More and more of us are looking for a solid replacement to Anthropic. What are you using now?
The top 8 I'm seeing today talking with OpenClaw users:
. What's working for you and what did you try that didn't?
I built a context engine that indexes your codebase and serves it to your coding agent via MCP. The agent understands the architecture before making changes instead of exploring blindly.
On benchmarks it takes Sonnet 4.0 from 66% to 73.4% on SWE-bench. Biggest help on complex repos (Django +12%, sympy +17%).
Most AI coding agents struggle when they hit 10k+ line repositories because of context loss. I’ve been benchmarking Xanther.ai using a proprietary PRAT protocol designed to handle systemic validation rather than just code completion.
Key Results:
Curious to hear what you guys think about the transition from "chat-with-code" to fully autonomous agents
Results on SWE-bench Verified (500 real bugs)
MiniMax M2.5 + Xanther: 78.2% ($0.22/instance)
Sonnet 4.0 + Xanther: 73.4% (baseline was 66%)
Claude Opus without it: 76.8% ($0.75/instance)
Biggest gains on complex repos — sympy +17%, scikit-learn +13%, django +12%.
Looking for people to try it on real projects. Free tier, 60 second setup:
Works with Claude Code, Cursor, Kiro, Windsurf — anything that supports MCP.
Discord: https://discord.gg/Y768kBRS
Requested a refund because I tested your API and decided that it not suits for my use case. 3 Work Days passed after requesting a refund, but no answer at all from support.
I purchased a year for approximately $13/month. I get 4500 text requests per session. I've never been able to get near 25% of that number. Until today.
Data processing hundreds of math curriculum files with output:
* 346 files
* 45K lines of .md and json files
5-10 subagents running continuously.
I finally broke 25%.
I see MiniMax making things worse and damaging the existing system if you use extension tools from VS Code, but it becomes good when you use an IDE other than VS Code.
I also think the choice of tools used to access the MiniMax model matters a lot, because every tool has its own prompting system, which can make the responses significantly worse. This is just based on my personal experience, though, since every project is different.
Right now, I’m using TRAE IDE from TikTok’s company. TRAE is basically a fork of VS Code, but with more advanced features.
I’ve been trying to use MiniMax in more local coding-agent setups recently.
It works relatively smoothly in some agents, but when I tried to run it through Codex CLI, the hard part was not the model itself. The hard part was the API shape.
After digging into it, the issue is that today’s coding-agent ecosystem is split across several protocol families:
For normal chat, these can look similar enough. For coding agents, they diverge very quickly.
The difficult parts are things like:
So I ended up building a local protocol translation layer instead of treating this as just another endpoint configuration problem.
The translation path is roughly:
agent wire request
-> source protocol translator
-> protocol-neutral request/event model
-> optional provider adapter
-> target protocol translator
-> provider wire request
And the response path runs back the other way, including stream chunks.
The first built-in translators cover:
After getting that working, I split the protocol layer into a standalone Rust package:
https://github.com/jazzenchen/va-ai-api-proxy
It is intentionally not an HTTP gateway. It does not own credentials, retries, billing, accounts, or chat history. It only provides the shared types and translator traits for moving between OpenAI Chat, OpenAI Responses, and Anthropic Messages-style traffic.
Then I wired it into VibeAround:
https://github.com/jazzenchen/VibeAround
The current MiniMax + Codex workflow is:
Once the local session is running, VibeAround can also expose the same Codex session through a browser terminal or IM bridge such as Telegram. So the interaction can happen remotely, while Codex, MiniMax routing, shell access, git state, and dev servers stay on the local machine.
This is less about benchmarking MiniMax in a chat box, and more about whether MiniMax can sit inside real coding-agent traffic: tool calls, streams, reasoning metadata, and long-running repo context.
I asked both the models to "Review the code for improvements, use graphify" on a small codebase of my hobby project and asked Opus 4.7 thinking with max efforts to review and here's the output.
| Dimension | Minimax m2.7 | Deepseek v4 Pro | Opus 4.7 |
|---|---|---|---|
| Bugs caught | drag undo, structuredClone, history singleton |
none | both + verified mechanism |
| Architecture insight | store-slicing, subscription perf | community cohesion | union + concrete splits |
| Line counts | canvas.ts 554 (actual 561, close) | Popup.tsx 467 (actual 657), Toolbar.tsx 388 (actual 546) | verified all |
| Dead code | motion-path caught | missed | confirmed |
| Used graph data | no — manual review only | yes — cited cohesion + god nodes | yes |
| Hallucinations | minor (line numbers off by ~7) | major (line counts off by 30–40%) | none |
| Actionable fixes | yes, prioritized | partial (suggested split points but no specifics) | yes |
Minimax wins on substance. It found 3 real bugs Deepseek missed (drag undo, structuredClone, history singleton) and the dead motion-path tool. Its perf observations are concrete and correct.
Deepseek wins on graph utilization. It actually used cohesion scores and god-node analysis from graphify, which is the whole point of running it. But it invented line counts and missed every concrete bug.
Best play: Minimax's bug list + Deepseek's community-cohesion framing. Minimax did real code reading; Deepseek did graph reading. Mine combined both and verified line numbers.
I have been using minimax 2.7 with tokens plan on cursor, i have noticed that it YAPS for too long and talks too much. Is there a solutions for faster development?
I noticed I was hitting my 5 hour limit twice in a row on my MiniMax. I was running one instance of OpenClaw against it, trying to debug some code. It runs a lot of tool calls, but I've never run out of usage.
For a while, I thought my key got hacked, so I reset it and it's still being used very quickly. With one instance of OpenClaw, the usage is saying about 3 request every 10 seconds. No wonder my hourly usage got swallowed up so quick! Very unfortunate, since I just paid for a year of MiniMax.
Hey everyone,
I’ve been working on an open-source project called MiniMax Agent GUI.
It’s a modern web interface for using MiniMax models and tools in one place, instead of jumping between scripts, API calls, and CLI commands.
Current features:
The stack is FastAPI, React, Vite and Tailwind.
The goal is simple: make MiniMax easier to use as a personal AI agent from a clean web interface.
It’s still evolving, and I’m actively improving the UX, agent workflow, tool support and code workspace.
Repo:
https://github.com/eduardoabreu81/minimax-agent-gui
Feedback is welcome. Especially from people using MiniMax, building agent tools, or experimenting with multimodal AI workflows.
I mostly got about 60-75tps on a regular plan and would like to try the high speed if it actually high speed lol
How much tps do you guys get?
They say on their page it is 1.6 billion credit and mimo v2.5 pro takes 2 credit per token, mimo v2.5 takes 1 credit per token but here is how they get you, cached token is still billed the same credit per round trip, absolutely not suitable for coding cli then, because every single one of them by design would keep going back and forth with toolcalls, that's how they work, normally inference providers charge 1% for the pre existing cached context, but Xiaomi takes the full amount, I did 10 small tasks like not even that deep, small tasks and it is already at 12 or so million credit used, it used probably under a million context tasks were that mini, like saying hello, and mv this folder around, write some sql etc, like 10 total prompts same session, credit cost keeps snow balling, they don't mention nothing of this sort in the token plan docs or anything anywhere, for a big task it would be what 200 million token uncached, so 400million credit if you used mimo v2.5 pro, so with max 100$ plan you can use it for 4 tasks PER MONTH, honestly get anything over mimo token/coding plan, 40m token task(input+output) would be like 400million, cache hit rate is avg 90%
Is minimax m2.5 any good?
I've seen it available for free on OpenCode and now that OpenCode head a nice desktop interface I'm thinking to give it a go.
Can it reliably build websites, platforms, and systems end to end including Auth and security?
I see the 2.7 version is also available from minimax directly for a recently priced sub.
What does M2.5 compare to in terms of the more established models from openai/Claude?
Hey everyone,
I'm experiencing major issues with Minimax on OpenCode lately. The model starts generating but almost never finishes the full response. It regularly interrupts mid-generation, throws SSE timeouts, and the output just stops.
The same problems happen both on my direct Minimax token plan and when routing through OpenRouter (Minimax provider). It feels like something on their side is overloaded or throttled heavily at the moment.
Has anyone else run into this in the last few days? Is it just me or is Minimax currently very unstable/slow across the board?
Prompt and API reference - https://useapi.net/blog/260504
I recently came across a setup that gives access to a bunch of well-known tools across:
• Dev & deployment
• Product + UI/UX
• Analytics
• AI productivity
Some examples (to give context):
– Tools like Notion, Framer, Linear
– Dev platforms like Railway, Replit
– Analytics tools like PostHog
💰 If you actually price these individually, it adds up to a few thousand dollars per year.
I’m currently exploring it and shared the exact steps + context inside our Skool community (kept it simple and transparent).
👉 If you're curious, you can check it out here: Skool Post