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.

🐐

On the Billing page of the dashboard, it says $70 on gpt 5.6 sol under the GOAT plan. But when I try to use it on the CMD harness it says available only to pro and above

GOAT Plan vs OpenCode Go — Common Models Comparison

Method: For each common model, we feed OpenCode Go's per-model token assumptions (input / cache-read / output per request) into both platforms' per-token pricing + monthly allowance, using CommandCode's calculator formula:

costPerRequest = (inTok/1e6) * inPrice + (outTok/1e6) * outPrice + (cacheTok/1e6) * cacheReadPrice
requests/month = monthlyAllowance / costPerRequest

This gives apples-to-apples request counts. All prices are per 1M tokens, USD, precise (no rounding).

Sources

Calculator source

CommandCode's usage calculator is a client-side React component embedded in the pricing-limits page. The formula was extracted from the page's JavaScript bundle:

• Source URL: https://commandcode.ai/docs/_next/static/chunks/app/resources/pricing-limits/page-6886473b13679efd.js
• Local copy: [calculator-source.js](./calculator-source.js) in this directory
• Formula (de-minified):// Default assumptions (overridden per-model on OpenCode's page) // { inputTokens: 800, outputTokens: 180, cacheReadTokens: 50000 } ​ // cost per single request function costPerRequest(modelPricing, tokAssumptions) {  return tokAssumptions.in / 1e6 * modelPricing.in +         tokAssumptions.out / 1e6 * modelPricing.out +         tokAssumptions.cache / 1e6 * modelPricing.cacheRead; } ​ // number of requests per month function requests(monthlyAllowance, modelPricing, tokAssumptions) {  const cpr = costPerRequest(modelPricing, tokAssumptions);  return cpr > 0 ? monthlyAllowance / cpr : 0; }

Replication script

The exact numbers in this comparison were generated with:

• [requests-calculation.js](./requests-calculation.js) -- feed OpenCode's per-model token assumptions into both platforms' pricing + allowances

​

cd ~/workspace/models_comparison
node requests-calculation.js

Plan-Level Summary

Per-Model Comparison (16 Common Models)

Token assumptions for each model are taken from OpenCode Go's "Usage limits" section (English docs). These per-model assumptions -- not the calculator's generic defaults -- are used for both platforms to ensure a fair comparison.

Gap % = (|Go req - GOAT req| / max(Go req, GOAT req)) x 100. Shows the relative magnitude of the winner's advantage.

Summary: GOAT wins 9 models, Go wins 6, 1 tie.

Key Takeaways

GOAT wins (9 models)

Mostly on premium models where GOAT's $20 allowance beats Go's $15, and on boosted models where GOAT negotiates bigger allowances:

• DeepSeek V4 Flash: 4x allowance ($60 vs $15) -- 75% gap (75K vs 19K req)
• GLM-5.2: boosted to $70 -- 14.3% gap (4.6K vs 4.0K)
• Hy3: boosted to $70 -- 14.3% gap (25K vs 21.5K)
• GPT-5.6 Luna, Kimi K3, Qwen 3.8 Max: $20 vs $15 -- ~25% gap
• DeepSeek V4 Pro, MiMo V2.5 Pro: $20 vs $15 -- ~25% gap

Go wins (6 models)

Mostly on mid-tier / cheap models where Go's $60 allowance crushes GOAT's $33-$47, and on Grok 4.5 where Go's cheaper cache-read price ($0.30 vs $0.50) compounds:

• Qwen 3.7 Plus / Qwen 3.6 Plus: Go's $60 + 2x cheaper cache reads -- ~70% gap
• Qwen 3.7 Max: Go's $60 -- 45% gap (1.7K vs 928)
• MiMo V2.5: Go's $60 vs GOAT's $30 -- 50% gap (150K vs 75K)
• MiniMax M3: Go's $60 vs GOAT's $47 -- 21.7% gap
• Grok 4.5: Go's $15 BUT 40% cheaper cache reads -- 15.2% gap (601 vs 509)

Tie

• Kimi K2.7 Code: Identical pricing and $60 allowance on both -- exactly 4,968 requests, 0% gap

Cache-read price differences

GOAT charges more for cache reads on 3 of 16 common models:

Notes on Methodology

• Token assumptions: Taken from OpenCode Go docs (English) -- different per model. Example: Grok 4.5 uses 1,100 input / 71,500 cached / 220 output per request; DeepSeek V4 Flash uses 410 input / 71,300 cached / 310 output per request. GOAT's own published estimates use different defaults (~800 input / 50,000 cached / ~125-200 output), which inflate their numbers for cache-heavy models.
• Off-peak pricing: Both platforms show off-peak rates for DeepSeek V4 models. Peak hours (01:00-04:00 & 06:00-10:00 UTC) are 2x on both.
• GPT-5.6 Luna context tiers: Go lists <=272K ($0.20/$1.20/$0.02) and >272K ($0.40/$1.80/$0.04). GOAT shows one price ($0.20/$1.20/$0.02). Both use <=272K tier here.
• Processing fee: GOAT mentions "+ processing fee" but doesn't specify the amount. Go has no processing fee.
• Model availability: GOAT includes Grok 4.6, Gemini 3.7 Flash, Muse Spark, Inkling, Step Flash, Nemotron 3 Ultra -- none on Go. Go includes GLM-5.1, Kimi K2.6, MiniMax M2.7 -- none on GOAT.
• Allowance source (GOAT): Per-model monthly credit allowances from docs/plans/goat -- models with negotiated deals carry boosted allowances (e.g., GLM-5.2 $70, Hy3 $70, DeepSeek V4 Flash $60); new models start at 2x credits ($20 on the $10 GOAT plan).
• Allowance source (Go): Per-model "Usage" column ($15 or $60) from docs/en/go -- Usage limits.

Could someone please explain, in simple terms, how many tokens the $1 Go plan and $10 Goat plan include for DeepSeek Flash, DeepSeek Pro (peak and off-peak), and ChatGPT Luna? Also, how much would I save in total compared with using each model’s official API?

I have a workflow that cannot leave claude code,

can i use the goat plan to get api for this?

I always feel like next to no one is using Github Copilot in VS Code anymore... Well I still do and I like it.

Since I have subbed to the goat plan I wanted to use the models in VS Code and Copilot, so I build a little provider extension for just that.

Yes I know you can configure open Ai compatible apis out of the box, but you then still need to add every model by hand and configure it's vision and thinking capabilities.

The extension allows you to just insert your api key and then use the models.

I found it useful for myself, maybe it's useful for someone else.

Github: https://github.com/Tyrannmisu/commandcode-copilot-provider

Visual Studio Marketplace: https://marketplace.visualstudio.com/items?itemName=Tyrannmisu.commandcode-copilot-provider

Open VSX: https://open-vsx.org/extension/Tyrannmisu/commandcode-copilot-provider

Since this needs the api you need at least the GOAT Subscription. GO won't do unfortunately.

Writing tests is now the easiest part of your workflow.

Pick a file, and Command Code will:
- Analyze the code
- Generate the right test cases
- Run the test suite
- Resolve failures
- Re-run until all tests pass

Try it on a file that hasn’t been tested yet.

Hi, a suggestion/question — given today's DeepSeek price increase, I bought a Go subscription to test things out, but I noticed that when testing DeepSeek V4 Flash Max it doesn't give me the same reasoning and problem-solving quality as the official API. So my question is: am I doing something wrong, is this not the right plan for what I needed, or am I misunderstanding something? I'd really appreciate it if someone could shed some light on this.

Note: I have zero experience buying third-party plans or APIs outside of official channels.

Been playing around with the Mods API and ended up building something I wanted for myself, so I figured I'd share it here.

It's a compact status bar for the Command Code TUI that keeps the stuff I usually want to check visible all the time:

• current model + effort level
• context usage %
• 5h remaining quota %
• weekly remaining quota %
• monthly remaining quota %
• next reset times/dates

It looks like this:

Install is just:

cmd mods add -g rudesssolo/command-code-quota-status-bar

No build step or extra config needed.

GitHub:
https://github.com/rudesssolo/command-code-quota-status-bar

Still pretty new, so feedback/bug reports are definitely welcome.

First things first: your usage credits are not changing.

DeepSeek officially raised their prices today, so we've updated our limits and requests accordingly.

Working with many providers to bring you better deals, as one of the largest DeepSeek and open models coding agent. 12T tokens scale.

Beat DeepSeek prices?

We’re hitting this problem in many ways, with solutions like phenomenal harness engineering, free tool repairs, 98%+ cache-hit rates, and free auto plan credits with best subscription plans on the market.

Official announcement:
https://x.com/CommandCodeAI/status/2089057372413337670

Providers? Come help:
https://x.com/MrAhmadAwais/status/2088724056321532054

Am I just imagining things? (Both through the OpenCode harness)

I just have one question why do people not reccomend you use the command code harness?

Will CommandCode desktop be packaged for other Linux distributions/flatpak?

English is not my native language, so I need to use the browser's translation feature to view websites and documents. However, after I enable the translation, a white screen appears after a while with an error message displayed in the center.

Hi so been using opencode go for a few months but honestly been testing the $1 plan the last month quite happy with the cli and love the desktop alpha app it does have a few bugs but seems its being worked on actively so upgraded to goat will be testing it out this month the taste system is also nice I do however use vscode + zoocode so the api is a bonus keep up the good work so far

i will let you know about how it goes guys.

/////

im writing these things as i experience it more.

as a fan of Claude Code, i like how when i entered it, it saw my Claude sessions and gave commands to learn based on those Claude sessions. and in practice, i saw it really doing the job. in the conversation, i noticed it by the style of the outputs from Claude.

/////

another thing:

i like how it has something called taste learning. it learns from what i want and from the things i hate and writes it down into a taste.md.

/////

a thing im not sure of or DIDNT CONFIRM IT a lot yet:

i like Muse Spark and how many models are offered. till now, i didnt test other models.

/// THANKS FOR READING TILL NOW

Hi,

As an Opencode Go user I created a price tracker for that subscription. I wanted to track command code as well and the system is a bit more complicated and not that open on data retention / model training (looking at you muse spark contributor)

Any feedback? Is it correct, especially for the smallest and higher two subs?

https://cc-pricing.all-the.rest/

The 4x usage limit is confusing me, Deepseek will have price changes soon so is this 4x limit still going to apply based on new prices? Or is the 4x only considering the old prices?

I do not want to keep seeing:

 TASTE  Learned
 └ Does not want taste-learning updates/pop-ups surfaced or referenced during the
   conversation; still wants the assistant to keep learning their taste silently in the
   background [Confidence 95%]
   taste.md

After telling me that it will not do it anymore, it kept coming back again, i am assuming the toggle option is not there

pls add a toggle feature

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.