The difference after moving from the Plus Token plan to the Starter plan is ridiculous. I almost never had failed requests on Plus, but now a huge number of responses either fail midway, timeout, or completely break for no reason.

It honestly feels like they’re intentionally crippling the lower-tier plan just to push people into upgrading again. What’s the point of paying for a “Starter” plan if basic reliability becomes this bad? It’s borderline unusable for anything beyond light testing.

What makes it even worse is that they still use your data for training, yet the actual service quality on paid lower tiers is this unreliable. At that point, I’d honestly rather move to another service that’s more stable and transparent.

Has anyone else experienced the same thing after switching plans?

Hi all, I have an account from before the 22nd of March, so I'm not subject to the weekly limits. However, I'd like to upgrade my account.

Will I keep my legacy status if I upgrade? Has anyone tried this?

First of all, I do not know if this is actually a minimax issue. Posting here to see if others have the same issue.

I've used minimax' token plan quite intensively for a week now, and have been very impressed with the value.

My usage is for coding, using pi coding agent.

Since two days ago, I notice that the model often cuts out mid-response, even mid-sentence, and ends the turn as if the response was complete.

I am nowhere near my token limits. It doesn't happen with other providers. Am I alone in experiencing this?

I’m using MiniMax-M2.7-highspeed through an Anthropic-compatible endpoint inside an agent framework called Hermes.

MiniMax appears to advertise a large context window, around 204,800 tokens, but I’m seeing API failures well below that.

The error looks like this:

⚠️  API call failed (attempt 1/3): BadRequestError [HTTP 400]
   🔌 Provider: minimax  Model: MiniMax-M2.7-highspeed
   🌐 Endpoint: https://api.minimax.io/anthropic
   📝 Error: HTTP 400: invalid params, context window exceeds limit (2013)
   📋 Details: {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'invalid params, context window exceeds limit (2013)'}, 'request_id': '065b1bb0eab63a4b21e50cb78514'}
   ⏱️  Elapsed: 2.61s  Context: 106 msgs, ~134,146 tokens
Provider reported overflow amount only; keeping context_length at 204,800 tokens and compressing.
⚠️  Context length exceeded at minimum tier — attempting compression...
🗜️ Context too large (~134,146 tokens) — compressing (1/3)...
🗜️ Compacting context — summarizing earlier conversation so I can continue...

My questions:

1. What does the number in this error mean?

I use well-organized ADRs and well-defined test plans. I use the Obsidian methodology, but even so, Minimax delivers an incomplete solution with mocks Instead of real functionalities.

Is this normal, or am I doing something wrong?

(generated by my agent)

The Problem

MiniMax's vision model (VLM) uses a non-standard endpoint: POST /v1/coding_plan/vlm with a custom request body format. A _MiniMaxVLMAdapter class existed to handle this translation, but the async call path had two bugs that prevented it from working.

Bug 1: The Wrong Endpoint Was Called

What happened: When Hermes received an image via Telegram, it tried to use MiniMax's VLM, but the request went to POST /v1/chat/completions instead of POST /v1/coding_plan/vlm.

Root cause: _is_anthropic_compat_endpoint() was returning True for MiniMax, causing async_call_llm to convert image_url content blocks to Anthropic's format ({"type": "image", "source": {...}}) before passing them to the VLM adapter. The adapter's create() method expected the original OpenAI image_url format but received Anthropic blocks it didn't recognize, so it treated the image as missing and returned "I don't see any image."

Fix: Removed MiniMax from _is_anthropic_compat_endpoint. The function now only returns True for actual Anthropic providers ("anthropic", "anthropic-direct"). MiniMax's VLM adapter handles its own internal format conversion — it shouldn't be pre-processed by async_call_llm.

# Before (wrong)
_ANTHROPIC_COMPAT_PROVIDERS = frozenset({"minimax", "minimax-oauth", "minimax-cn"})

# After (correct)
if provider in {"anthropic", "anthropic-direct"}:
    return True

Bug 2: The Async Wrapper Was Discarded

What happened: _to_async_client() had a isinstance(sync_client, _MiniMaxVLMAdapter) branch that built a wrapper object, but it was missing a return statement, causing execution to fall through to the generic AsyncOpenAI wrapping path — which routed to /v1/chat/completions.

Fix: Added the missing return so the VLM adapter's wrapper is actually returned and used, rather than being overwritten by the generic async wrapper.

# The isinstance check existed but had no return
# After fix:
if isinstance(sync_client, _MiniMaxVLMAdapter):
    async_create = client.async_create
    # ... build wrapper ...
    return mock, model  # ← this was missing

Why Testing Direct Calls Worked But End-to-End Failed

When testing _build_minimax_vlm_adapter() and calling create() directly, it worked perfectly. That's because the direct call bypasses async_call_llm entirely — it hits the adapter's create() method which correctly routes to /v1/coding_plan/vlm. The bugs only manifested in the full Telegram → Hermes → async_call_llm → vision tool chain.

Files Changed

- agent/auxiliary_client.py — two patches: fixed the missing return in _to_async_client and corrected _is_anthropic_compat_endpoint to exclude MiniMax.

Today when i have tried to login to my console using google, it rigoursly refreshed the console and kicked me back to login page. I was really flustered. anyone experiencing this kind of behaviour?

Hey, je setup un système de 3 bots Hermes et je voulais avoir vos retours avant de changer de provider.

J'ai actuellement MiniMax Starter à $10/mois pour mes 3 bots (Obsidian notes, CTF/cyber, et un bot manager qui envoie des rapports Telegram) et vais voir pour en faire d’autre pour remplacer Claude .

J'ai découvert OpenCode Go à $10/mois et franchement ça m'a l'air dingue — 15 modèles incluant MiMo V2.5-Pro, Kimi K2.6, DeepSeek V4 Flash, GLM-5.1 pour le même prix avec $60 de budget mensuel. Mais la doc est vieille de 2 mois et les modèles listés officiellement c'est juste GLM-5, Kimi K2.5, MiniMax M2.5.

Quelqu'un utilise OpenCode Go avec Hermes en ce moment ? La qualité des modèles est comparable aux APIs officielles ou c'est clairement en dessous ? Et le $12/5h c'est suffisant pour un setup multi-bots avec des crons ?

Merci

Is minikax doing away with token plan and only moving ahead with credits. This will become expensive!

I'll admit I'm a new discord user, never used it before and I can see why. It's just a nightmare. All I want to do is join the minimax discore and I get this:

https://preview.redd.it/4alxr5oql91h1.png?width=579&format=png&auto=webp&s=3de8d9f7a094af32910aac045986a0337d56d8fc

trying to get me to install some wallet s/w. WTF is this kind of support and wtf is this wallet crap? Couple this with them billing me for stuff - TWICE for a subscription I don't have. My opinion of this so far is not great.

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:

• Context Handling: Zero-shot success on multi-file PRs in complex repos.
• Orchestration: Integrated with MCP for real-time tool use.
• Quality: Focused on deterministic, enterprise-grade output that passes CI/CD on the first run.

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:

https://preview.redd.it/xpf20k6ugtyg1.png?width=1137&format=png&auto=webp&s=c6091dae916b0a6e8762b2323eedcbd1477962bb

Works with Claude Code, Cursor, Kiro, Windsurf — anything that supports MCP.

https://xanther.ai

Discord: https://discord.gg/Y768kBRS

https://medium.com/@xanther.ai/how-a-0-02-call-model-scored-78-2-on-swe-bench-verified-beating-every-model-on-the-leaderboard-153be05a60f1

High latency (3000-9000ms) using MiniMax 2.7 with Claude Code via CCSwitch – Any advice?

Hi everyone,

I’ve been using Claude Code for about a week now. Since I was hitting the token limits pretty quickly, I started using the CCswitch plugin to swap between different APIs.

After doing some comparisons, I decided to try MiniMax 2.7 because the price-to-performance ratio seemed great. However, in practice, the latency is absolutely insane—anywhere from 3000ms to 9000ms. I’m not sure if it’s a server location issue or if I messed up my configuration. For context, the official Claude Pro plan ($20/mo) works perfectly, and MiniMax ($10 plan) works functionally, it’s just painfully slow.

My questions:

1. How can I reduce this latency? I’m based in Taiwan (located south of Japan). Are there specific nodes or settings I should be looking at?
2. Could my CCSwitch configuration be wrong? Is there anything in the plugin settings that typically causes this kind of lag?
3. Any other recommendations for token providers? My budget is under $10 USD, as I’m a relatively light user.
4. Functionality check: Claude Code works fine on the official plan. MiniMax is slow, but the output quality is fine. It’s purely a speed issue.

Thanks in advance for the help! Hope someone can shed some light on this.

Please excuse any awkward phrasing; English is not my native language, so I'm using a translation tool.

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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.

is it me or are you all facing the same problem?

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.