Opening a live Koda session from the dashboard used to take two confirmations: matching a six-digit code in the browser, then running /remote approve in the terminal. Both are gone.
Reaching your session already required your OpenAdapter credentials — the same ones that authorize every model call Koda makes. A second and third confirmation guarded a door you had already unlocked, and the terminal step in particular defeated the point of remote: it asked you to walk back to the machine you had walked away from. Pick your session, and you are watching it.
Sessions stay end-to-end encrypted between your terminal and your browser; the relay still forwards frames it cannot read. Permission gates are untouched — a dangerous command still stops and waits for you to approve it.
Two fixes ship alongside. Refreshing the page no longer loses the conversation or the model and thinking controls: a long transcript exceeded the relay's frame limit, which quietly killed the connection every time you reattached. And a permission prompt raised while your browser was closed now waits for you instead of expiring unseen — the relay tells Koda when someone is actually watching.
Qwen3.8-Flash is live. It reads text, images and video, carries a 1M-token context window, and is the least expensive of the three vision models we added this week.
Alibaba positions it for coding assistance, agentic workflows, document and codebase analysis, chart reading and long-video analysis. Call it as Qwen3.8-Flash.
It costs 1.5 requests of quota per call and is available on every paid plan and the free tier — the same terms as GLM-5.3-Flash and DeepSeek-V4-Flash-Vision.
Alibaba has not published benchmark scores for this model yet. We will add them to the model page once they exist rather than borrowing figures from its siblings.
GLM-5.3-Flash and DeepSeek-V4-Flash-Vision are live. Both take images as well as text, and both carry a 1M-token context window.
GLM-5.3-Flash is built for coding and long-running agent work — it scores 84.3 on Terminal-Bench 2.1, within a point of the frontier models, and also accepts video input. DeepSeek-V4-Flash-Vision adds image understanding to the V4 Flash line, which posts 79% on SWE-bench Verified and 88.4% on LiveCodeBench, and is aimed at documents, charts and multimodal agent workflows.
Both are available on every paid plan and on the free tier, and cost 1.5 requests of quota per call. Call them as GLM-5.3-Flash and DeepSeek-V4-Flash-Vision.
Benchmark figures are as published by the model authors; DeepSeek's vision variant is marked experimental upstream.
Speech-to-text now works as audio arrives instead of only after a recording ends. Each sentence comes back the moment the speaker finishes it, so a voice agent can start responding before the person has stopped talking.
Open a WebSocket to wss://api.openadapter.in/v1/realtime/transcribe, send microphone audio, and read transcripts as they land. Your normal API key works — nothing to enable.
Available on parakeet, whisper-1, whisper-large-v3 and whisper-large-v3-turbo.
You're charged per transcribed sentence, not per connection or per second of audio — an open socket with nobody talking costs nothing.
Qwen3.8-27B is live on Pro plans at 0.25× quota — four requests for the price of one.
It handles a 250K-token context, supports tool calling, and thinks step by step before answering (reasoning is returned separately, so you can show or hide it). Released by Alibaba on 14 August 2026.
Call it as Qwen3.8-27B from any client.
One note if you use it programmatically: thinking is on by default and consumes part of your max_tokens budget. Give it room — a few thousand tokens — or turn thinking off if you only want the answer.
Both come in at 1x quota, the same cost as a standard model, which makes them the cheapest MAX-tier models on the platform. If you want frontier-class output for everyday agent loops without burning through your plan, start with these two.
Hy3 is Tencent's Hunyuan 3. Luna is the newest GPT-5.6 variant.
Alibaba's newest flagship and the largest Qwen we carry. It is the most expensive model on the platform at 4x quota, so reach for it when the task actually warrants it.
GLM-4.7 is being retired. You do not need to change anything — requests for GLM-4.7 now serve GLM-5 automatically, so existing configs, scripts and agents keep working. GLM-5 is the stronger model on every axis, and it costs the same 1x quota.
If you would rather be explicit, switch your config to GLM-5 at your convenience.
Two separate faults, which together meant search was broken for essentially everyone:
The search tool never reached our servers. The MCP we installed talked to a search
instance on localhost:8888 — a local container almost nobody runs. So it had nothing
to query.
Our own search fleet was being blocked. The engines behind it refuse traffic from
datacenter addresses, so even a correctly-pointed request came back empty roughly eight
times in ten.
Search now runs through the gateway. Your machine asks us, we handle the fleet and
which engines to use. That means the next time an engine starts blocking, it is a fix on
our side — not a reinstall on yours.
Koda is out of beta. Until today npm i -g @openadapter/koda gave you
1.0.0-beta.13 — latest had been frozen there for months while the real work happened
on @beta. This release moves latest onto current code:
Koda speaks the Debug Adapter Protocol. It can set a breakpoint, launch or attach,
then read the real stack and actual variable values at the moment things broke — step
through, and evaluate expressions in the paused frame.
It discovers 14 adapters on your PATH, with an xcrun fallback for the lldb-dap
Xcode hides. It registers the tool only when an adapter genuinely exists on your machine,
and never loads the engine until you actually debug.
This is the difference between reading a stack trace and watching the value change.
A task tool with context-isolated subagents — general, explore, or your own in
.koda/agents/*.md. They run in parallel and only their results come back, so the main
context stays clean. A live line under the input shows how many are working and for how long.
hashline_edit (vendored from oh-my-pi, MIT). Reads carry a content tag and line
numbers; edits are line-anchored operations that never echo old text back. A stale tag
fails closed with a re-read instruction rather than quietly corrupting the file.
Choose it in /settings → Edit tool, which enforces one or the other — running both just
hands the model a coin to flip.
After an edit, the errors your change introduced — including the call sites you broke
somewhere else — come back in the tool result, so the model fixes them in the same turn.
TypeScript needs no install; Python, Go, Rust, Java and C/C++ work through their standard
language servers.
The big one if you have ever watched an agent stop halfway.
There is now a completion gate on every model tier, not just the ones running the
overseer. The old check only fired when the model had made a todo list — which happened in
13.6% of multi-step tasks, so on a cheap model nothing was checking at all. The new gate
costs zero extra model calls.
Todos can also carry a verify command, and marking one done is only a claim until Koda
runs that command itself and sees it exit 0.
KODA_FULLSCREEN=1 gives a live sidebar: session, context, model, LSP status, and the todo
plan marked [ ] / [x] / [✓] — that third mark meaning Koda verified it, not that
the model said so.
Plus 15 themes on a four-role colour system. The old palette used accent 102 times
against text's 16, so everything read as important and therefore nothing did.
DeepSeek V4 Flash 0731 is a re-post-trained revision of V4 Flash, and it is now a
primary backend behind DeepSeek-V4-Flash on OpenAdapter. Same model name, same quota
cost. What changed is what it can do as an agent.
A sparse mixture-of-experts model: 284B total parameters, 13B active per token, tuned
for coding, reasoning and agent workflows. Only a fraction of the network fires per token,
so it answers at small-model speed while reasoning like a much larger one.
Measured by Artificial Analysis, who run models themselves rather than republishing vendor
claims:
Benchmark
Score
Reasoning
GPQA Diamond — graduate-level science
90.8%
HLE — Humanity's Last Exam
36.8%
AA-LCR — long-context reasoning
65.7%
GDPval-AA — economically valuable tasks
52.9%
CritPt — research-level physics
16.6%
Coding
SciCode — scientific Python
49.9%
Knowledge
AA-Omniscience — accuracy
37.2%
AA-Omniscience — non-hallucination rate
15.6%
90.8% on GPQA Diamond is the number to look at — that is graduate-level science
reasoning, from a model with 13B active parameters. The low CritPt and non-hallucination
figures are equally worth knowing: it is strong at structured reasoning, weaker at
research-frontier physics, and like most models it will still assert things confidently
when it does not know. Do not use it as an oracle.
The weights are the same size. The post-training is not. DeepSeek's own agent-benchmark
figures for this revision:
Benchmark
Previous build
0731
DeepSWE
7.3
54.4
Terminal Bench 2.1
61.8
82.7
Cybergym
38.7
76.7
SWE-bench Verified
79.0
79.0
Note the shape: single-shot coding barely moved (SWE-bench flat at 79.0) while
multi-step agent ability transformed. This model got better at operating, not at
writing a function. Driven in a loop with tools you will feel it; on one-off questions you
may not. DeepSeek claim 0731 now beats V4 Pro Preview on every agent benchmark they
published — Terminal Bench 82.7 against Pro's 72.1.
Treat that second table with care: those are DeepSeek testing DeepSeek, on a harness
that is not public yet, and two of the nine are internal sets the company built. The
Artificial Analysis figures above and our own measurements below are the parts with
independent backing.
OA-Robin-Mini-Preview is the first model we built and host ourselves, and it is live now.
Because it runs entirely on our own hardware, your prompts never leave our infrastructure and nothing is retained — the same privacy guarantee as our other Edge models.
Private by default — great for confidential chat, brainstorming and creative work you would rather not send to a third party.
256K context with vision, tool calling and JSON mode, so it slots straight into agents and long-running workflows.
0.5x multiplier — every request costs half a normal one, so you get roughly double the usage from the same plan.
It is a preview, so expect it to keep improving. A much larger model in the same family is coming soon — we will announce it here when it lands.
Try it from the Models page, or just point your client at OA-Robin-Mini-Preview.
Share your referral link from the Referrals page. When a friend signs up and makes their first purchase, they get 20% off — applied automatically at checkout, no code to enter. You earn 20% of what they paid as credit, used automatically toward your own next plan. Track your link and referrals right from the dashboard.
Browser-based, OpenAI-compatible apps — like Janitor AI, SillyTavern, and other web chat front-ends — can now call the API directly.
Set the endpoint to https://api.openadapter.in/v1/chat/completions and use your sk-cv-… key.
Previously these apps hit a browser "network error" because the API only accepted requests from our own domains. The /v1/* endpoints now accept any origin.
Note: only paste your key into apps you trust — a key used in the browser is visible to that app.
The GO plan now includes the latest 0G Network models — DeepSeek-V4 (Pro & Flash), Qwen3.7-max, 0GM-1.0, and MiniMax-M3 — alongside the existing 0G lineup.
These models also group correctly under 0G Network on the Models page instead of slipping into "Other."
Attach to your live Koda CLI session from the dashboard — watch it work in real time, send it prompts, and answer its permission gates, all end-to-end encrypted. Our relay only forwards ciphertext and cannot read your session.
In the CLI, run /remote on (or koda --remote) — it shows a QR code and a short pairing code.
Open the Koda page → Remote tab → pick your session (or Add by code), then confirm the 6-digit code matches what your terminal shows. That match is the security check that defeats a man-in-the-middle.
/remote off to detach. Pairing codes expire after 5 minutes.
Read + Write, no exec(default) — read and edit files, but run no commands.
Full autonomous — full control; dangerous commands like sudo and rm -rf still need an explicit Approve in the dashboard.
The policy is enforced locally by your CLI, and secret paths (~/.ssh, ~/.aws, /etc, **/.env) are hard-blocked under every preset. Keep the preset as low as the task needs, and detach when you're done.
The Koda page now feels like the rest of the dashboard instead of a separate site.
Follows your theme — light or dark, with the standard page header, cards, and the official Koda wordmark.
Spreads across the page — the early-access, setup, and connected views fill the width like every other section instead of sitting in a narrow column.
Easier to scan — a tidier feature preview while you're on the waitlist, a side-by-side setup wizard once you're in, and clearer session and install stats when Koda's connected.
Click Get early access. Invites roll out in waves, and Max plans skip the line, switched on within hours. Once you're in, the page walks you through install and setup, then becomes your personal Koda dashboard.
Overseer reviews its own work and runs it to be sure, pulling in a stronger model when it's stuck. Cheap models that actually finish.
Jobs schedule autonomous tasks that run while you sleep.
Checkpoints & rewind, memory + vector recall, built-in tools + MCP, ask-before-dangerous permissions, and zenitsu speed that never idles on a rate limit.
Your Koda dashboard shows sessions, version, where it's installed, and a direct line to tell us what to improve. Anonymous to us, never your code.
Share your personal referral link from the new Referrals page. When a friend signs up with it and makes their first paid purchase, you both get 20% off:
Your friend gets the discount on their first plan, applied automatically at checkout.
You get the discount on your next plan purchase — it's waiting for you the next time you upgrade or re-subscribe.
Track who you've referred and where each one stands right from the dashboard. The discount applies before checkout, so the price you see is the price you pay — no codes to enter.
Heads up: Teams is a request-specific feature. If you want it enabled on your account, click the support chat at the bottom-right — we'll switch it on for you.
Create a team and invite peers via a shareable join link (optional expiry, max-uses, rotate, revoke) or by email.
Owner sees aggregate + per-member usage — requests, tokens, est. provider cost, model breakdown, last-active, and which client (Claude Code, Cursor, Cline, etc.) each member uses.
Per-member drill-down with daily activity chart and top models.
Members can leave at any time; only the owner sees usage.
Owners do not see your prompts, request bodies, or API keys — ever. The privacy line is reinforced at every consent point. Each member still uses their own plan and quota; the team doesn't share capacity.
Real signal, not vanity numbers. An activity heatmap in the GitHub style, quota windows with countdown timers, top-model breakdown, upgrade hints sized to your actual usage. The whole app feels calmer.
Generate a shareable SVG of your gateway activity at https://api.openadapter.in/embed/<token>.svg — drop it on a README, a blog post, or your portfolio. Three formats:
?format=svg — the badge image (default)
?format=iframe — a 320×120 iframe with hover detail
Full Vector DB — manage collections from the dashboard, upload documents, search semantically, and chat with your data through the built-in RAG playground. The Code SDK panel emits ready-to-paste snippets for Python and JavaScript.
Your Vector DB plan stays in sync with your OpenAdapter plan, so quota matches everywhere.
Allow 2× burst above your RPM limit before throttling kicks in. Useful when an agent fires parallel tool calls — the natural bunching no longer trips the rate limiter.
Usage reset timer now reflects the real window even when it's already expired. Dashboard logs only show the requests you actually made — no internal noise.
OpenAdapter gives you one API key that works in every AI tool you already use — Claude Code, Cursor, Cline, Continue, Aider, and more. One subscription, one quota, no juggling provider keys.
OpenAI SDK and Anthropic SDK formats both work — same key, both shapes.
Live usage dashboard with countdowns to your quota reset.
A starter Developer Guide to get you running in a couple of minutes.