Anyone have a model they like that is super fast and good enough at coding? And maybe cheap?

pls help me

I want to know what models people use, like models that output good code, and doesn't hallucinate, because running a test like making a webpage, doesn't test how it does when it's really coding. Thank you!

Any ideas? I can’t do anything right now. The application is frozen and no session is open.

Just asking, they claim it has 3x the usage of Claude Pro, but I’m super hesitant. I once bought a $5 token plan from Mimo and only got $5 worth of usage (actually around $4.60 by my calculations). I really don't want to get scammed, so if any of you have already gotten burned, I’d appreciate it if you could let us know.

TL;DR: Tasked GLM-5.3-Flash, MiMo-V2.6-Flash, and Deepseek-V4.1-Flash with reviewing a skeleton codebase. Deepseek decided to ignore instructions to use included stand-ins instead of live API calls, then panicked when it realized it's error.

I am usually a lurker, but this is too funny. I had Argon create a series of structured prompts for MiMo-V2.6-Flash:Free to create a skeleton proof-of-concept for my project.

I then had MiMo-V2.6-Flash, GLM-5.3-Flash, and Deepseek-V4.1-Flash analyze the codebase for issues (I defined that more clearly than "look for issues" ofc). Argon and Sonnet 5.5 XHigh then also independently reviewed the codebase, then graded the reports against their own.

Summarized Results:

GLM
(~30m review time) - Identified 12/17 real defect issues, 3 unique valid findings, 0 false positives. Some issues with consequences of issues on operation, but pretty solid and methodical.
Rated 9/10 by Argon and Sonnet 5.5 XHigh.

MiMo
(~30m review time) - Identified 10/17 real defect issues, 4 unique valid findings, 1 false positive. Makes sense given it was the original coding model. Not bad, but thought itself into loops fairly frequently
Rated 8/10 by Argon and Sonnet 5.5 XHigh.

And now Deepseek...
(~15M review time) - Identified 9/17 real defect issues, 0 unique valid findings, 3 false positives. Literally blew past specific direction to not test live API calls. Twice. Reading the panic once the model realized it's error is the funniest thing I have seen in a long while. Also, this model isn't touching my codebase without extreme babysitting or a change in prompt philosophy.
Rated 6.5/10 by Argon and Sonnet 5.5 XHigh.

I am fairly new to agentic coding and coding in general, but I am fairly structured and methodical when it comes to personal projects, so I've been researching different options. Turns out my research was mostly confirmed: GLM-5.3-Flash was fairly complete and measured, MiMo-V2.6-Flash is not bad but can get stuck, and Deepseek is a talented teenager with severe ADHD, seven energy drinks, and identifies as chaotic neutral.

Disclaimers:
This is not rigorous, and borders on anecdotal - This was single pass with identical instructions. The codebase included design documents that the LLMs could reference for intended design, as well as the original prompts for creating the codebase and any additional instructions given to the original coding agent. I am not claiming that Deepseek-V4.1-Flash sucks; it is very fast, but may need very strict prompting to not go off the rails and be confidently wrong. Perhaps two passes of 4.1 is worth more than one pass of the other models, but I hesitate to trust that the same model would catch issues in the second pass without creating more issues.

There are some absolute Deepseek-V4.1-Flash stans on the interwebs. I would love to hear some insight on how you effectively prompt it (or if you just have it check itself frequently).

Also, any free models, stealth or otherwise, that I should try?

TL;DR (again): Tasked GLM-5.3-Flash, MiMo-V2.6-Flash, and Deepseek-V4.1-Flash with reviewing a skeleton codebase. Deepseek decided to ignore instructions to use included stand-ins instead of live API calls, then panicked when it realized it's error.

Edit: formatting

Hey everyone,

I've been using OpenCode for coding and I'm now looking to add browser and computer-use capabilities to my setup. I'd love to hear what's working for you.

Brother what do I even say just dont use them man literally benchmaxxed to the point of everest

I made a plugin that adds Claude Code's /goal command to OpenCode v2. You give the agent a finish line like "/goal npm test passes with 0 failures" and it keeps working turn after turn until the goal is actually met, no need to keep typing "continue".

After every turn an evaluator checks the conversation and decides if the goal is met, not met or impossible. If it's not met, the agent gets the reason and keeps going on its own.

It's token efficient: the evaluator reuses the session's cached context, so each check costs little more than a short answer.

It also has safety rails (pauses when the agent stops making progress, retries on network errors, turn limit) and a status indicator in the TUI.

Tell me what you think about it and feedback is appreciated.

Here's the repo and npm package:

https://github.com/EL4CTEO/opencode-goal

https://www.npmjs.com/package/@el4cteo/opencode-goal

Ultramarine improves on GLM-5.3 Flash on all six knowledge-work benchmarks, by 4.1 points on average, while generating 6.5% fewer tokens across them

Looks like they trained/benchmaxed it for OpenCode v1.18.4 so hopefully that will translate to v2.

After reading about the pattern online, I experimented with Claude Code subagents so that I could delegate to less powerful models than Opus, while having Opus drive them. It sounded good in theory and after ironing out some kinks, I had it working, even when the "implementer" subagent was from outside Anthropic. I used T3 Code to orchestrate it. The same machine had `claude` and `opencode` CLIs installed on them, so via subagents and skills, my Claude session could be instructed to call `opencode` via Bash when it needed to.

However, when I measured it today, using Opus 5.5 as the main session, comparing to when DeepSeek 4.1 Flash was used to implement vs having the main session do the implementation itself, I found that there was no reduction in Claude usage. I guess the overhead of having Opus 5.5 orchestrate DeepSeek negated the savings.

I also tried it with a Sonnet 5.5 implementer subagent.

Here is the final result in Claude usage (from Opus 5.5):

It would probably be too verbose to share the complete subagent markdown and CLAUDE.md that told it how to use them, but I'm happy to share it if anyone wants to see it.

summary, these are the two agents I had:

• DeepSeek implementer
  • name: ds-implementer
  • description: Implements a plan through DeepSeek-V4.1-Flash (free) on OpenCode, using the ds-implement script. The default implementer for every plan that is more than a few lines.
  • model: haiku
  • tools: Bash
• Sonnet implementer
  • name: sonnet-implementer
  • description: The Claude implementer, using Sonnet. Use it when ds-implementer failed, when the user has opted out of OpenCode for the session, or after OpenCode was unavailable earlier in the session.
  • model: sonnet

And the DeepSeek implementer agent uses Bash to invoke a script I saved at `~/.local/bin/ds-implement` which invokes `opencode`, doing the handoff and giving just the `opencode` result (and context if it failed) to the agent.

I had an existing app that I wanted to add a feature to, and this is the prompt I used for the test each time (minus the extra instructions I gave each to point them at the right subagent manually and to tell one not to use any subagents):

On a new feature branch from main, let's add the ability for planners to export their data. New tab on the left called export. Pick a good Lucide icon.

Sharing because I'm curious if people have had success with this kind of worker agent orchestration, or if they think it's simpler for me to just stick to having Claude use Claude subagents.

hey guys, does anyone have a spare claude guest pass? been using chatgpt lately but want to try claude code before subscribing. if anyone has one available, id really appreciate it. thank you

So i created chrome agent like comet which does chrome task, answers the question and take control of browser.
Tho there are lots of them that does same thing but i created this to solve this problems.

1. I wanted to use opencode go, nvidia and openrouter apis
   
2. In linux they dont have comet.

We can use free models, stores all data on browser other than apis and do lots of other things.
Would anyone want that if i make it open source?

Built optimAIzr over the past few months, and it now supports OpenCode!

Track token usage, spot waste, find ways to save and much more.

Free, open source, local-first.

git: https://github.com/blendbunjaku/optimaizr

I have opencode GO, I see these free models like step 5, longcat, cat jam free. I try one simple prompt and they say they are rate limited but I'm on the paid opencode go? I try to use zen with the free deepkseek flash 4.1 and all say they are rate limited it's like nothing that says it's free works for me? am I the only one?

When they had spacebunny free I used it 24/7 and it worked all the time no problem now these ones it's like nothing works?

https://research.meta.ai/blog/muse-spark-open

Also, is there a way to select a part of the agent message and add it as a quote in the text field area?

I tested five free OpenCode models on Oct 15: two questions and one toy Python fix, one attempt per task.

Follow-up to my Oct 8 test.

The four perfect-pass scores mainly separate latency. Oct 8 used different concurrency/settings, so these ranks cannot establish speed improvements or reliability.

Tests: daily-hazard probability, Fibonacci modulo 1,000,000,007, and a duration-parser/median fix. All five fixes passed the unchanged five-test file; Space Bunny's Fibonacci code did not parse.

Same score as Oct 8: floor(20 per correct question + 40 per accepted fix + 20 × fastest accepted fix time / model fix time).

OpenCode 1.18.35, serial runs in isolated synthetic repos. Fix time includes CLI startup and tool calls. I repaired a grader that rejected a valid exception constructor and rescored the saved answer; no model rerun.

Free availability is temporary and data terms differ by model; contributor routes may use prompts for training.

I have now tried all free models and all return "Error: Rate limit exceeded. Please try again later."

Don't mind my prompting, I have full on architecture .md by opus 5.5 and after 5 .md plans this is the state.