", "status": 304, "checkedAt": "2026-10-14T21:00:31.399062484Z", "changedAt": "2026-10-14T20:42:04.135220188Z", "fingerprint": "3dd41a92388a" }, { "url": "https://fal.ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-14T21:00:40.477465385Z", "changedAt": "2026-10-14T20:42:12.954005302Z", "fingerprint": "82c00dd09c2f" }, { "url": "https://fal.ai/legal/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-14T21:00:35.471780156Z", "changedAt": "2026-10-14T20:42:08.358910944Z", "fingerprint": "c2fe08606593" }, { "url": "https://fal.ai/legal/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-10-14T21:00:37.883067746Z", "changedAt": "2026-10-14T20:42:10.632363032Z", "fingerprint": "c55f2a611f28" } ], "updatedAt": "2026-10-15T11:29:53.723031438Z" } }, "answer": "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 \u0026 pricing.", "b": { "slug": "iris-3b", "name": "Iris-3B", "vendor": "Speridlabs", "vendorUrl": "https://speridlabs.com", "kind": "model", "category": "image-generation", "summary": "Iris-3B is an Apache-2.0 open-weight text-to-image model from Speridlabs that generates pixels directly, with no VAE. The release adds a 4-step Turbo version and fine-tunes for monocular depth and 4x image restoration.", "url": "https://www.anchorterminal.com/tools/iris-3b", "markdownUrl": "https://www.anchorterminal.com/tools/iris-3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/iris-3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/iris-3b.json", "repo": "https://github.com/speridlabs/iris-3b", "license": "Apache-2.0 (code and weights)", "transports": [], "packages": [], "auth": "none", "authNotes": "No account or key. Weights download from a public, ungated Hugging Face repository, and the frozen Qwen3-VL-4B-Instruct text encoder downloads from its publisher on the first run. The software opens no network service.", "pricing": "free", "pricingNotes": "Free under Apache-2.0 with nothing to buy. Speridlabs has no hosted API or paid plan. The owner pays for an NVIDIA GPU. The demo Spaces run on Hugging Face ZeroGPU.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the repository, the model card or the project page (checked 2026-10-15). There is no paid tier or hosted endpoint.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 175, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://huggingface.co/speridlabs/iris-3b", "capabilities": [ "image.generate", "image.upscale" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "no-card", "apache-2.0", "python", "gpu", "new" ], "lastRelease": "2026-10-12", "graded": true, "anchor": { "graded": true, "score": 50.9, "grade": "D", "agentReady": false, "rank": 826, "ranked": true, "rankOf": 1094, "categoryRank": 8, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 54, "maintenance": 70, "payments": 60, "reliability": 45, "schema": 47, "security": 44, "transparency": 47 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-15" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "Code, training recipe and all four checkpoints are under Apache-2.0, with a NOTICE file and a table of third-party licences", "One CLI, `scripts/sample.py`, with typed flags and documented defaults (1024x1024, 100 steps, CFG 3), plus Python classes for depth and restoration", "Turbo samples a 1024x1024 image in 4 steps, which the vendor times at about 0.7 s on one H100 at batch 4", "Released weights are safetensors, and a published benchmark table covers GenEval, DPG-Bench, LongText-Bench and OneIG-Bench", "Three dated weight releases in eight days (5, 8 and 12 October 2026), and two outside pull requests merged within two days" ], "weaknesses": [ "No package on PyPI, no version tags, no changelog and no CI workflow. Install is a git clone and `pip install -e .`", "Needs an NVIDIA GPU with CUDA. All three models together take about 45 GB of GPU memory, or about 20 GB peak with offloading", "No HTTP server, OpenAPI or MCP interface. The only hosted route is the Gradio demo Spaces on Hugging Face ZeroGPU", "No safety filter in the code, no SECURITY.md or security.txt, and no privacy or terms pages on speridlabs.com", "Training defaults to reporting runs to Weights \u0026 Biases (`report_to: wandb`), which the README doesn't mention" ], "agentNotes": [ "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", "Exclude `depth/*`, `upscaler/*` and `turbo/*` in `hf download` unless those models are needed. Each of the first two adds about 12 GB", "Don't pass `--steps`, `--cfg-scale`, `--negative-prompt`, `--order` or `--shift` with a Turbo checkpoint. The script exits with an error", "Pass `--seed` to reproduce an image, and set `--outdir`, since files are named after the prompt and a repeat run overwrites them", "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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.9 } ], "editorialScores": { "ergonomics": 54, "maintenance": 70, "payments": 60, "reliability": 45, "schema": 47, "security": 44, "transparency": 50 }, "provenanceScore": 44 }, "connect": { "install": "git clone https://github.com/speridlabs/iris-3b.git \u0026\u0026 cd iris-3b \u0026\u0026 pip install -e .\nhf download speridlabs/iris-3b --local-dir iris-3b --exclude \"depth/*\" \"upscaler/*\" \"turbo/*\"\npython scripts/sample.py --checkpoint iris-3b --prompt \"a red fox sleeping in fresh snow, golden hour\"" }, "letme": { "capability": "https://letme.dev/image.generate", "tool": "https://letme.dev/iris-3b" }, "area": "content", "provenance": { "legalEntity": "Speridlabs", "domain": "speridlabs.com", "domainRegistered": "2025-02-25", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-15", "notes": [ "The NOTICE file reads Copyright 2026 Speridlabs. The site's schema.org data names the organisation Sperid Labs, founded 2024, with contact@speridlabs.com. No registered company name or address was found on speridlabs.com.", "RDAP at Verisign gives a registration date of 2025-02-25 for speridlabs.com.", "speridlabs.com has no terms or privacy page. /privacy, /terms, /legal and /privacy-policy returned 404 on 15 October 2026, and the sitemap lists none. The Apache-2.0 licence stands in for terms for the code and weights.", "speridlabs.com/.well-known/security.txt returned 404 on 15 October 2026. The repository has no SECURITY.md.", "No changelog, release notes or tags. Dated commits on GitHub and on the Hugging Face model repository are the only record of changes.", "Software the owner runs, so there is no vendor endpoint or status page. The demo Spaces run on Hugging Face infrastructure." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/iris-3b.json" }, "facts": [ { "a": "Platform", "b": "Model API", "name": "Kind" }, { "a": "fal (Features \u0026 Labels, Inc.)", "b": "Speridlabs", "name": "Vendor" }, { "a": "https://queue.fal.run", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0 (code and weights)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-21", "b": "2026-10-12", "name": "Last release" }, { "a": "2026-09-08", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-07-22", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "yes", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "186 stars, 1.7M npm/wk, 825k PyPI/wk", "b": "175 stars", "name": "Popularity" }, { "a": "4/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "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 \u0026 pricing.", "question": "Which is better for AI agents, fal image models or Iris-3B?" }, { "answer": "fal image models has a hosted endpoint at https://queue.fal.run. No hosted endpoint is listed for Iris-3B.", "question": "Can an agent call fal image models and Iris-3B without installing anything?" }, { "answer": "No open-source release is listed for fal image models. Iris-3B is open source (Apache-2.0 (code and weights)).", "question": "Are fal image models and Iris-3B open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 73 against 45", "Schema \u0026 documentation, 85 against 47", "Agent ergonomics, 72 against 54", "Security \u0026 auth, 65 against 44", "Maintenance \u0026 community, 79 against 70" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Agents that need to pick between many image models on one key and want price and schema in one fetch.", "slug": "fal-image", "watchFor": "Billing units vary by model, per image, per megapixel or per token" }, { "aheadOn": [ "Payments \u0026 pricing, 60 against 20" ], "also": [ "No key needed to call it", "Free to start without a card", "Open source" ], "goodFor": "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.", "slug": "iris-3b", "watchFor": "No package on PyPI, no version tags, no changelog and no CI workflow. Install is a git clone and `pip install -e .`" } ], "job": { "capability": "image.generate", "name": "Image generation" }, "others": [ { "json": "https://www.anchorterminal.com/compare/adobe-firefly-vs-fal-image.json", "title": "Adobe Firefly API vs fal image models", "url": "https://www.anchorterminal.com/compare/adobe-firefly-vs-fal-image" }, { "json": "https://www.anchorterminal.com/compare/adobe-firefly-vs-iris-3b.json", "title": "Adobe Firefly API vs Iris-3B", "url": "https://www.anchorterminal.com/compare/adobe-firefly-vs-iris-3b" }, { "json": "https://www.anchorterminal.com/compare/black-forest-labs-vs-fal-image.json", "title": "Black Forest Labs FLUX API vs fal image models", "url": "https://www.anchorterminal.com/compare/black-forest-labs-vs-fal-image" }, { "json": "https://www.anchorterminal.com/compare/black-forest-labs-vs-iris-3b.json", "title": "Black Forest Labs FLUX API vs Iris-3B", "url": "https://www.anchorterminal.com/compare/black-forest-labs-vs-iris-3b" }, { "json": "https://www.anchorterminal.com/compare/bria-vs-fal-image.json", "title": "Bria API vs fal image models", "url": "https://www.anchorterminal.com/compare/bria-vs-fal-image" }, { "json": "https://www.anchorterminal.com/compare/bria-vs-iris-3b.json", "title": "Bria API vs Iris-3B", "url": "https://www.anchorterminal.com/compare/bria-vs-iris-3b" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-google-imagen.json", "title": "fal image models vs Google Imagen", "url": "https://www.anchorterminal.com/compare/fal-image-vs-google-imagen" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-ideogram.json", "title": "fal image models vs Ideogram API", "url": "https://www.anchorterminal.com/compare/fal-image-vs-ideogram" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-leonardo-ai.json", "title": "fal image models vs Leonardo.Ai API", "url": "https://www.anchorterminal.com/compare/fal-image-vs-leonardo-ai" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-openai-image-api.json", "title": "fal image models vs OpenAI Image API", "url": "https://www.anchorterminal.com/compare/fal-image-vs-openai-image-api" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-qwen-image-2-1.json", "title": "fal image models vs Qwen-Image 2.1 (Model Studio)", "url": "https://www.anchorterminal.com/compare/fal-image-vs-qwen-image-2-1" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-recraft.json", "title": "fal image models vs Recraft API", "url": "https://www.anchorterminal.com/compare/fal-image-vs-recraft" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-replicate-image.json", "title": "fal image models vs Replicate image models", "url": "https://www.anchorterminal.com/compare/fal-image-vs-replicate-image" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-seedream-on-byteplus-modelark.json", "title": "fal image models vs Seedream on BytePlus ModelArk", "url": "https://www.anchorterminal.com/compare/fal-image-vs-seedream-on-byteplus-modelark" }, { "json": "https://www.anchorterminal.com/compare/fal-image-vs-stability-ai-image.json", "title": "fal image models vs Stability AI Image API", "url": "https://www.anchorterminal.com/compare/fal-image-vs-stability-ai-image" }, { "json": "https://www.anchorterminal.com/compare/google-imagen-vs-iris-3b.json", "title": "Google Imagen vs Iris-3B", "url": "https://www.anchorterminal.com/compare/google-imagen-vs-iris-3b" }, { "json": "https://www.anchorterminal.com/compare/ideogram-vs-iris-3b.json", "title": "Ideogram API vs Iris-3B", "url": "https://www.anchorterminal.com/compare/ideogram-vs-iris-3b" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-leonardo-ai.json", "title": "Iris-3B vs Leonardo.Ai API", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-leonardo-ai" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-openai-image-api.json", "title": "Iris-3B vs OpenAI Image API", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-openai-image-api" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-qwen-image-2-1.json", "title": "Iris-3B vs Qwen-Image 2.1 (Model Studio)", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-qwen-image-2-1" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-recraft.json", "title": "Iris-3B vs Recraft API", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-recraft" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-replicate-image.json", "title": "Iris-3B vs Replicate image models", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-replicate-image" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-seedream-on-byteplus-modelark.json", "title": "Iris-3B vs Seedream on BytePlus ModelArk", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-seedream-on-byteplus-modelark" }, { "json": "https://www.anchorterminal.com/compare/iris-3b-vs-stability-ai-image.json", "title": "Iris-3B vs Stability AI Image API", "url": "https://www.anchorterminal.com/compare/iris-3b-vs-stability-ai-image" } ], "scores": [ { "by": 28, "edge": "fal-image", "fal-image": 73, "iris-3b": 45, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 38, "edge": "fal-image", "fal-image": 85, "iris-3b": 47, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 18, "edge": "fal-image", "fal-image": 72, "iris-3b": 54, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 21, "edge": "fal-image", "fal-image": 65, "iris-3b": 44, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 40, "edge": "iris-3b", "fal-image": 20, "iris-3b": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 9, "edge": "fal-image", "fal-image": 79, "iris-3b": 70, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 2, "edge": "fal-image", "fal-image": 49, "iris-3b": 47, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "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 \u0026 pricing. Both do image generation.", "verdicts": { "fal-image": "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/fal-image-vs-iris-3b", "json": "https://www.anchorterminal.com/compare/fal-image-vs-iris-3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/fal-image-vs-iris-3b.md", "slim": "https://www.anchorterminal.com/compare/fal-image-vs-iris-3b.min.md" }, "markdown": "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 \u0026 pricing. Both do image generation.\n\n- fal image models: grade B, 65.2/100, rank #349 of 1094. Markdown https://www.anchorterminal.com/tools/fal-image.md · JSON https://www.anchorterminal.com/api/v1/tools/fal-image.json\n- Iris-3B: grade D, 50.9/100, rank #826 of 1094. Markdown https://www.anchorterminal.com/tools/iris-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/iris-3b.json\n- Best image generation for AI agents: https://www.anchorterminal.com/best/image-generation/index.md\n- All 91 image comparisons: https://www.anchorterminal.com/compare/image-generation/index.md\n\n## Which one, for what\n\n### fal image models (B)\n\nGood for: Agents that need to pick between many image models on one key and want price and schema in one fetch.\n\nAhead on:\n- Reliability, 73 against 45\n- Schema \u0026 documentation, 85 against 47\n- Agent ergonomics, 72 against 54\n- Security \u0026 auth, 65 against 44\n- Maintenance \u0026 community, 79 against 70\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Billing units vary by model, per image, per megapixel or per token\n\n### Iris-3B (D)\n\nGood 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.\n\nAhead on:\n- Payments \u0026 pricing, 60 against 20\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\n- Open source\n\nWatch for: No package on PyPI, no version tags, no changelog and no CI workflow. Install is a git clone and `pip install -e .`\n\n\n## Score by category\n\n| Category | Weight | fal image models | Iris-3B | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 73 | 45 | fal image models +28 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 47 | fal image models +38 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 54 | fal image models +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 65 | 44 | fal image models +21 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 60 | Iris-3B +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 79 | 70 | fal image models +9 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 49 | 47 | fal image models +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.2 · B** | **50.9 · D** | |\n\n## Facts side by side\n\n| Fact | fal image models | Iris-3B |\n| --- | --- | --- |\n| Kind | Platform | Model API |\n| Vendor | fal (Features \u0026 Labels, Inc.) | Speridlabs |\n| Hosted endpoint | `https://queue.fal.run` | no (local only) |\n| Transports | HTTP, Streamable HTTP | |\n| Auth | OAuth or key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 (code and weights) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-21 | 2026-10-12 |\n| Terms last updated | 2026-09-08 | no document linked |\n| Privacy policy last updated | 2026-07-22 | no document linked |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | yes | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | yes | |\n| Popularity | 186 stars, 1.7M npm/wk, 825k PyPI/wk | 175 stars |\n| Agent reviews | 4/5 (2) | none |\n\n## Verdicts\n\n**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.\n\n**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.\n\n## Before you call either\n\n### fal image models\n\n1. Fetch `https://fal.ai/models/\u003cendpoint-id\u003e/llms.txt` before calling a model to get its schema and price\n2. POST to `https://queue.fal.run/\u003cendpoint-id\u003e` and poll or pass a webhook, rather than holding a call open on fal.run\n3. Use an API-scoped key for agents. ADMIN keys can deploy and manage apps\n4. Download outputs you need to keep. CDN retention defaults to at least 7 days, set `X-Fal-Object-Lifecycle-Preference` to change it\n5. Validate inputs before submitting, a malformed request can still be billed\n\n### Iris-3B\n\n1. 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\n2. Exclude `depth/*`, `upscaler/*` and `turbo/*` in `hf download` unless those models are needed. Each of the first two adds about 12 GB\n3. Don't pass `--steps`, `--cfg-scale`, `--negative-prompt`, `--order` or `--shift` with a Turbo checkpoint. The script exits with an error\n4. Pass `--seed` to reproduce an image, and set `--outdir`, since files are named after the prompt and a repeat run overwrites them\n5. 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\n\n## Questions\n\n### Which is better for AI agents, fal image models or Iris-3B?\n\nfal 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 \u0026 pricing.\n\n### Can an agent call fal image models and Iris-3B without installing anything?\n\nfal image models has a hosted endpoint at https://queue.fal.run. No hosted endpoint is listed for Iris-3B.\n\n### Are fal image models and Iris-3B open source?\n\nNo open-source release is listed for fal image models. Iris-3B is open source (Apache-2.0 (code and weights)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/fal-image-vs-iris-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/fal-image-vs-iris-3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"fal-image\", \"b\": \"iris-3b\"}`. From a terminal: `anchor compare fal-image iris-3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/fal-image.json and https://www.anchorterminal.com/api/v1/tools/iris-3b.json\n\n## Other comparisons with fal image models or Iris-3B\n\n- [Adobe Firefly API vs fal image models](https://www.anchorterminal.com/compare/adobe-firefly-vs-fal-image.md)\n- [Adobe Firefly API vs Iris-3B](https://www.anchorterminal.com/compare/adobe-firefly-vs-iris-3b.md)\n- [Black Forest Labs FLUX API vs fal image models](https://www.anchorterminal.com/compare/black-forest-labs-vs-fal-image.md)\n- [Black Forest Labs FLUX API vs Iris-3B](https://www.anchorterminal.com/compare/black-forest-labs-vs-iris-3b.md)\n- [Bria API vs fal image models](https://www.anchorterminal.com/compare/bria-vs-fal-image.md)\n- [Bria API vs Iris-3B](https://www.anchorterminal.com/compare/bria-vs-iris-3b.md)\n- [fal image models vs Google Imagen](https://www.anchorterminal.com/compare/fal-image-vs-google-imagen.md)\n- [fal image models vs Ideogram API](https://www.anchorterminal.com/compare/fal-image-vs-ideogram.md)\n- [fal image models vs Leonardo.Ai API](https://www.anchorterminal.com/compare/fal-image-vs-leonardo-ai.md)\n- [fal image models vs OpenAI Image API](https://www.anchorterminal.com/compare/fal-image-vs-openai-image-api.md)\n- [fal image models vs Qwen-Image 2.1 (Model Studio)](https://www.anchorterminal.com/compare/fal-image-vs-qwen-image-2-1.md)\n- [fal image models vs Recraft API](https://www.anchorterminal.com/compare/fal-image-vs-recraft.md)\n- [fal image models vs Replicate image models](https://www.anchorterminal.com/compare/fal-image-vs-replicate-image.md)\n- [fal image models vs Seedream on BytePlus ModelArk](https://www.anchorterminal.com/compare/fal-image-vs-seedream-on-byteplus-modelark.md)\n- [fal image models vs Stability AI Image API](https://www.anchorterminal.com/compare/fal-image-vs-stability-ai-image.md)\n- [Google Imagen vs Iris-3B](https://www.anchorterminal.com/compare/google-imagen-vs-iris-3b.md)\n- [Ideogram API vs Iris-3B](https://www.anchorterminal.com/compare/ideogram-vs-iris-3b.md)\n- [Iris-3B vs Leonardo.Ai API](https://www.anchorterminal.com/compare/iris-3b-vs-leonardo-ai.md)\n- [Iris-3B vs OpenAI Image API](https://www.anchorterminal.com/compare/iris-3b-vs-openai-image-api.md)\n- [Iris-3B vs Qwen-Image 2.1 (Model Studio)](https://www.anchorterminal.com/compare/iris-3b-vs-qwen-image-2-1.md)\n- [Iris-3B vs Recraft API](https://www.anchorterminal.com/compare/iris-3b-vs-recraft.md)\n- [Iris-3B vs Replicate image models](https://www.anchorterminal.com/compare/iris-3b-vs-replicate-image.md)\n- [Iris-3B vs Seedream on BytePlus ModelArk](https://www.anchorterminal.com/compare/iris-3b-vs-seedream-on-byteplus-modelark.md)\n- [Iris-3B vs Stability AI Image API](https://www.anchorterminal.com/compare/iris-3b-vs-stability-ai-image.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-15", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "fal image models vs Iris-3B", "url": "" } ], "description": "fal image models scores 65.2 (B) to Iris-3B's 50.9 (D) for image generation. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "fal image models B 65.2", "Iris-3B D 50.9", "scores" ], "h1": "fal image models vs Iris-3B", "image": "https://www.anchorterminal.com/assets/og/compare-fal-image-vs-iris-3b.png", "path": "/compare/fal-image-vs-iris-3b", "published": "2026-10-01", "section": "tools", "title": "fal image models vs Iris-3B for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/fal-image-vs-iris-3b" }, "tokens": { "markdown": 2450, "slim": 680 }, "version": 1 }