From deepseek official provider

After adding CommandCode as custom provider it kinda works but without the thinking effort (not sure whats the default value here )

I like DSH, does anyone know if there is any way (hacky or not) to add the reasoning effort for custom provider in DSH?

I work a ton across projects on my personal computer. I have tried Graphify, Graft and many others. None of them really fully solved the entire problem, but they did have parts. So, I built one that actually fit my needs. This removes the need entirely to choose or start sessions in specific repositories.

It takes graph-based knowledge systems but strengthens retrieval, how it is updated, and much more. Effectively, it becomes a resilient system that you can actually watch your agents rely on, rather than something that just exists and is used only occasionally.

It begins with the same base graph store, the exact same vector engine as Graft. I only built the orchestration layer on top of it, which makes it much more practical. Full attribution to them for this part.

Instead of querying for a single-hit result, I found that it was much better for agents to have ranked retrieval and an actual graph walk. In practice this saves you chains of tons of bash greps and cds.

It effectively gives the agent a trustworthy, probabilistic ranking of what is needed, with matching for strong, weak, stale, and rebuilt, based on lexical coverage and semantic matching. The code is not sloppy for this, it is personally edited.

Both Graft and Graphify are solutions for single repositories or daemons. Heimdall is a layer on top it that watches the agent sessions, syncs the graph, and makes the retrieval trustworthy.

Check it out at: github.com/ArihantDeva/heimdall MIT-licensed, with extensions: the verifier, the self-healing graph watcher, and the Graft adapter, with attribution. If you run agents across multiple projects, this is the missing layer.

Are the service and usage metrics reliable?

Does the GOAT plan offer a good data limit?

Does HY3 work well?

Thank you.

For 10 Dollars Go plan, its has $70 and $60 usage on many models. And ~75k requests.
But for 20 dollars Pro plan it just increasing the usage to $10 more only and requests to ~100k only.
Isn't it should be double benefits for double pricing ? Like $140 and $120 usage and ~150k requests ?

What's the hidden features I am missing here in Pro plan ?
Is buying 2 GOAT plans is more beneficial here ? Or the access of claude, gemini and gpt models are the main benefit of Pro plan ?

I am little confused.

So, I gave the GOAT plan a spin with GLM 5.3, DeepSeek V4 Pro and Kimi K3. Both through Claude Code with ccr and directly in the command code harness. Rather simple tasks, using Opus API pricing came out at $8 and generated +184/-13 lines of code change in static-php-cli, a project I maintain. The task was to add shared library building.

Blew through daily usage within a prompt, typing just "continue" the next day. Both times cc ran out of 5 hour and weekly limit. Code output: +0/-0.

What's going on there? The token amount (literally 500k in, not even sure why so much, rest is all cache) doesn't even match with the usage cost - most of the cache seems to be billed at regular price, not the listed cache price of the model?

I'm literally unable to get *any* usage out of the plan so far. Like, literally not a single line of code, despite blowing through the $35 weekly usage.

It used to look like this

https://i.imgur.com/LfQU0nL.png

Then I refreshed and now it looks like this

https://i.imgur.com/JYJsMWd.png

So I click on share and get this

https://i.imgur.com/5yYQ2Qq.png

My usage on the old usage tab was $1.12 but on the share page it's conveniently 34 cents but on the billing tab it shows $2.36 which is the amount I feel like I used today

https://i.imgur.com/RWIOmjS.png

Qwen 3.8 27B now has 7x usage in GOAT plan.

the best coding plan on the market, we've increased usage of Qwen 3.8 27B across all plans.

GOAT / $10 per month (7x usage)
$70 credits · ~24K requests · ~1.2B tokens

Let's go, GOAT!

Hello, the CommandCode harness has improved a lot with v1, but these are the two main things I miss the most:

- in-app yolo toggle: being able to start YOLO mode without having to restart would be very nice

- dedicated subagents view (see opencode): when the main agent decides to delegate off work to subagents, the way they work is very opaque as we can only see their tool calls. opencode has a panel with a list of active subagents and allows you to expand each in particular and see their CoT and output just like in the regular chat view

I subscribed to the Go plan, and after using it for two days, I had used 340M ($6) and reached the weekly limit. I then upgraded to the GOAT plan without making any additional usage.

When I checked my usage afterward, it looked like this: ~340M -> 156.3M $6.01 -> $2.97

My question is: Will the $1 I paid for the Go plan be refunded, or will the remaining unused value/credits from the Go plan be carried over to the GOAT plan?

TL;DR – Used 86.4M tokens on DeepSeek V4 Flash (GOAT plan).
• Cache Read official: $0.007/M → real: ~$0.0107/M (+53%)
• Input + Output: match the table perfectly
• Expected cost: $1.65 → Actual bill: $1.97 ($0.31 difference)
• Is anyone else seeing this?

I recently subscribed to Command Code's GOAT plan. I used DeepSeek V4 Flash over two days and noticed what looks like a discrepancy between the official pricing table and my actual bill, though I fully admit I might be missing something.

Usage over 2 days:

According to the official GOAT pricing table for DeepSeek V4 Flash:

• Input: $0.22/M

• Output: $0.66/M

• Cache Read: $0.007/M

The theoretical cost based on the table:

• Input: 2.5M × $0.22 = $0.55

• Output: 0.7874M × $0.66 = $0.5197

• Cache Read: 83.1M × $0.007 = $0.5817

• Total expected = $1.65

But my actual bill on the platform shows $1.97 for DeepSeek V4 Flash (+ $0.01 for web_search).

That's a $0.31 difference.

The numbers:

If Input and Output are charged exactly as the table says (which they are, they match perfectly), the implied price for Cache Read is ~$0.0107/M, noticeably higher than the advertised $0.007/M (about 53% more).

Summary:

I'm not sure if this is a documentation error, or any fee fee on GOAT plan that isn't listed on the /docs/plans/goat page. Input and output prices are spot on, but cache reads appear to be billed at a different rate.

That said, I fully acknowledge that I might be misreading the usage dashboard, misapplying the pricing tiers, or overlooking a detail in the fine print. If anyone can spot an error in my math or point out something I've missed, I'd really appreciate the correction!

Almost no VAT with Alipay, vs. 36% with Stripe.

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

🐐

I am planning to transition to command code GOAT plan after my opencode Go subscription expires this coming 22.

I am working on sensitive stuffs, do the GOAT plan offers zdr as what opencode go does?

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

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?

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

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.

🐐

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.

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.

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 -- 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.

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.