Higgsfield Assist

Higgsfield Assist + Credit Optimization

Higgsfield Assist (GPT-5 Powered Copilot)

Location: higgsfield.ai/chat

Higgsfield Assist is a GPT-5 powered creative copilot built directly into the platform. It's separate from Claude — it lives inside Higgsfield's interface and is trained specifically on Higgsfield's tools, workflows, and generation patterns.

What Assist Can Do

• Generate image prompts optimized for the Soul model
• Generate prompts for viral videos in specific styles
• Navigate the platform — recommend which tool to use for a goal
• Recommend the right effects, apps, or presets for your use case
• Answer questions about features and capabilities
• Give feedback on scripts, prompts, or creative concepts
• Suggest fresh ideas when you're blocked
• Help with storyboard planning

How to Use Assist

1. Click "Assistant" in the top Higgsfield header
2. Select the GPT-5 model
3. Ask anything — examples:
   • "Generate a Soul image prompt in the style of a Helmut Newton editorial"
   • "What's the best workflow to create a 30-second branded video with consistent characters?"
   • "Which camera preset works best for a car chase sequence?"
   • "Help me write a prompt for a product video for a skincare brand, sophisticated tone"
4. Copy the generated prompt → paste into the relevant feature

When to Use Assist vs Claude with This Skill

Best workflow: Use this Claude skill to plan and structure → use Higgsfield Assist for final in-platform prompt refinement and quick generation.

Coming Features in Assist

• Generate content (Image, Video, Canvas) directly inside chat
• Upload and analyze media files
• Large file analysis
• Storyboard builder from ideas

Credit Optimization Guide

Understanding Credits

Commercial rights: Basic and above.
Watermarks: Free tier only.
Priority processing: Pro and above.

Plan names, prices, and credit allowances above are hand-maintained and not verifiable from the API catalog (last reviewed 2026-07-06, not re-verified against the live UI) — check higgsfield.ai/pricing before quoting them.

Credit Cost Tiers (Approximate)

Model roster reviewed against the 2026-07-05 catalog snapshot; tier placements are hand-maintained — verify live before quoting.

Low cost: Seedance 2.0 Fast / Mini, standard image generation, Nano Banana 2 Lite Medium cost: Kling 2.6 (legacy), Kling 3.0 Turbo, Wan 2.6/2.7 (and 2.5 — not in the API catalog, 2026-09-26 — verify in the live UI), Minimax Hailuo 2.3, standard I2V High cost: Kling 3.0 (pro/4K modes), Seedance 2.0 at 1080p/4K, Veo 3 / 3.1, Cinema Studio Apps: Vary widely — one-click apps are generally efficient

"Seedance Pro" is a legacy UI label — not in the API catalog (2026-07-05); its budget slot is now Seedance 2.0 Fast / Mini. Sora 2 is retired — OpenAI shut the Sora 2 API down on 2026-09-24 and Higgsfield UI availability is unconfirmed; don't recommend it (../../model-guide.md).

Quote From the Ledger, Not From Vibes

Before quoting any credit estimate for multi-shot work, run the generation ledger and cite the numbers:

python3 ../../scripts/higgsfield_memory.py ratio <project> --credits
python3 ../../scripts/higgsfield_memory.py budget <project> --shots <manifest.json>

• ratio gives empirical takes-per-kept per shot type, with the structural-vs-stochastic rejection split (high structural% = rewrite the prompt, don't re-roll; high stochastic% = priced re-roll territory).
• budget multiplies a planned shot manifest by those ratios → expected generations + credit estimate with a stated confidence level.
• Never budget from a row marked low-n (under 5 logged generations) — the tool flags them; respect the flag.
• If the ledger is empty or thin, say so explicitly and use the documented default planning ratios — 2–3:1 simple shots, 4–6:1 complex shots — labeled as defaults, not data. The budget command does this labeling automatically; keep the label when you relay the estimate.
• Every logged generation sharpens these numbers — the logging workflow is one command (../higgsfield-recall/SKILL.md § Log the Generation Result).

The 5 Most Common Credit Waste Patterns

1. Generating video before perfecting the image The single biggest waste. If your Hero Frame (base image) isn't right, every animated version will be wrong too. Fix: Spend extra time on image generation (low cost) → animate once (higher cost)

2. Long prompts that fight each other Over-specified prompts create conflicting instructions, forcing multiple regenerations. Fix: Under-specialize on elements you don't care about. Specify only what matters.

3. Changing multiple variables between generations If you change the prompt, the model, AND the camera in one go, you can't learn what fixed what. Fix: Change one thing at a time. Systematic iteration is faster than random retries.

4. Using premium tiers (Kling 3.0 pro/4K, Seedance 2.0 4K, Veo 3.1) for simple shots Premium models for simple single-character, single-camera shots. Fix: Reserve premium models for scenes that genuinely need their capabilities. Kling 3.0 Turbo or Kling 2.6 (legacy) handles most character drama at lower cost — and on Kling 3.0, sound: off gives a silent video at lower credits (per the live spec). Seedance has the same switch (generate_audio: false).

5. Not using Apps for tasks Apps are built for Face swap, product placement, style transfer — doing these manually via prompt takes more credits than the App designed for that task. Fix: Check the Apps library first. If an App covers your use case, use it.

The Hero Frame Efficiency Method

This is the single highest-leverage credit optimization technique:

Step 1: Generate 5–10 image variations (very low credit cost)
         → Find the one that's closest to your vision
Step 2: Refine that one image with inpainting/editing (low cost)
         → Get it exactly right
Step 3: Animate ONCE from the perfect Hero Frame (medium-high cost)
         → First animation attempt is already working with a strong foundation

Result: You spend more on cheap image credits, far less on expensive video credits. The credit math almost always favors this approach.