Hermes Best Setup: DeepSeek V4 Flash 0731 + MiMo V2.5 (Vision)

Hermes Agent is Nous Research's self-improving AI agent — one of the few agents with a built-in learning loop that accumulates skills, memory, and a user model across sessions. The MIT-licensed repo sits at roughly 211K–223K stars and 43K forks, and the agent ships 60+ tools^[2]. DeepSeek's official API docs carry a dedicated “Integrate with Hermes Agent” page, so this pairing is first-party on both sides^[1].

Install is a one-liner. On Linux, macOS, WSL2, or Termux run curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash; Windows users run iex (irm https://hermes-agent.nousresearch.com/install.ps1). Then hermes setup opens a Quick Setup flow: choose DeepSeek, paste an API key, keep the base URL at https://api.deepseek.com, and pick a model.

The native model IDs deepseek-v4-flash and deepseek-v4-pro landed in Hermes' DeepSeek provider and OpenRouter lists on April 24, 2026 — the same day DeepSeek previewed the V4 family (PR #14934). You do not need a custom provider file just to run DeepSeek on Hermes.

• Config splits across ~/.hermes/config.yaml (non-secrets) and ~/.hermes/.env (API keys).
• Hermes supports 20+ providers: DeepSeek, Xiaomi MiMo, OpenRouter, Nous Portal, Anthropic, OpenAI, and Ollama among them.
• Xiaomi's own docs also list Hermes Agent as an official MiMo partner alongside Claude Code, Codex, and OpenClaw.

DeepSeek V4 Flash — and V4 Pro for that matter — is a pure text model. The July 31, 2026 update to DeepSeek-V4-Flash-0731 kept the same architecture and input modality; it only re-ran post-training. Send an image to the Flash API and the request fails with an error like “unknown variant image_url, expected text” — a rejection tracked in Hermes issues #32160 and #58581.

One r/hermesagent user put it plainly: “DeepSeek-V4-Flash doesn't support image/vision processing — the model returned a 404 error when I tried to read the photo.” No prompt engineering fixes this; the API rejects image content before the model ever sees it^[3].

If your agent workflow touches screenshots, diagrams, receipts, or photos, a text-only brain is a hard wall. The fix is not to switch models — it is to give Hermes a second, cheaper model that can see.

• DeepSeek V4 Flash 0731: same 284B/13B MoE, same 1M context, still text-only.
• DeepSeek V4 Pro (1.6T/49B) is also text-only — the flagships share the limitation.
• Both run at $0.14 input / $0.28 output per 1M tokens, the cheapest agent-grade pricing covered on this site.

Hermes fixes this with a two-model architecture. When the main model is text-only — DeepSeek is the documented case — pasted images automatically route to a vision_analyze auxiliary tool. That tool calls a separate vision model you configure, which describes the image in text, and the description is injected back into the conversation as context.

You configure the helper under the auxiliary.vision block in ~/.hermes/config.yaml, with three knobs per slot: provider, model, and base_url. The vision slot's default provider is “auto”, which routes images to the main model — and that default breaks with a text-only main model, because the main model cannot read the image. Leave it at auto and image analysis fails; you must point the slot at a real multimodal model.

The same auxiliary mechanism powers web_extract, compression, title_generation, approval, triage, and profile_describer slots. Vision is just the one you need for images.

• vision_analyze: describes images into text for a text-only main model.
• web_extract: pulls page content for research-style tasks.
• compression: summarizes long context to control token spend.
• approval / triage: human-in-the-loop and routing helpers.

MiMo-V2.5 is Xiaomi's native omnimodal model — text, image, video, and audio under one architecture. It is a 310B-total / 15B-active Sparse MoE trained on 48T tokens with a native 1M-token context and a 729M-parameter ViT vision encoder^[6]. On OpenRouter it is described as delivering “Pro-level agentic performance at roughly half the inference cost”.

The decisive number is price. MiMo-V2.5 official pricing is $0.14 input (cache miss) / $0.28 output / $0.0028 cache-hit per 1M tokens — identical to DeepSeek V4 Flash^[4]. Adding a vision model does not raise your per-token ceiling.

The trap is the name. MiMo-V2.5-Pro is a different, text-only model. Artificial Analysis flags the Pro as non-multimodal with no image input, and Hermes issue #18884 confirms that models.dev mislabels mimo-v2.5-pro with attachment:true^[7]. When you see “Pro”, do not assume vision — the multimodal model is mimo-v2.5.

• MiMo-V2.5: omnimodal and vision-capable, $0.14/$0.28 — use this one in the vision slot.
• MiMo-V2.5-Pro: text-only, $0.435/$0.87, not a vision model — do not use it here.
• V2-series names (mimo-v2-pro, mimo-v2-omni) retired on June 30, 2026; move to mimo-v2.5.

With both API keys in hand, the whole setup is one config block. The main model becomes deepseek-v4-flash — the 0731 build, and the model ID did not change — while the auxiliary.vision slot gets provider xiaomi with model mimo-v2.5.

That single block is the whole Hermes DeepSeek MiMo setup: a text main model and a multimodal helper at the same price. The equivalent CLI is hermes config set auxiliary.vision.provider xiaomi and hermes config set auxiliary.vision.model mimo-v2.5, or the interactive hermes model menu under “Configure auxiliary models”.

If the xiaomi provider errors in your Hermes version — models.dev metadata for the CN plan has known bugs — route the slot through OpenRouter instead: provider openrouter with model xiaomi/mimo-v2.5. Xiaomi's official Hermes integration docs use the XIAOMI_API_KEY / XIAOMI_BASE_URL environment variables for direct access.

# ~/.hermes/.env
DEEPSEEK_API_KEY=sk-...
XIAOMI_API_KEY=sk-...                # or OPENROUTER_API_KEY=sk-or-...

# ~/.hermes/config.yaml
model:
  default: deepseek-v4-flash         # main model = text reasoning (0731 build)
  provider: deepseek
  base_url: https://api.deepseek.com

auxiliary:
  vision:                            # vision slot = image support
    provider: xiaomi                 # or openrouter
    model: mimo-v2.5                 # NOT mimo-v2.5-pro (that one is text-only)
    # base_url: https://api.xiaomimimo.com/v1   # set explicitly if needed

MiMo V2.5 speaks OpenAI-compatible APIs, so it drops into any OpenAI-shaped client. The base URL is https://api.xiaomimimo.com/v1 with model id mimo-v2.5, and an Anthropic-compatible https://api.xiaomimimo.com/anthropic endpoint is available as an option^[5].

Images are accepted as a public URL or a Base64 data URL (data:{MIME};base64,...). Supported formats are JPEG, PNG, GIF, WebP, and BMP; a single image must stay under 50MB, and multiple images per request are allowed. There is no local-file upload — the image must be reachable over HTTP or embedded as Base64.

The cost math stays small: one common-resolution image costs roughly 1,024 image tokens. At $0.14 per 1M tokens that is a rounding error per image, but it is extra on top of the conversation — community reports of MiMo “burning tokens like crazy” usually trace back to vision and long tool loops, not the base rate.

• Base64 and public URLs both work; local file paths do not.
• Roughly 1,024 image tokens per typical image.
• Multi-image requests are supported, so a screenshot batch costs about 1K tokens per image.

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ["MIMO_API_KEY"],
    base_url="https://api.xiaomimimo.com/v1",
)
client.chat.completions.create(
    model="mimo-v2.5",
    messages=[{"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": "https://.../image.png"}},
        {"type": "text", "text": "please describe the content of the image"},
    ]}],
)

The pairing makes one simple bet: Flash is the agent brain, MiMo V2.5 is the eyes, and neither raises the other's cost. Because both models bill at $0.14/$0.28, the multimodal upgrade is effectively free per token — you only pay for the ~1,024 image tokens per picture.

This is community consensus, not a fringe hack. r/hermesagent threads frame it as a principle — “hermes works best when you don't hinge on one model” — and recommend a vision-capable model in the auxiliary slot exactly as described here^[8]. The same community also votes for multi-model failover chains (MiMo 2.5, GLM 5.2, DeepSeek V4 Pro) when a task outgrows a single model.

Be honest about the trade-offs. Some users report MiMo is “less capable in autonomous work” than Flash in head-to-head agent loops, and vision tokens add up on heavy screenshot workflows. That is why the split exists: you want Flash driving, with MiMo only describing images on demand.

One more caveat: Hermes' direct xiaomi provider under auxiliary.vision is not yet battle-tested in the wild — issue #18884 shows metadata bugs on the Xiaomi side. The safe default is provider xiaomi, and if it fails, OpenRouter's xiaomi/mimo-v2.5 is the documented fallback.

• Flash 0731 does the reasoning and coding; it hit 82.7 on Terminal-Bench 2.1 in DeepSeek's own numbers.
• MiMo V2.5 handles vision_analyze only — image to text, then hands the context back.
• Same token price on both sides; the vision slot adds no premium.
• Budget tip: keep the conversation on Flash and let cache hits ($0.0028/M) absorb the long context — the same lever the Reasonix guide uses to hit 99%+ cache rates.