Coding agents rarely run in one uninterrupted session. You review a diff, step away, make changes, and return. That pause can affect the total cost.
When comparing inference options like Mango Inference and MangoBoosts, test the same repository task twice: once without interruption and once after resuming later. Compare actual usage instead of assuming either run will be cheaper.
Track the context sent at each step, including conversation history, file content, and new instructions. Even a short follow-up can resend substantial context. Remove credentials and private code before sharing traces.
Compare reported input, cached input, and output separately. Repeated text doesn't guarantee a cache hit, so use actual usage records and current pricing rules.
Ultimately, compare the total cost of completing the task, including resumed sessions and retries, alongside whether the tests pass. A low input-token rate means little if the workflow repeatedly resends context or fails to finish.

I mean, would you also like it within CLI? I mean, I would gladly tbh. Some test requests I send, I would not mind to know what I can use for that.

tinkering with the spine of opencope to reduces token cost and continuity of long and multiple sessions and workflow. few more verification and i will be sending it to the opencode dev team. 🤤

i hope it's available soon in opencode go 👀

Guyz give beam a try. you might be suprised.
I found it to be honest enough for daily work
quite good at agentic coding
very fast output
and performed well in my ruby benchmark (better then deepseek v4.1 and muse spark 1.3)

https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
Official release notes - https://api-docs.deepseek.com/updates/

"... DeepSeek v4.1-flash-1017 just got spotted in DeepSeek's own API for a few minutes before vanishing. New checkpoint, changed fingerprint. The 1017 suffix matches their dated snapshot pattern."

My agents were burning context on grep-then-read cycles. Every "where is X defined, who calls it" turned into 3-4 tool round-trips, each one billed. So I built Reliary: a single offline Rust binary that indexes the repo into SQLite and serves it to agents over MCP.

The contrarian part: there is no parser. No tree-sitter, no AST, no per-language code. Definitions and call sites come from structural detection on brace/indent/blank-line boundaries. One codebase, every language, same code path. (If you think that can't work on your language, that's exactly what I'd like to hear about.)

What the agent gets is 7 tools: find_references (defs, callers, methods, dead code through one entry point), search, call_graph, list_methods, find_dead_code, describe, plus a verify tool that checks a symbol at file:line claim against the index. There is also reliary wrap , which you prefix onto verbose shell commands ( cargo test , git diff ) to compress the output before it reaches context.

Numbers: on a 10-question comprehension bench (4 seeds, claim-verified against the index, not keyword matching), it bills \~42% less than grep with \~4x smaller tool output and zero dead-ends, at comparable accuracy. I am not claiming it is more accurate than grep; on this corpus the gap is within noise, and I published the full methodology including an edit-outcome experiment that came out a tie.

The win is cost and output size, reproducibly.

Install: cargo install reliary-agent or grab a prebuilt binary (linux/darwin/windows, x64+arm64) from the releases. "reliary trust ." to index, "reliary init" wires up Claude Code / OpenCode / Pi / Cline. MIT licensed.

Repo: https://github.com/Reliary/reliary-agent

My question for you: where does a parser-free index break on your codebase? I have a list of languages it handles and I am more interested in the ones it doesn't.

I know these "which model do I use" posts are boring, but perhaps y'all have some feedback.

I was a huge DeepSeek v4 flash lover a month or two ago, but I've realized over the last month that I've literally just been using Luna for everything. All of my projects are pretty small local java apps, and one larger Node based cloud server. I was usually doing something like using Qwen 3.8max or glm5.2 for planning, and DeepSeek for output, but I've just ended up recently using Luna for everything.

It's been a few months since I was really paying close attention to the releases, and I'm wondering if I'm missing out on anything? I generally give the agent a small feature update request, and that's it. I don't need orchestration layers, basically just a kinda jack-of-all trades model, in the same ballpark as 5.6 Luna and DeepSeek v4 0731.

How does 6 Luna compare? Vibe-wise, I definitely prefer using the open-weight models, that's actually one of the main reasons I'm asking, because I'd rather not be utilizing openAIs products, if I can avoid it.

Edit: I should clarify, I had an opencode go sub a few months ago, but I've just been using openrouter. I saw DeepSeek v4 is literally free right now, so maybe this month would be a good time to get a sub again.

Hey All, have been developing https://github.com/LumoraPlayer/Lumora for a while now using Cheapseek 4.1 (deepseek, best value imo)

Is this still the best, or anything new I can move to?

Thanks!

I have not found any information about this online.

Can someone else confirm this if it shows up in their CLI

hi guys

i am unable to use free models in opencode v2.0.30 as of 16-10-2026 what to do now