Run your coding agent on open models.
Pay per token in the cloud, or stay offline on your Mac with a local model. Both take one pip install.
GLM 5.3, GLM 5.3 Flash and DeepSeek V4.1 Flash. No subscription, no card to try it.
Pick how you run.
From $0.10 input, $0.03 cached, $0.50 output per million tokens
Pricing and setupModels
100+ OptiQ models and 80+ community quants on Hugging Face, mixed precision, built for Apple Silicon, and three in the cloud.
In the cloud
| Model | Input | Cached | Output | Context |
|---|---|---|---|---|
| GLM 5.3 Flash | $0.15 | $0.03 | $0.50 | 1M |
| DeepSeek V4.1 Flash | $0.10 | $0.03 | $0.50 | 1M |
| GLM 5.3 | $1.40 | $0.26 | $4.40 | 1M |
Per million tokens.
On your Mac
| Model | Notes | On disk | Capability |
|---|---|---|---|
| Qwen3.8-27B-OptiQ-4bit | dense, vision, MTP | 20.7 GB | 87.98 |
| gemma-4-31B-it-OptiQ-4bit | dense, vision | 23.5 GB | 82.77 |
| MiniCPM-V-4.7-35B-A3B-OptiQ-4bit-REAP-19B | expert-pruned MoE, vision | 14.6 GB | 81.91 |
On disk includes the vision tower and MTP head; Capability Score from each model card.
Measured on real coding tasks.
Each number is from a run we published, with the method on the linked page.
tasks solved with Boost, against 4 for the local model alone, for $2.80 in total.
The 30-task runof SWE-bench-Lite resolved by OptiQ Code on a 4-bit 4B local model, against 25% for mini-swe-agent on the same model.
Harness comparisonUnder the hood: the CLI.
The same engine runs all three. Use it directly for serving, quantizing and pruning models yourself.
CLI reference- optiq serve --model <repo>
- An OpenAI- and Anthropic-compatible server for any client.
- optiq convert <repo>
- Measure each layer's sensitivity and build a mixed-precision quant.
- optiq prune-experts <repo>
- Remove half of a mixture-of-experts model's experts.
- optiq serve --cloud-only
- The cloud models behind a local endpoint, with no model loaded.