HTML explainer artifact

Best Models with Hermes after “6 billion tokens”

Source: https://www.reddit.com/r/hermesagent/comments/1tjeitj/best_models_with_hermes_after_testing_with_6/
Generated: 2026-05-23T21:25:44Z

What this link is

A high-traction r/hermesagent Reddit discussion about which model/provider mix works best for Hermes Agent. The original poster claims very heavy usage across web scraping, research analytics, software development, LLM inference experiments, and multi-step cron jobs, with cost effectiveness as the main ranking criterion.

Source signals fetched from Reddit JSON during this pass:

Post: “Best Models with Hermes after testing with 6 billion tokens” by u/Puzzleheaded-Gas8179.
Traction: ~210 score, 0.97 upvote ratio, 131 comments at fetch time.
Flair: cost/pricing, token plans, API vs subscription, budget tips.
Nature: anecdotal field report plus comments, not a controlled benchmark.

OP’s ranking and the useful takeaway

Rank	Model / plan	What OP is really saying
1	GPT 5.5	Best overall for serious Hermes work. Expensive, but time saved beat the dollar savings from slower models.
2	Kimi k2.6	Strong value, but the 5-hour quota can interrupt productive runs.
3	GLM 5.1	Capable, but loop-prone in OP’s use. One example: an analysis taking ~18 hours vs under an hour on GPT 5.5.
4	MiniMax M2.7	Fine and cheap; needs tighter prompting and is less forgiving.
5	Qwen 3.6 Max	Good, but not the main recommendation in the post.
6	Gemini	OP’s weakest category; comments push back and ask which Gemini variant was tested.
Local	Qwen 3.6 35B A3B	OP’s top local option. Qwen 3.6 27B dense was “good but too slow” for their workflow.

The core lesson is not “always buy the biggest model.” It is: measure cost as wall-clock time + failure loops + context recovery, not just token price. For agent work, a cheap model that gets stuck for hours can be more expensive than a frontier model that finishes cleanly.

Community patterns in the comments

Frontier fallback

Several commenters agree that GPT 5.5 removes frustration. A recurring pattern is to use cheaper models for ordinary runs and switch to GPT 5.5 for complex coding, research, or “fix this properly” moments.

Cheap default + escalation

Kimi, MiniMax, DeepSeek V4 Flash/Pro, GLM, and Qwen appear as budget/default candidates. The winning strategy is not one model forever; it is an escalation ladder.

DeepSeek split

Multiple commenters say DeepSeek V4 Flash can be strong with high reasoning settings, while others report hallucinations or loops. Some use Flash for mapping/search and Pro or GPT for deeper fixes.

Local model caveats

Local Qwen variants are attractive, but tool-calling format, speed, RAM, and hardware matter. One thread notes a 32GB M2 Max struggling with a 27B path while MLX builds may help.

Suggested Hermes model stack for Ananth / Dab

This maps cleanly onto Dab’s operating model: Dab orchestrates, Claude Code/default executors handle substantial coding, and the Notice Board captures outcomes. A practical model policy should separate routine orchestration, deep work, and review/fallback.

1. Default / cheap lane
   - routine captures, small edits, low-risk summaries
   - cheap subscription/API model with enough tool reliability

2. Build / research lane
   - substantial coding, multi-file reasoning, hard synthesis
   - GPT 5.5-class model or Claude-class executor when quality matters

3. Review / rescue lane
   - independent code review, stuck-loop recovery, final critical validation
   - use a different strong model/provider when possible

4. Local lane
   - private/offline experiments, background low-stakes jobs
   - only if tool-calling and speed are good enough for Hermes workflows

The most actionable idea for Dab is a provider escalation policy: start cheap when the task is reversible and bounded; escalate quickly when there are signs of loop, tool-call confusion, or repeated partial fixes.

What to steal for Mission Control

Add model choice to task briefs. When delegating, include the reason for the model lane: cheap/default, deep work, review, or rescue.
Track failure modes, not vibes. For each provider/model, log: loops, tool-call failures, bad edits, context loss, cost/run, and wall-clock time.
Use “expensive model earlier” for hard cron jobs. Autonomous scheduled tasks are costly to debug after the fact. If a job needs high reliability, wall-clock and correctness beat token thrift.
Prefer explicit fallback providers. The comments mention fallback-provider setups; that belongs in Dab Improvements if Ananth wants robust unattended execution.
Separate orchestrator and implementor. Some users run a cheaper model as orchestrator and fire Codex/Claude/GPT for development. That aligns with Dab → Claude Code execution discipline.

Decision heuristic

Situation	Use	Why
Small capture, title cleanup, local render validation	cheap/default	Low risk; fast enough; errors easy to catch.
Multi-file repo change, architecture page, external research synthesis	deep work	Context and correctness dominate token cost.
Agent loops, repeated wrong patches, unclear tool-calling	rescue	Switch model/provider rather than burning hours.
Private/experimental/offline work	local	Good only if tool calling is stable and speed is acceptable.

Caveats

This is a Reddit anecdote, not a reproducible benchmark. Treat it as community signal and workflow inspiration.
Model names/prices/quotas change fast; the durable lesson is the routing policy, not the exact ranking.
Subscription access, OAuth, API limits, and Hermes provider support differ by account and version. Verify before relying on any single lane for unattended jobs.
“Cost effective” must include Dab’s verification burden. A model that needs more hand-holding is expensive even if the plan is cheap.

Recommended next step

Create or update a Dab Improvement item only if Ananth wants this operationalized: “Hermes provider escalation policy + fallback providers.” The deliverable would be a small matrix of model lanes, default triggers, rescue thresholds, and one test suite of representative Dab tasks.