Qwen 3.8 27B is now live in Command Code.
Beats Opus 4.6 Max, GPT 5.6 Luna Max on Artificial Analysis Intelligence Index.
Best for coding and long agent tasks. Multimodal with reasoning toggled on/off
Available on all plans & API
🐐
GLM-5.3 is already live in Command Code.
Same price as GLM-5.2, ~50% better on Zai's internal Code Bench.
Still 5–10x cheaper than the frontier closed models (per 1M output tokens, list price today).
Plans: $1 Go ($10 credits) · $10 GOAT ($20 credits)
GLM-5.3 is pretty cheap:
10x cheaper than Fable 5
6x cheaper than GPT-5.6 Sol
5x cheaper than Opus 5
🐐
Launching `a11y` mode in `/design`
Run `/design a11y` to catch:
• Tap targets that are too small
• Focus management problems
• Broken keyboard navigation
• ARIA and accessible name gaps
• Missing or incorrect form labels and errors
Run `cmd update`, then try `/design a11y`
Learn more about our /design command here: https://commandcode.ai/docs/design
GPT 5.6 Sol is now in Command Code GOAT
Best coding plan with best GPT model. $70 credits.
$10/mo GOAT plan gets you:
GPT Sol has $70 credits
~105M tokens ~2.1K reqs
We've also achieved 99.43% cache hit ratio.
Available on GOAT + above.
Limited time deal.
🐐
Tested FlappyBench with GLM 5.3, Fable 5 and GPT-5.6 Sol
3 models. Same prompt with /design command.
Scored on features, UX/UI, and cost.
🔹 Fable 5 → 9.5/10 · $0.420
🔹 GLM 5.3 → 9/10 · $0.018
🔹 GPT-5.6 Sol → 9/10 · $0.150
Results:
→ Fable 5 wins on quality, but costs 23x more than GLM 5.3
→ GLM 5.3 gives better output than GPT 5.6 Sol at 8x cheaper
→ Optimizing for cost? Go for GLM 5.3. Otherwise, Fable 5 if quality matters
Our engineering and design team has been testing 26+ side-by-side comparisons across frontier and open models. All runs are public and open source. Benchmark for this demo here: https://github.com/CommandCodeAI/slash-design-showcase/tree/main/flappy-bird
GLM 5.3 is live in Command Code (2x usage)
The best deal: 2x usage, permanently. 🐐
$10 GOAT plan has $20 worth of GLM 5.3 usage.
That's ~70M tokens & ~1.3K requests.
- Text-reasoning. 1M context
- IN $1.4/M · OUT $4.4/M · Cache $0.26/M
- Available on all plans & API
GLM-5.3 is the most capable open-weights model for coding. Right there with Kimi K3 on terminal bench.
GLM 5.3 is easily the most impressive GLM model yet.
Excited to partner up and ship GLM 5.3 in Command Code AI soon.
A fun internal eval we run at Command Code: deliberately trap the model in a loop and see what happens. Every GLM model so far just... keeps looping. GLM 5.3 is the first one to notice, go “wait”, and break out.
It’s a small bench (15 loop traps), but escaping a loop you were intentionally placed in takes a little flicker of metacognition, and it’s fun to watch that show up.
Bonus: it plays very nicely with our new tool defer harness → ~50-60% token savings on an average session. Capability going up while cost going down is the good stuff.
Gemini 3.7 Flash is now live in Command Code at 50% OFF.
- It's fast with frontier level intelligence
- $0.75 input/$3.75 output per 1M tokens
- Available on GOAT, Max and Team plans
Starts at $10/month
$ npm i -g command-code
DeepSeek V4 Pro already live in Command Code
Plans
🔹 $1 Go ($10 credits)
🔹 $10 GOAT ($70 credits usage) and above!!
DeepSeek V4 Pro GA is pretty cheap:
Tested FlappyBench with Grok 4.6, GPT-5.6 Sol, and Opus 5.
3 frontier models. Same prompt with /design command.
Scored on gameplay features, UX/UI, and cost.
🔹 Grok 4.6 → 9.5/10 · $0.095
🔹 GPT-5.6 Sol → 9/10 · $0.150
🔹 Opus 5 → 9/10 · $0.253
GPT-5.6 Sol and Opus 5 gave comparable output, but Opus costs 1.7x more.
Grok 4.6 gave best output with lowest cost: smooth motion & clean UI.
Grok 4.6 by xAI is now available on Command Code GOAT plan and above.
Update to latest Command Code: npm i -g command-code@latest
Switch any time with /model
Tested DeepSeek V4 Pro 0813, Kimi K3, and GLM 5.2 through FlappyBench.
3 models, same prompt with the /design command.
Scored on gameplay features, UX/UI, and cost.
🔹 DeepSeek V4 Pro 0813 → 8/10 · $0.0005
🔹 Kimi K3 → 9.5/10 · $0.0740
🔹 GLM 5.2 → 9/10 · $0.0480
DeepSeek is 148x cheaper than Kimi and 96x cheaper than GLM, for 84% of the quality. But $0.0005 for a playable one-shot game is insane.
Kimi K3 had the best output, with GLM 5.2 close behind.
Our engineering and design team has been testing 26+ side-by-side comparisons across frontier and open models. All runs are public and open source. Benchmark for this demo here: https://github.com/CommandCodeAI/slash-design-showcase/tree/main/flappy-bird
DeepSeek V4 Pro 0813 (latest) is live in Command Code. 🐐
🔹 Same price and same model ID
🔹 DeepSeek-V4-Pro-0813 is the GA release
With free tool calls repairs and cache repairs available on all plans and provider api.
Much better than flash in benchmarks.
Available across all plans. Try now with Command Code GOAT plan.
Code reviews should help you ship faster, not block you.
Run /review [PR number] and get your code reviews directly in the terminal
Try now with our $10/mo GOAT plan.
Read docs: https://commandcode.ai/docs/plans/goat
Use /plans to align your workflows
- Browse, review, and annotate saved plans.
- Select plans from current session vs all.
- Search across all plans.
Read more in docs: https://commandcode.ai/docs
Muse Spark 1.2 by Meta now live in Command Code
🔹 Muse Spark 1.2
🔹Muse Spark 1.2 Contributor
(cheaper than DeepSeek V4 Flash and scores higher in intelligence)
We had to repair a lot of tool calling issues on this model, so have fun
Features:
Also check out new $10/mo GOAT plan $70 credits. 🐐
Plan mode removes the guesswork. When the change is too big for a quick fix:
No surprises, just clarity.
Qwen 3.8 Max is now live in Command Code Go.
… and it's going open weight!!
most importantly, next week open-weights of Qwen3.8-Max and Qwen3.8-27B will be released!
🔹 2.4T params MoE flagship
🔹 Input: $2 Output: $6
🔹 Cache: $0.25 / M tokens
3.8 is 20% cheaper than 3.7
Try now! npm i -g command-code@latest
https://reddit.com/link/1vdkcgz/video/ry4n1ndl4mgh1/player
Save your favorite prompts forever and reuse them anytime!!
Create custom slash commands and use them frequently.
🔹 Save a prompt as a markdown file
🔹 Define its scope per project or user level
🔹 Run it anytime with dynamic arguments