This is a DELLA merge of the following finetuned & post-trained models: | Model Repo | Purpose | | --- | --- | | EryriLabs/Ornith-1.5-35B-A3B-BigBang-MTP-GGUF | The base model, for terminal use, coding, software architecture, planning. Also provides MTP support | | Kwaipilot/KAT-Coder-V2.5-Dev | Contributes efficient coding with fewer tokens over more languages | | Accio-Lab/occamy-1.0 | Contributes general agent abilities, also a semi-official finetune | | AllSpark-Research/CompoWorld | Also contributes general agent abilities, different strengths/weaknesses from occamy | The result is an intelligent and efficient hybrid model for coding, terminal agent work, planning, designing, and conversation. Although most of this model's weights were merged with with a method that drops weights, more sensitive tensors such as the router gates and shared experts, were protected by using a density of 1.0 and epsilon of 0.0 to disable sparsification.

Text generation

Input Text→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-PLE-50pct

DarkKitsune

This model is ISTA-DASLab's GSQ-RCO IQ3_XXS quant of Qwen3.8-Flash-Next, but with the PLE (ngram) table pruned to 50% of its original parameters. This reduces the total size of the model further in return for an increase in perplexity. If using mmap without mlock, this probably won't make any noticable difference in VRAM usage, but with mmap *and* mlock (which boosts prefill speeds and lowers latency), there should be significantly less VRAM usage compared to the unpruned PLE table. The resulting model is ~61 GB (20% smaller than the original quant which is ~76GB), fitting nicely on 128 GB systems with ample room for the KV cache and other resources, even with mlock enabled. It is capable of chat, reasoning, basic tool calls & coding, but may stumble on more complex tasks. Compared to DarkKitsune/Qwen3.8-Flash-Next-UD-IQ3_XXS-PLE-25pct, this quant offers higher accuracy, and slightly higher performance.

Text generation

Input Text→ Output Text

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.

Find your ideal AI model.

Explore AI models 5

LFM2.5-8B-A1B-Opus-Distil-Q5_K_M-imat-GGUF

Qwen3.5-9B-Polaris-HighIQ-INSTRUCT-Q4_K_M-GGUF

Qwen3.6-Candid-Coder-35B-A3B

Qwen3.6-Qworus-35B-A3B-V1-APEX-GGUF

Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-PLE-50pct

No matching models

Great models. Clear costs.

Understand before you choose

Follow trends. Find tools. Explore models.