Current as of April 2026. Gemini 3.1 Pro is a heavy-hitter for Hermes Agent deployments that require massive state retention across its 1.0M token context window. It is built for developers who need their agent to remember months of Discord conversations while juggling 47+ tools simultaneously.
Specs
| Provider | |
| Input cost | $2.00 / M tokens |
| Output cost | $12 / M tokens |
| Context window | 1.0M tokens |
| Max output | 66K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning |
What it’s good at
Massive Context Retention
The 1M token context window allows Hermes to maintain a truly persistent identity and memory without aggressive pruning of session history.
Native Multimodal Support
Vision capabilities mean your agent can accurately process screenshots or files sent in Slack, Discord, or Telegram and act on them via tools.
Robust Tool Orchestration
Its native function calling is reliable enough to handle complex MCP tool chains across multiple messaging platforms without losing the reasoning thread.
Where it falls short
Expensive Output Tokens
At $12 per million output tokens, long autonomous loops or verbose agent responses become significantly more expensive than competitors.
Aggressive Safety Filters
Google’s internal safety layers can occasionally trigger on benign cross-platform data, causing the agent to stall or refuse a legitimate tool call.
Context Latency
While it handles 1M tokens, the time-to-first-token increases noticeably as the Hermes memory buffer fills up past the 500k mark.
Best use cases with Hermes Agent
- Cross-Platform Community Management — It can monitor 10+ channels simultaneously and maintain a coherent cross-session memory of every user interaction over several weeks.
- Complex MCP Orchestration — The reasoning engine handles a large number of available tool schemas and long-running autonomous tasks without getting confused by previous tool outputs.
Not ideal for
- Low-Latency Text Bots — The $2/$12 pricing and architecture are inefficient for simple, single-task bots that do not require multimodal input or deep context.
- High-Volume Transactional Agents — The output costs make it cost-prohibitive for agents that generate thousands of small, repetitive messages per hour.
Hermes Agent setup
Obtain an API key from Google AI Studio and ensure your Hermes tool definitions strictly follow the OpenAPI-style schema Gemini requires for native function calling.
Hermes makes custom endpoints easy. Run:
hermes model
Choose Custom endpoint from the menu. Enter the base URL and model identifier when prompted:
- Base URL:
https://generativelanguage.googleapis.com/v1beta - Model:
google/gemini-3.1-pro-preview
Hermes stores the selection and uses it for all subsequent agent runs across whatever platforms you have wired up (Telegram, Discord, Slack, etc.). Tune HERMES_STREAM_READ_TIMEOUT and related env vars if you’re hitting slow providers.
How it compares
- vs Claude 3.5 Sonnet — Claude offers sharper reasoning for complex tool selection but lacks the 1M token context headroom and generous 66K output limit.
- vs GPT-4o — GPT-4o provides better reliability in autonomous loops for some users, but its 128k context window feels cramped compared to Gemini’s million-token ceiling.
Bottom line
If your Hermes Agent needs to be a long-lived autonomous entity with an infinite memory and multimodal awareness, Gemini 3.1 Pro is the best choice despite the higher output pricing.
For more, see our Hermes local-LLM setup guide.