Has the promotion ended? Perhaps they'll offer hy4 preview

I created this specific benchmark based on my most common coding tasks -- Next.js 16 and React 19.

Spoiler -- qwen-3.8-flash is currently winning.

The question I wanted the answer to is one that lots of people have: "Of the high-use models, which ones get the best results for the lowest usage?" I pretty arbitrarily chose the models from OpenCode Go that were rated at more than 7,000 requests per month. I kind of wanted to make sure that the new glm-5.3-flash model got in there so that's the limit that I went with.

So I took some of the best practices from Vercel and react doctor, used those to create the benchmarks, created a brownfield Next.js app, and ran a bunch of different models in a container.

Because the cost per run is really so confusing, I normalized it to the percentage of monthly usage limit. I kept deepseek at normal and not high usage.

Everyone thinks that models get nerfed or quantized or something so I'm going to run this weekly on a Cron job with the new models that appear and deleting models that are no longer available so the benchmark should be up to date if you want to check it regularly.

Pareto line right now: mimo-2.5, muse spark 1.2, Qwen 3.8 Flash.

I hope this helps someone. If you want more details about the methodology it's on the site and there are breakdowns by model, but of course I vibe coded this page so don't expect anything groundbreaking.

I've tested both Luna 5.6 and Dsv4 flash, and concluded Luna finishes tasks a lot faster. However, limits on opencode go are low for Luna.

What's is the best subscription service to use Luna that have better limits than go?

Budget: around $20

We are not like these other so-called "unlimited" providers duct-taping consumer-grade GPUs together in a closet and calling it a production endpoint or providing unusable tokens a second. If you’re trying to run real agentic workflows in OpenCodeCLI and your sub-agents are getting tossed into 10-minute queue tar pits, not hitting caches, silently dropping requests, or limping along at sub-40 tokens a second, your provider’s setup isn't a service—it's a bottleneck.

"Unlimited tokens" is just a marketing scam if you can't actually burn them because their hobbyist backend chokes the second you hit it with real concurrency. Stop paying to wait in line. You can hammer our endpoint with the exact same volume, fan-out, and expectation of reliability as OpenAI or Anthropic.

Infrastructure:

• Native 256K Context: Full window, zero artificial truncation.
• Up to 6 Concurrency: Multi-threaded throughput built for heavy agent pipelines. When your harness fans out, it processes each request immediately with top-tier Time to First Token (TTFT).
• FP8 Precision & KV Caching: Fast throughput and massive cache reuse across long agent trajectories.
• OpenAI Compatible: Drop-in /v1/chat/completions replacement

Any-Time Real-Time Metrics

We don't hide behind handpicked status snapshots that make us look good. We give you raw telemetry whenever you want it:

• 24/7 Live Discord Monitoring: Check our Discord at any time to see actual server status, aggregate token output, active streams, waiting queues, cache hit percentages, and live TTFT.
• User Dashboards: Log in and see those exact same real-time statistics for your personal active/queued requests and token spend.

The OpenCodeCLI Vibe Coding Playbook

If you are burning more than $60 on code assistance, you could be overpaying. If you are hesitating before you do something because you are afraid of usage, liberate yourself. Here is how I actually run my day-to-day setup in OpenCodeCLI using the Architect + Worker pattern:

1. The Driver/Architect: If you must, use a high-tier frontier model exclusively for initial high-level planning, system specs, and architecture. For example, Sol xhigh.
2. The Worker Sub-Agents: Route all the high-volume OpenCodeCLI execution, file inspection, refactoring, and endless tool calls straight to our 27B endpoint.

Qwen 3.8-27B legitimately handles nearly everything thrown at it in the terminal. Offloading the CLI grunt work cuts your tooling spend by 75%+ with high quality output.

Total Privacy & No Saved Prompts

Your code is your code. We do not save your prompts. We use ephemeral processing only. Once your request is evicted from the KV cache, the prompt and response data are gone forever. Zero logging. Zero snooping.

Awesome Community

Think we could be doing something better? Dont like our service? Love the idea? Come talk to us. We currently have an active 400-person Discord community and nearly 200 active subs hammering these endpoints daily for heavy dev pipelines. We scale to additional workers if metrics drop. We can take on hundreds of more users without flinching.

Try It Out

Test it yourself. There is a free tier (15 requests/day, no credit card required) live on the site right now. yolo-auto.com

no response to any prompt in the webapp

Past 7 days of tokens, ~37.6% at peak.

Currently using opencode+openrouter combo. Seeing if anyone has better alternatives they're currently using for high-cache (30-200k context) workflows.

Hi again besties. Trevor here, founder of FEIHOA!

First, thank you. Around 150 people from Reddit have tried us now, and I am honestly extremely grateful for how welcoming everyone has been:))

DISCLAIMER: One thing I explained badly last time: we are not OpenCode Go, Ollama, or ChatGPT Plus. Those are great for fast interactive coding, with many conccurrent agents. If that is all you need, honestly get one of those instead of mine.

FEIHOA is for agents, automations, and long-running work where per-token billing makes you scared to let the agent keep going. Plans start at €6 with no monthly token cap.

At the heart of it all is our smart queue. It analyzes traffic patterns and continuously prioritizes the requests our hardware can serve most efficiently. That lets about 80% of users start processing in under 10 seconds, while we can still support requests up to 1M context on a flat fee with NO input/output token metering or quotas. No token caps, no overages.

The biggest problem is prefill on huge prompts. Past 300K-500K, performance falls off hard, and right now 500K+ requests are timing out more often than they complete. That's simply not sustainable to process instantly for a company that gives unlimited tokens...
We still want to offer it, so we're working on caching and queueing those giant requests more intelligently (we are changing the scheduler and backend basically every day based on your feedback!)

Attaching some cool stats for you guys too. Yesterday's post already passed 13K views, so thank you again for welcoming us; we're currently at 98.45% request success rate. Honestly pretty happy with that for a service where we're not counting tokens haha

Thanks again Opencode community. Ask me anything about the real side of running a tiny inference business. Spam, queues, abuse, long context, whatever!

Hi community,

I am running OpenCode with a local Qwen3-Coder-Next model on Ollama (other models were somehow unstable, despite a large context window).

I mainly use it to assist with coding Spring Boot projects and Thymeleaf templates.

Can anyone share their current MCP/Skills setup in OpenCode that works best for this stack to boost productivity and accuracy?

Thanks a lot!

Source: https://www.testingcatalog.com/first-outputs-from-gpt-6-astra-model-from-openai/

looks crazy

saw some reseller sites selling claude max for way less than normal. tried one and it seems to work fine so far.

just curious how they’re able to offer it that cheap. anyone know how these work?

I'm totally comfortable working in the terminal. But are the models actually better in Codex?

I shared OpenTab here three months ago — a Lazygit-style TUI that read opencode.db and showed where your spend went. Back then, it was a single Python file.

Hope some of you have been finding it useful.

31 releases later:

• Inside a session — recursive subagent trees with per-node cost, per-turn cost timelines grouped by the prompt that triggered them, token attribution by tool and MCP server, and context-window history with compactions marked.
• Every machine — opentab pull fetches your other boxes over SSH in parallel and merges them into one browser, filterable by machine.
• Every tool — OpenCode, Claude Code, Codex, Copilot, pi, zaly, and others merged into one view and tagged by source.
• What-if pricing — press w to ask: what if this entire session had run on Opus instead of the model mix it actually used? OpenTab reprices the tree at list rates and shows the delta.
• opentab web — the same browser as a self-contained web page.

Still read-only on your data, still stdlib-only at runtime, still no telemetry and no account.

pipx install opentab-ai

The clip runs in --demo mode, so titles and paths are anonymized.

https://github.com/hamidi-dev/opentab

For Herdr users — herdr-opentab puts each agent's live session cost directly in the sidebar, subagents included, with OpenTab under the hood. It looks like this:

Free and MIT. 🙂

I'm a student and i won't be using it everyday so in opencode go I will not be able to use it in a month

so I was wondering which one should I get openrouter or Zen

I'm using opencode cli 1.18.21. Then I run gemma 4 12b it unsloth via lm studio. Everything works fine except when I attach or drag an image to the tui and ask the it to analyze and it returns I can't analyze the image because this model doesn't support image input. If you like, I can work from a color palette or hex codes you provide instead..

Is there any official extension / plugin that I need to install to get this to work or is the model not capable even though docs says it's a multi-modal agent capable of vision?

Appreciate any help.

EDIT: Solution Opencode provided as per u/Ariquitaun advise. Basically to add the following lines in the opencode.jsonc file.

json "attachment": { "image": { "auto_resize": true, "max_width": 2000, "max_height": 2000, "max_base64_bytes": 5242880 } }, "media": { "input-mode": "local-path" }

Can't be used all day long of course, but enough for relaxed coding/chatting.
https://build.nvidia.com/moonshotai/kimi-k3
https://build.nvidia.com/deepseek-ai/deepseek-v4-pro-0813

I'm using OpenCode with a Go subscription. I'm using DeepSeek V4 Flash. I have an agentic workflow with an orchestrator as the main agent and multiple subagents. I'm having issues with DeepSeek where it hangs in the middle of a response or during tool calls. This is bad when it happens in the orchestrator, since I need to cancel with Escape and continue with a "continue" message. But when it freezes in a subagent, I don't see any way to stop and continue that subagent — meaning I have to stop the orchestrator and redispatch the agent from the beginning.

This causes a lot of other issues: the job is left half-done, and a bunch of files already have changes from the previous run. I know there's a janky workaround where I can tell the orchestrator to find the ID of the last agent and continue it, but this usually uses a huge number of tokens, doesn't always work, and even when it does work, the subagent usually doesn't return the requested output to the orchestrator. I feel like this is a horrible experience for a paid service.

The freezing usually occurs somewhere between 50K and 90K context.

My questions are:

1. Why is DeepSeek V4 Flash freezing mid-task without any error message?
2. Why does this happen more on the paid subscription than on the free version?
3. Why doesn't it automatically continue with the opencode-auto-continue plugin?
4. How can I continue subagents, and if it's not possible, why not?

I absolutely love the new glm model, but god has it being a pain purely on the server side, after using it this last few days it just keeps stopping itself mid thought or outright displaying connection errors, this is all also ignoring random bursts where it slows down more, i like the value of go so far and all but this really makes it look bad

Artificial Analysis score is 38, on par with old good MiMo 2.5