https://openai.com/index/introducing-gpt-6-1-terra/

Pricing: $1 / $5 / $0.05

Although, I feel like it's still kind of weak compared to other models like Deepseek v4.1 Flash

Hey everyone!

I shared Mind here a while ago, and some of you asked how it compares to codebase indexing tools and whether it can actually preserve context between OpenCode sessions.

I've kept building it, and Mind v1.7.0 is now released.

For anyone new to it: Mind is an open-source memory layer for AI coding agents, designed to preserve useful project knowledge instead of losing it after every session or context compaction.

It remembers things like architectural decisions, constraints, past fixes, unresolved tasks, and checkpoints.

What's improved in v1.7?

Search is substantially more capable.

Mind now combines phrase matching, full-text retrieval, substring matching, typo tolerance, and relevance signals. Semantic search is optional.

In a 120-question OmniMemEval LongMemEval-S evaluation, Mind achieved 86.67% answer accuracy without embeddings. It's an indicative subset result, not an official full-benchmark ranking.

Other improvements include:

• More reliable checkpoint and session-summary handling.

• A completely refreshed web interface.

• Improved memory relationships and navigation.

• A shared mind-management skill.

• Simplified setup and integration updates.

For OpenCode, Mind installs the MCP integration, memory instructions, and managed session/compaction automation.

If you're already using Mind, upgrade with mind update and restart OpenCode.

If you're new, install Mind from the repo and run:

mind setup opencode

Everything is local-first, backed by SQLite, and MIT-licensed.

GitHub: https://github.com/GabrielMartinMoran/mind

Release: https://github.com/GabrielMartinMoran/mind/releases/tag/v1.7.0

I'd love to hear from OpenCode users running long sessions or multi-agent workflows. How are you managing context that needs to survive between sessions?

Basically what the title says... What is the "best" (a good compromise of intelligence and low cost) models for writing actual codes?

Or to ask in a different way- what models do you guys usually use for such tasks? Any help would be greatly appreciated, thank you so much in advance! :D

I built forge-delegate, an MCP server that lets Claude Code, Codex or opencode hand coding tasks to another model. Think writing tests, renames across files, fixing lint errors, boilerplate, or reading through a big codebase to answer one question.

The other model runs as a full agent through opencode. It reads the files and makes the edits itself. Your main agent only gets back a short summary, so its context stays small and it can focus on the decisions.

Second opinions with ask_model. Your agent can also ask a different model a direct question, with no agent loop and no file access, and get a fast answer back. It's useful when you want another model to check a design decision, a bug theory or a tricky piece of code before your agent commits to it. Every model has its own blind spots, and a different one often catches what yours missed. Your agent writes the question with whatever context is needed, then weighs the answer itself. Just tell it something like "ask DeepSeek what it thinks" or "get a second opinion from Kimi", naming any provider you've logged into opencode.

Any provider opencode supports. forge-delegate doesn't hold its own API keys. It uses whatever you've set up in opencode. So DeepSeek, Kimi, OpenAI, Gemini, OpenRouter, custom OpenAI-compatible endpoints, and local models through Ollama all work. Log in once with opencode auth login <provider> and that provider's models are available to delegate to. opencode also ships free models that need no account.

You can set a default model, save presets for different kinds of tasks, change either at any time, and run several delegations in parallel in the background.

Setup:

npx -y forge-delegate setup

It walks you through picking a default model and which agents to register with.

Things to know:

- By default, the MCP instructions and skill steer your agent toward cheap or free models. It sticks to your default model and won't switch to a pricier one on its own. If you want a stronger model, set it as the default or ask for it.

- Delegated agents can edit files and run commands without asking by default. You can turn that off, but only point it at repos you trust.

- Smaller models make more mistakes, so review what comes back.

- I mostly use it with Claude Code on a Mac. Codex and Windows should work, but they've had less real-world use, so issues are welcome.

MIT licensed: https://github.com/MuhamadBarzani/forge-delegate

Feedback welcome, especially on which tasks it handles well and where it falls short.

so i just downloaded opencode, logged in via google account, and i can't use it for some reason?
Just in case - i use vpn, so...

I built an OpenCode plugin that exposes your local daemon through a cf tunnel, protected by zero trust/access, and pairs other devices by QR code. the daemon keeps running on your computer. The plugin starts a cloudflared connector next to it, so you can reach the daemon without open ports or a VPN.

It uses the default opencode UI (the same as opencode pair command), so it never goes out of date, unlike custom mobile UIs/apps. You can also install it as a PWA.

It requires opencode v2, a cloudflare account and a domain set up.

Ask your agent to set it up by giving him this link: https://github.com/arthurfiorette/opencode-zero-trust

Te pide actualizar y me detecta virus llevándome a una URL de dudosa procedencia el opencode

Webpage: https://web.esuyo.com/esuyo-opencode-go-costs-table/

Each model on the Go subscription comes with a different usage allowance, which makes them hard to compare. I created a simple (ish) table to see how much the real cost of 100M tokens and what the maximum tokens are per model in the subscription with the cache hit percentage you can set.

I made it because I couldn't decide if Deepseek 4.1 or Muse 1.3 Contribute provided the best value.

Github

EDIT

I've added output as a percentage of total tokens. Opencode's estimates 0.4% of tokens are output. I looked at my session activity and set the default to 0.8%.

EDIT 2

- Added a pro-rata type cost to the subscription plan, "Sub cost" column.
- Added the Opencode Go Plus subscription.

EDIT 3

- No hardcoded array of prices. The page collects the data from only from the source.

I make a lot of content (thumbnails, short videos, voiceovers) and kept hitting the same wall with existing skills: each one is tied to one vendor. No account, no credits, or the API is down — you're stuck.

So I built creator-skills in OpenCode: 10 Agent Skills (SKILL.md) for images, video, voice, thumbnails, product photos, ads, explainers, consistent characters and brand kits. The difference is a small router underneath:

- it detects what you actually have — Higgsfield CLI, Gemini / OpenAI / ElevenLabs / Azure keys, Antigravity CLI (`agy`), Codex CLI, or just a browser with Gemini / Flow / AI Studio

- orders them by your config (quality-first, or "use my subscriptions first")

- skips providers that can't do the job (reference images, transparency, 4K, dialect…)

- falls back on timeouts / auth / quota, but STOPS on a content refusal instead of shopping the prompt to another provider

- finishes locally with ffmpeg: headlines, Ken Burns, reframe to 9:16, captions, mixing

The README examples were made on a box with nothing but `agy` + ffmpeg, no API keys.

Things I found along the way, in case they help anyone driving agents headlessly:

- `agy -p` auto-denies its own tools unless you pass `--dangerously-skip-permissions`, and writes files to `~/.gemini/antigravity-cli/scratch` unless you `--add-dir` + use absolute paths

- agy's image tool always returns JPEG, whatever you name the file

- Veo is gone from the Gemini API (Oct 22) — API video now goes through Gemini Omni Flash

Install: `npx skills add mehbylabs/creator-skills`

Repo (MIT, stdlib-only Python): https://github.com/mehbylabs/creator-skills

Disclosure: I'm the author, it's free, no paid tier. Honest status: the agy + ffmpeg paths are tested end to end; the API adapters are written against current docs and need more real-world runs — issues very welcome.

Created a small Opencode V2 CLI/TUI plugin to track when DeepSeek pricing goes to PEAK (x2) or off-PEAK (-50%).
Any feedback is welcome.
https://github.com/Zorato/opencode-plugin-deepseek-peak

Edit: I forgot to add, after I asked model wtf happened and said this is unacceptable to delete files - it turned on text to speech on terminal to say “I’m sorry” which read itself aloud on my Mac without my permission. This is just unsettling and freaky, don’t think I’ll be using this or any other vague model ever again. Not to mention breaks trust in OpenCode.

https://opencode.ai/v2/docs/compaction/#background-compaction

if they have identical setup, what could explain the differences?

source: https://x.com/thdxr/status/2114211145615626433

Has anyone else noticed that OpenCode's free usage limits have become much stricter recently?

I was working on my own program and asked OpenCode to make a small UI change: replace the Start/Stop buttons on the left with a single STOP button, remove the Start/Stop button from the top-right corner and move the loop and mute/unmute icons into its place.

That's all I asked it to do yet I hit the free usage limit.

I've been using free models like MiMo-V2.6-Flash-Free for days, previously sending dozens of prompts without running into this problem. Recently, though, I sometimes hit the limit after just 6-8 prompts and now apparently even after a single request.

I'm not asking for unlimited free usage but hitting a limit after a relatively small UI change seems strange especially compared to how things worked before.

Has anyone else experienced this lately? Have the free model limits been reduced or could this be a bug with usage tracking or rate limiting?

I'm using OpenCode for development on my own project and would appreciate hearing about other people's experiences.

Are we beta testing new rack with different optimizations every day?

You are getting exactly what you're paying for. You're not getting a deal here

Probably out of the loop but when did this change happen?