VS Code Copilot

Add to .vscode/mcp.json:

{
  "servers": {
    "vision-squeezer": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

JetBrains (IntelliJ, WebStorm, PyCharm)

Open Tools → GitHub Copilot → Model Context Protocol (MCP) → Configure, then add:

{
  "servers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

Gemini CLI

gemini mcp add --scope user vision-squeezer -- npx -y vision-squeezer

Or add to ~/.gemini/settings.json (user) / .gemini/settings.json (project):

{
  "mcpServers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

Codex CLI

codex mcp add vision-squeezer -- npx -y vision-squeezer

Or add to ~/.codex/config.toml:

[mcp_servers.vision-squeezer]
command = "npx"
args = ["-y", "vision-squeezer"]

Qwen Code

qwen mcp add vision-squeezer -- npx -y vision-squeezer

OpenCode

opencode mcp add is interactive-only, so add directly to ~/.config/opencode/opencode.json (global) or opencode.json in the repo root (project):

{
  "mcp": {
    "vision-squeezer": {
      "type": "local",
      "command": ["npx", "-y", "vision-squeezer"],
      "enabled": true
    }
  }
}

Kimi CLI

kimi mcp add vision-squeezer -- npx -y vision-squeezer

Zed

Add to ~/.config/zed/settings.json:

{
  "context_servers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

Kiro

Add to .kiro/settings/mcp.json (workspace) or ~/.kiro/settings/mcp.json (global):

{
  "mcpServers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

Antigravity

MCP-only, no hooks needed. Configure via the Antigravity MCP settings:

{
  "mcpServers": {
    "vision-squeezer": {
      "command": "npx",
      "args": ["-y", "vision-squeezer"]
    }
  }
}

If you send raw images to an LLM, you are leaking tokens. Modern vision models do not care about your file size (MB/KB); they only care about pixel dimensions, but each provider calculates costs completely differently. vision-squeezer simulates these algorithms to find the mathematical minimum size that drops your token usage without losing visual context.

1. Claude (Area-Based)

As of 2026 (Claude 3.5 / 4.5+), Anthropic uses an area-based formula: Tokens ≈ (Width × Height) / 750. Every single pixel of solid background or padding costs you tokens.

• The Fix: vision-squeezer aggressively crops padding (removing solid color borders). A 1025×1025 screenshot shrinks just enough to drop from 1,400 tokens to 1,024 tokens (%26 savings).

2. GPT-4.5 / GPT-4o (Tiling & Short-side Scaling)

OpenAI scales your image to fit inside a 2048px box, then rescales it again so the shortest side is exactly 768px. Finally, it chops the image into a grid of 512×512 tiles. Each tile costs 170 tokens.

• The Fix: If your image's shortest side ends up being 769px, OpenAI will spill over into an entirely new row of 512×512 tiles, doubling your cost. vision-squeezer simulates this exact math and snaps the image down by a few pixels so it fits perfectly into the minimum number of tiles.

3. Gemini 2.0 / 3.0 (Massive Tiles)

Gemini uses a massive 768×768 tile system (if the image is > 384px). Each tile is a flat 258 tokens.

• The Fix: An 800×600 image will trigger a 2×1 tile grid (1,032 tokens). vision-squeezer snaps it down slightly to fit exactly inside a 768×768 box, dropping the cost to 258 tokens (%75 savings).

4. Llama 3.2 / 3.3 Vision (560px Tiles)

Meta's Mllama vision tiles images on a 560×560 grid, capped at 4 tiles (~1601 tokens each).

• The Fix: A 2400×1670 screenshot trimmed to 2400×1200 drops from a 2×2 to a 2×1 canvas: 6,404 → 3,202 tokens (−50%). (Llama 4 uses a different vision encoder and is not modeled.)

5. Qwen2-VL / 2.5-VL / 3-VL (28px Patch Grid)

Alibaba's Qwen-VL uses a 28px effective grid (14px patch × 2×2 merge); tokens = (W/28)·(H/28) bounded to [4, 16384].

• The Fix: The patch is small, so area is the lever — a 1024×1024 image with its border stripped to 896×896 drops 1,369 → 1,024 tokens (−25%).

6. DeepSeek-VL2 (384px Anyres Tiles)

SigLIP-384 + 2× pixel-shuffle gives 196 tokens/tile on a (m·384, n·384) canvas (m·n ≤ 9).

• The Fix: An 800×768 image snapped to 768×768 drops from 3×2 to 2×2 tiles: 1,415 → 1,023 tokens (−28%). (Open weights — the win is local-inference context, not API billing.)

Full provider math, exact formulas, and cited sources: visionsqueezer.com/providers