THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Explore AI models 2

Provider Input Output License

Discover models worth your attention

qwen3.5-4B-super-coder

jica98

`qwen3.5-4B-super-coder` is a 4-bit quantized GGUF model optimized for fast, reliable coding, structured tool calling, and active reasoning (thinking mode) on consumer/mobile hardware. It is distilled from Claude Sonnet 4.6 & Opus 4.6, and merged/quantized using Unsloth. - **Base Model**: `Qwen/Qwen3.5-4B` - **Format**: GGUF (Q_4.0 Quantization) - **Size**: ~2.6 GB - **Context Window**: 32K (optimized for mobile RAM budgets, natively supports up to 262K/1M context via YaRN) - **Key Architectural Advantage**: The base `Qwen3.5-4B` model uses a hybrid architecture combining **Gated DeltaNet** (3 layers) and **Full Attention** (1 layer) repeating. Since only 8 of the 32 layers store a full KV cache, the KV cache footprint is incredibly small (~0.4GB for 32K context), making it exceptionally well-suited for high-context coding on mobile devices (e.g., iPhone 15 Pro+, flagship Android, iPad Pro). This model was trained using a staged Supervised Fine-Tuning (SFT) pipeline to systematically inject reasoning capability, coding specialization, and tool-calling precision:

Text generation

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

qwen3.5-9B-super-coder

jica98

**Example usage**: - For text only LLMs: `llama-cli -hf jica98/qwen3.5-9B-super-coder --jinja` - For multimodal models: `llama-mtmd-cli -hf jica98/qwen3.5-9B-super-coder --jinja`

Category pending

Input Not verified→ Output Not verified

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.