OpenClaw Backend Optimization: Groq vs Claude for Persistent AI Agents

Jason Perlow

27 Feb 2026 — 7 min read

Persistent systems don’t sleep — and neither does the meter.

Context Windows, Tool Stability, and the Economics of Continuous Execution

Disclosure: This post reflects independent personal experimentation using publicly available documentation and pricing. It reflects only my personal views and is not professional advice, and does not represent any organization, employer, or official position.

Abstract

Persistent AI agents generate steady token flow, accumulate context between turns, and stress backend pricing models in ways interactive chat sessions do not. This post examines the cost and stability tradeoffs of running OpenClaw continuously using Claude, Groq GPT-OSS-120B, and Together AI’s Qwen 3 Next 80B. Using publicly available pricing and real-world usage scenarios, it explores why context headroom and sustainable economics often matter more than frontier-model capability for personal agent workloads.

The Starting Point

A couple of weeks ago, I began running OpenClaw as a persistent agent runtime in my home lab. My initial backend was Claude. For interactive reasoning, structured tool invocation, and multi-step coding tasks, Claude remains excellent. I still use Claude Max heavily for direct development work.

But persistent agents are not interactive sessions.

They inject system scaffolding between turns. They persist tool schemas. They summarize memory. They retry plans. They accumulate execution traces. Token consumption becomes steady rather than bursty.

When I first experimented with OpenClaw, it was briefly possible to authenticate using a Claude subscription token. That made early testing simple. That access path was later discontinued, and persistent workloads now require API credits.

That distinction changes the economics immediately.

Subscription plans are optimized for interactive usage. API billing is optimized for programmatic workloads. A continuously running agent behaves much closer to infrastructure than to a chat window.

That’s what forced the backend rethink.

What Continuous Use Actually Costs

Using publicly listed pricing (early 2026):

Groq GPT-OSS-120B: ~$0.15 per 1M input tokens, ~$0.60 per 1M output
Together Qwen 3 Next 80B: ~$0.15 per 1M input, ~$1.50 per 1M output
Claude Haiku: ~$1 per 1M input, ~$5 per 1M output
Claude Sonnet: ~$3 per 1M input, ~$15 per 1M output

At light hobby usage — say 10M input and 2.5M output tokens per month — everything looks reasonable.

At 50M input and 12.5M output:

GPT-OSS ≈ $15
Qwen ≈ $26
Haiku ≈ $112

At heavier persistent usage — 300M input and 75M output:

GPT-OSS ≈ $90
Qwen ≈ $157
Haiku ≈ $675
Haiku with modest Sonnet escalation ≈ $800+

The arithmetic is straightforward. The implications are not.

Under continuous execution, pricing architecture compounds. What feels negligible at a small scale becomes structural at a sustained scale.

For a persistent personal agent, that matters.

Context Window Is Stability Insurance

Cost was only half the equation. The context window was the other.

OpenClaw injects significant context between turns by design: system instructions, tool definitions, execution state, memory summaries, and prior reasoning traces. Under short-lived interaction, this accumulation is invisible. Under continuous execution, it becomes a constraint.

Below roughly 128K tokens of headroom, degradation appears under sustained injection. Not immediate collapse — drift. Tool schemas fall out of scope. Structured calls weaken. Planning coherence erodes.

For persistent agents, context window is not a luxury. It is stability insurance.

At present, GPT-OSS-120B is the only Groq model I’ve tested that consistently satisfies three constraints simultaneously:

~128K context window
Reliable tool-call formatting
Sufficient multi-step reasoning depth

Other models may be faster or smaller. Under sustained context accumulation, they did not maintain all three properties at once.

That narrowed the field quickly.

Why Not Just Run Something Bigger?

Qwen 3 Next 80B is strong. It is faster than GPT-OSS-120B in my testing and offers a 256K context window. Tool-call behavior has been consistent.

You could run everything on Qwen.

The tradeoff is steady-state cost. Over sustained workloads, Qwen roughly doubles baseline monthly spend relative to GPT-OSS-120B. It remains materially less expensive than Claude API credits, but the delta compounds under continuous execution.

Since publishing the original version of this post, I also tested xAI's Grok-4 Fast as a potential primary backend. On paper, it is compelling. The 2M token context window is roughly 15x larger than GPT-OSS-120B, which directly addresses the context accumulation problem that persistent agents create. It includes built-in web and X search without requiring a separate tool pipeline. And the pricing — approximately $0.20 per 1M input and $0.50 per 1M output — comes in at only about $7.50 per month above GPT-OSS-120B at sustained workloads. For that premium, you get a dramatically larger context window and a frontier agentic tool-calling trained through reinforcement learning.

In practice, several considerations tempered the comparison.

First, latency. GPT-OSS-120B runs on Groq infrastructure optimized for low-latency token generation. In my benchmarking — a complex distributed caching system design prompt of roughly 150 tokens — Groq returned a response in 4ms compared to 16ms for Grok-4 Fast. That is a 4x difference. Grok-4 Fast also produced a longer response (8,756 characters vs 6,947), which means it consumes more output tokens per query at similar per-token rates. xAI has not disclosed the parameter count for Grok-4 Fast, but the full Grok-4 is reported as a 3-trillion-parameter mixture-of-experts model. If the Fast variant is anywhere near that scale, the latency gap has a structural explanation.

Second, billing infrastructure. For a persistent agent, billing continuity matters as much as pricing. Groq's Developer plan bills automatically in arrears — you add a payment method, and usage is charged at progressive thresholds, then monthly. Your agent never gets cut off. Together AI supports auto-replenishment based on a balance threshold, which requires some configuration but enables continuous operation. xAI defaults to prepaid credits with a $0 invoiced billing limit. When credits run out, the API stops. You can raise the limit manually, but the default behavior is the opposite of what a persistent agent needs. For a runtime that operates unattended, that distinction is operationally significant.

Third, platform considerations. xAI is closely integrated with X, and some users may have reservations about that association, independent of technical merit. That is a personal decision, but it is worth noting as a factor that could influence adoption.

For a personal, persistent agent focused primarily on orchestration, structured execution, and incremental reasoning, the combination of Groq's inference speed, frictionless billing, and GPT-OSS-120B's proven stability remains the most practical, steady-state choice in my setup. Qwen provides escalation when necessary, and Grok-4 Fast remains an option worth revisiting as both its infrastructure and billing model mature.

This is an engineering tradeoff, not a value judgment.

Why the Free Tier Isn’t Enough

Groq’s free tier is remarkable for experimentation. GPT-OSS-120B is available without a credit card, which makes evaluation frictionless.

The constraint is not access. It’s rate limits.

Free tiers are typically capped at requests and tokens per minute. A persistent agent does not behave like a human typing into a chat window. It can issue bursts of tool calls, retries, summarization passes, and memory updates within short intervals. Those bursts can exceed per-minute ceilings even if total monthly usage is modest.

When that happens, the API returns 429 rate-limit errors.

OpenClaw will retry, but repeated throttling introduces latency spikes and execution stalls. Plans slow down. Tool chains break mid-execution. Memory updates lag behind task state.

For interactive testing, this is tolerable.

For a continuously running agent, it becomes destabilizing.

That’s why sustained operation generally requires a developer plan, even if total token consumption remains moderate.

Do Personal Agents Require Frontier Models?

There is a common assumption that serious agent systems require frontier-tier intelligence.

In my experience with personal workloads, that has not been the binding constraint.

Most persistent tasks involve structured execution, file operations, retrieval, summarization, incremental planning, and tool orchestration. These require stability, headroom, and consistent schema adherence more than top-tier benchmark performance.

When persistent agents fail, it is usually because:

Context windows collapse under accumulation
Tool calls drift out of schema
Rate limits interrupt execution
Or cost throttling forces artificial constraints

It is less often because the model lacked raw reasoning depth.

For personal persistent runtimes, the practical question becomes:

What can I afford to run continuously without watching the meter?

That framing changes the optimization problem.

Conclusion

Interactive AI and persistent AI are different workloads.

Under sustained operation, context headroom and pricing architecture compound over time. The most capable model is not necessarily the most practical.

For personal persistent agents, sustainability often matters more than marginal benchmark superiority.

That realization is what drove the backend change.

But the economics described here are a snapshot, not a permanent state. This shift is already visible. OpenAI’s ChatGPT desktop app, Google’s Gemini Chrome extension (which runs as a resident process), Anthropic’s Claude Desktop and Cowork, and Ollama’s recently launched OpenClaw integration — which ships with tiered subscription plans explicitly designed around daily and continuous agent usage — all point toward a future where persistent agent capabilities are bundled directly into consumer subscriptions rather than assembled from API credits and configuration files. Ollama’s approach in particular trades model selection and per-token visibility for flat monthly pricing and zero configuration, which is the right tradeoff for most users even if it is not the right tradeoff for this setup.

Of these, as a user, Anthropic's offerings currently come closest to providing consumer-facing agentic tooling with local system integration — OpenAI's agentic tools like Codex remain developer-oriented, and Gemini's extension does not yet support local tooling. But this area of the industry is accelerating rapidly, and progress is measured in weeks rather than years. The general direction trades flexibility for accessibility — and for most users, accessibility wins.

Projects like OpenClaw offer far more control, but the barrier to entry is real. Getting the configuration described in this post to a stable, cost-effective state took two weekends of trial and error — testing providers, debugging tool-call behavior, and narrowing down which model combinations actually held up under sustained execution. That is not a mainstream user experience.

As persistent agents move from enthusiast experimentation toward everyday personal computing, providers will face pressure to price for continuous execution and to lower the operational complexity of keeping an agent running. The competitive surface is shifting from model capability alone toward the full cost — in dollars and in effort — of sustained operation.

That trajectory is worth watching.

~/.openclaw/openclaw.json

Hybrid Configuration (Groq Primary, Qwen Escalation)

{
  "models": {
    "providers": {
      "groq": {
        "baseUrl": "https://api.groq.com/openai/v1",
        "api": "openai-completions",
        "models": [
          {
            "id": "openai/gpt-oss-120b",
            "name": "GPT-OSS 120B",
            "contextWindow": 128000,
            "maxTokens": 8192
          }
        ]
      },
      "together": {
        "baseUrl": "https://api.together.xyz/v1",
        "api": "openai-completions",
        "models": [
          {
            "id": "Qwen/Qwen3-Next-80B-A3B-Instruct",
            "name": "Qwen 3 Next 80B",
            "contextWindow": 262144,
            "maxTokens": 8192
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "groq/openai/gpt-oss-120b",
        "fallbacks": [
          "together/Qwen/Qwen3-Next-80B-A3B-Instruct"
        ]
      }
    }
  }
}

Switching Configurations

Edit:~/.openclaw/openclaw.json
Restart gateway:

openclaw gateway stop
openclaw gateway start

Verify:

openclaw channels status

No retraining required. Configuration changes apply immediately.

API Key Storage

Keys are stored per-agent at:

~/.openclaw/agents/main/agent/auth-profiles.json

Minimal example:

{
  "version": 1,
  "profiles": {
    "groq-main": {
      "type": "api_key",
      "provider": "groq",
      "key": "gsk_YOUR_GROQ_API_KEY"
    },
    "together-main": {
      "type": "api_key",
      "provider": "together",
      "key": "tgp_v1_YOUR_TOGETHER_API_KEY"
    }
  },
  "order": {
    "groq": ["groq-main"],
    "together": ["together-main"]
  },
  "lastGood": {
    "groq": "groq-main",
    "together": "together-main"
  },
  "usageStats": {}
}

Ensure restricted permissions:

chmod 600 ~/.openclaw/agents/main/agent/auth-profiles.json