Current as of April 2026. Claude 3.7 Sonnet (thinking) is the current gold standard for Hermes Agent because it actually reasons before firing off tools across 15+ messaging platforms. It is the first model where the reasoning process feels like a genuine safety check for autonomous actions rather than just a coding feature.
Specs
| Provider | Anthropic |
| Input cost | $3.00 / M tokens |
| Output cost | $15 / M tokens |
| Context window | 200K tokens |
| Max output | 64K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning, web_search |
What it’s good at
Tool-Use Reliability
It rarely hallucinates arguments for the 47+ built-in Hermes tools, maintaining high precision even with complex JSON schemas for Slack or Discord.
Multi-Platform Reasoning
The thinking block allows the model to reconcile conflicting inputs from different messaging channels before executing shell commands or SSH tasks.
Context Management
With a 200K context window, it maintains a coherent identity and memory across long-running autonomous sessions without losing the conversation thread.
Where it falls short
High Latency
The reasoning phase adds significant delay, making real-time chat responses on Telegram feel sluggish compared to standard non-thinking models.
Output Costs
At $15 per million output tokens, those long internal monologues eat into your budget much faster than standard Sonnet 3.5 or GPT-4o.
Best use cases with Hermes Agent
- Cross-Platform Automation — It excels at monitoring Slack, processing data via MCP, and posting results to Discord where accuracy is more important than speed.
- Long-Session Autonomy — Ideal for persistent agents running on Modal or Docker that need to remember complex user preferences over several days of interaction.
Not ideal for
- High-Volume Chatbots — If you are building a basic Telegram bot for instant replies, the reasoning overhead and $15/1M output cost are overkill.
- Simple Tool Triggers — It is too expensive and slow for basic tasks like checking the weather or setting timers that do not require deep reasoning.
Hermes Agent setup
Set your max_tokens high, at least 16K, to accommodate the thinking blocks and ensure your Hermes config explicitly enables the thinking parameter to avoid truncated reasoning chains.
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://api.haimaker.ai/v1 - Model:
anthropic/claude-3.7-sonnet:thinking
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 GPT-4o — GPT-4o is faster and cheaper at $5/1M output, but it lacks the explicit thinking trace that prevents Claude from making impulsive tool-calling errors.
- vs DeepSeek-R1 — R1 is significantly cheaper for reasoning, but its tool-calling reliability in complex Hermes workflows is lower than Sonnet 3.7.
Bottom line
If you value reliability and coherent multi-platform automation over raw speed, Claude 3.7 Sonnet (thinking) is the only choice for a production-grade Hermes Agent.
TRY CLAUDE 3.7 SONNET (THINKING) IN HERMES
For more, see our Hermes local-LLM setup guide.