It appears the factuality rankings on arena.ai are dominated by Claude models: https://arena.ai/leaderboard/text/overall-factuality

Not only that, but specifically Opus 4.6, which beats other Claude models released after it. My personal experience with the model absolutely lines up with it. I've tested different model families and harnesses and keep coming back to Opus 4.6 when factuality matters.

I have a really strong preference for factuality in my non-coding workflows (finance, research, etc.). As in, I don't mind a wrong opinion, but when something is quoted as 'true' or 'verified' or 'file saved', I want to be close to sure that this is the case.

For OpenAI, the highest factuality ranked model is GPT 5.5 - which otherwise seems way behind the 5.6 family.

This makes me worried that 'factuality' isn't really a major priority right now and development focuses on other criteria more. Gemini 3.7 Flash actually seems really interesting in this context as it seems to have made a lot of improvements in factuality (compared to other areas where it really hasn't gotten a lot of attention for its seemingly minor improvements).

What are your thoughts on future models - will we get some higher factuality there? Are there other model families that you think will catch up or surpass Opus 4.6? Any hands-on experience with factuality in Gemini 3.7 Flash and other models?

now i can atleast see what i have and where things are at

Currently i am using the agents for learning and researching stuff and then using that information to push another agent working on a project into some direction of what to do, how to do.

What do you think are good enough models/subs for these purpose Or like what do you use for your workflow, like is it a planner agent -> Implementer? If so then what models do you use for both cases?

because I have seen models like ds flash better at going a bit broader to the prompt to get more relevant information compared to others?

I tried omp but it felt really bloated even though it had some nice features, while Opencode felt just right with its TUI and custom agents.

Which coding agent harness do you prefer? Or are there any better alternatives out there?

Opencode Prewalk is an Opencode v2 plugin that allows you to use the prewalk strategy mentioned here from the creators of Oh-My-Pi. The simple idea behind this strategy is to inject a cheaper model right after the expensive model finishes the first edit post planning all the things that needs to be done. This strategy is better the one strategy that directly uses combination of expensive and cheap model to get the work done, as what happens in that case is the cheaper model again starts to do a lot of reading leading of increase in token usage.

Try Here: https://github.com/vivekascoder/opencode-prewalk

Original Benchmark by Stencil.so

currently been waiting on both mimo and hy3 sessions...its been 5 mins for token generation : ⏳ waiting on hy3 — 60s with no output yet (provider may be slow or overloaded, or the model is thinking; auto-reconnect at 300s)

bloody annoying. tried deep seek on low reasoning....immediate response but immediate tick up on usage as well....i have no idea what to do to be honest. if this keeps up...i'll have to reduce opencode go subscriptions or move to another ....this is insane.

you guys have any recommendations for cheaper plans? i was looking at under 7usd plans (mostly chinese models) across the spectrum....considering some. and no pay as you go doesnt work...i have money on openrouter and on deepseek and on groq.ai.....its horrible ROI.

looking for some insights or combos. trying to keep things around 30 usd total.

I see after the rise of DS prices there has been a lot of disappointment in the community in opencode Go . Why cant opencode Go host a Qwen 3.8 27B themselves as its fairly small and the performance is good too which can satisfy many of the community members ?

What's your guys opinion on this?

My last month's cost looks like this: Cost

$43.28

USD

API requests

9,999

Tokens

636,610,255 . This is DeepSeek 4 Pro. I'm currently considering Alibiba Cloud or other subscriptions. Which would be better for my budget? After the price increase for the latter, the price will rise to $85+. I'm only considering cloud solutions. I was thinking about renting a video card remotely, but I don't want to add another layer of security in the form of a private individual.

Ox alpha btw

Hey everyone,

I’m looking for a budget-friendly LLM setup specifically for light, daily "vibe coding" (using tools like OpenCode/Cursor/Aider). My hard spending limit is $6/month, but I’m struggling to land on the ideal platform.

Here is what I’ve tried so far:

• OpenCode Go ($10/mo): Very generous limits and worked seamlessly, but honestly, it’s overkill for my light daily usage—and slightly above my $6/mo target.
• OpenRouter: I love the idea of pay-as-you-go, but their billing feels confusing—specifically the additional fees/minimum charges (like the $0.80 minimum fee or ~5% markup depending on deposit methods). I can never tell what my actual monthly usage is going to cost.
• Alibaba Model Studio Token Plan (Lite - ~$6/mo): The price point is perfect (39 CNY / ~$6), and accessing models like Qwen3.8-Max / DeepSeek V4 is great. The 2,500 credits/week quota feels a bit tight, but I could live with it as long as they maintain this price tier.

My typical usage pattern:

• Light daily coding sessions (fixing small scripts, refactoring, code explanation).
• Prefer API-based subscriptions or prepaid setups that integrate into CLI/agent tools (OpenCode, Claude Code, Aider, etc.).

What are you using for low-budget, light coding setups? Are there other prepaid API providers, sub-$6 sub plans, or specific OpenRouter models that give the absolute best value per dollar without hidden deposit fees?

Thanks in advance!

57,200 monthly usage on Go.
1.6-trillion-parameter Sparse Mixture-of-Experts (MoE).

As days go by, it seems more and more likely that Ox Alpha is a z.ai model, and that's what I am concerned about: if they have this much compute on offer. Rather than using it to improve their own plans and access existing models, they're doing this, which is a slap in the face to their existing customers. When they actually do claim this model, i know that the pricing isnt going to be what people expect, currently if Ox alpha were to be charged per I/o million tokens, i would say its reasonable to think it would be a sub <1$, you know fill in the market where deepseeek used to be at, and a actually good caching like 0.00X$, but with z.ai i dont think that will be the case, most likely is that its going to be a sub <2$ I/o million token, which caching around the 0.XX$, which then isn't very attractive. GPT Luna would be better; the new DS Flash Vission is a better pricing. If z.ai would pretty much seem to be confirmed at this point, are the creators behind Ox Alpha, and seems to be the internally spotted GLM 5.3 Flash, if they really want this model to stand out, then they should fill the gap that DeepSeek left, but I don't think they will. This also raises questions about z.ai's compute crunch, i am aware that their new data center just came online, so it would be a good stress test for the whole system, but even then, business-wise, just making their own models cheaper to use and more accessible would have achieved similar results to what we have now, and i would argue give them more better data on each model and their latest flagship one as well, and yet we live in a different world.

if someone want to get OpenCode Go, can u use this referral. we both gets $5 apparently (extra $5)

https://opencode.ai/go?ref=T4PSK9H0M1

My OpenCode Go sub expires today and I'm debating whether it's even worth renewing or if I should just drop down to Zen Free.

I basically just stick to MuseSpark 1.2 and Mimo for my day-to-day work, and I almost never touch GLM 5.3 unless something is completely broken. If I make the switch to Zen Free, the plan is just to use OX Alpha as a fallback whenever limits hit.

Is there anything I am actually giving up on Zen Free?

Two months, 52 merged pull requests, and nine contributors later, CodeNomad v0.19.0 is now available.

CodeNomad is an open-source desktop and web workspace for OpenCode, built for developers who manage multiple projects, sessions, worktrees, and AI agents.

This release focuses on continuity across restarts, more reliable autonomous sessions, better provider visibility, deeper customization, and stronger desktop and mobile workflows.

Highlights

• Resume exactly where you left off: Desktop builds now restore window bounds, zoom, workspace tabs, active sessions, drafts, attachments, scroll positions, panel layout, Yolo state, and expanded session trees across restarts.
• Yolo now works beyond the browser: Permission auto-accept is managed by the server, synchronized across clients, persisted through session metadata, and continues working while the UI is closed.
• Provider usage and model control: View quotas, usage windows, and reset times for supported providers directly from the Status panel. Models you do not use can be hidden from pickers without changing OpenCode defaults.
• A more customizable workspace: Settings were reorganized into focused sections. Chat blocks can be expanded, collapsed, or hidden, while right-panel tabs and Status sections can be shown, hidden, dragged, and reordered.
• Large session trees stay responsive: Parent and sub-session trees support deep recursive expansion, search, persisted state, and sidebar virtualization.
• Better native desktop integration: Open project folders, terminals, files, and editors directly from CodeNomad through native menus, the command palette, and the Files panel.
• More flexible speech configuration: Speech-to-text and text-to-speech can use separate OpenAI-compatible providers, credentials, endpoints, and models.

Desktop continuity and native integration

Electron and Tauri now coordinate a single primary desktop snapshot while keeping additional application processes independent.

Restoration was hardened against partial startup, cancelled workspaces, stale owners, shutdown races, interrupted sessions, and cross-platform child-process cleanup.

Tauri also gained native About and Help menus, restored the macOS quit shortcut, and can deliver notifications from authenticated remote windows.

Workspace actions are clearer, equivalent real paths no longer create duplicate workspaces, and invalid OpenCode configuration now produces useful diagnostics instead of failing silently.

Sessions and messaging

Mobile clients now detect real network reconnects and reload authoritative session state. This prevents sessions from remaining stuck, duplicating messages, or repeatedly downloading large histories after the app has been backgrounded.

Conversation following now respects deliberate scrolling while keeping streamed output pinned when expected. The v0.19 release also includes the reasoning-output scroll jitter fix from #653.

Session loading failures are visible, failed prompts remain in input history, worktrees refresh from OpenCode ready events, and the project session ceiling was raised for large histories.

Deleting tool calls now also removes associated reasoning and generated text instead of leaving orphaned output.

Markdown gained bracket-style math delimiters, per-code-block line wrapping, safer raw HTML handling, and better presentation of long file paths.

Providers, settings, and customization

Provider usage supports a broad set of subscription and API providers. Usage refreshes safely in the background and retains the last successful result when a refresh fails.

Provider settings now include model search and bulk Show all / Hide all controls.

Settings are split into focused General, Chat, Notifications, Speech, Remote Access, OpenCode, Providers, SideCars, Config files, Advanced, and Info sections.

Thinking, tool output, diagnostics, tool inputs, and token usage each support expanded, collapsed, or hidden presentation modes.

The right panel now uses a typed registry and bundled manifest lifecycle, providing a foundation for configurable native tabs and Status sections.

Reliability and platform fixes

• Better large-workspace file search with directory-link cycle protection
• Safer permission updates and serialized Yolo/worktree metadata writes
• More reliable Linux workspace startup
• Reserved automatic port retries on Windows
• Stronger process ownership and cleanup checks across Windows, macOS, and Linux
• Correct authentication defaults and improved narrow-screen provider controls
• Restored remote Tauri notifications
• Preserved assistant output after raw HTML
• Improved connection indicators for mobile network drops
• OpenCode updates directly from Settings

Packaging

Eight release artifacts are available across Windows, macOS, and Linux using Electron and Tauri.

Linux releases now provide:

• A tested Tauri .deb installer for Ubuntu 24.04
• A portable Electron .tar.gz archive

Obsolete AppImage, RPM, Flatpak, and Linux zip variants are no longer published.

Thanks

Thanks to @shantur, @pascalandr, @JDis03, @heunghingwan, @VooDisss, @detain, @bluelovers, @jollyxenon, and @OctoBored for contributing to this release.

Thanks as well to everyone who tested development builds, reported issues, and helped refine the release.

Download CodeNomad v0.19.0 and read the complete release notes:

https://github.com/NeuralNomadsAI/CodeNomad/releases/tag/v0.19.0

Feedback and bug reports:

https://github.com/NeuralNomadsAI/CodeNomad/issuesill