The Best OpenClaw Models for Different Tasks and Budgets

Flagship agent models burn money fast. The good news is that you don’t need a top-tier model on every OpenClaw turn. Here’s how to match current models to the job without lighting money on fire.

Updated 27/07/2026

Deployment challenges for autonomous agents include not only raw reasoning capacity, but economic viability. And there is no single best best model to use with OpenClaw or Hermes. A careful coding agent, a browser worker and a heartbeat job all have different failure costs. Paying flagship rates for every turn wastes money. Sending a complicated migration to the cheapest model can waste even more when retries and bad tool calls pile up.

So the more useful question is: what’s the best for what, under which limits?

Updated Model Pricing Comparison

The table below reflects current API rates as of late July 2026. In OpenClaw, per-session cost is mostly a context-management problem. Compaction strategy, memory flushing, and how much garbage context you keep dragging forward will swing token burn by multiples, even on the same task or in a quick chat that seems straightforward.

Treat the numbers as raw rates. Your bill depends on routing and hygiene. If you’re not regularly compacting, pruning tool output and flushing stale memory, frontier-class models can surprise you with a severely high API invoice.

Quick Recommendations for Different Tasks

My current picks landed on the following models, based on five filters:

• The billing route had to be legible. API key, subscription/OAuth and local inference are different products.
• It had to resolve in OpenClaw. Launch post hype wasn’t enough.
• The provider still documents price and access.
• It had to fit actual agent work. Tool use and recovery over chat-leaderboard cosplay.
• Failure cost. Cheap first attempts aren’t cheap after three retries and human cleanup.

OpenClaw Model Recommendations by Workload

Ordinary daily agent work

I’d start with openai/gpt-5.6-terra or openai/gpt-5.6-luna.

Terra is the practical daily OpenAI workhorse at $2.50 / $15 before long-context uplifts. Luna is the cheaper value tier at $1 / $6 when the job needs tool use and multi-step follow-through, but isn’t so highly complex that Sol is needed. Both resolve cleanly in current OpenClaw catalogs.

If your account still runs openai/gpt-5.4 cleanly, that’s also still a viable OpenAI option at the same broad rate band as Terra. Don’t stay on it forever out of habit though when Terra and Luna are now available.

Difficult coding and orchestration

Use openai/gpt-5.6-sol when the model has to plan across a repository, preserve constraints and recover from tool errors. OpenAI lists Sol at $5 / $30 before long-context uplifts, so the output rate is not cheap. That’s the point of routing though.

If Sol stalls on a nasty recovery job and you’re willing to leave the OpenAI lane, Claude Opus 4.8 (anthropic/claude-opus-4-8, $5 / $25) and Claude Fable 5 (anthropic/claude-fable-5, $10 / $50) are the main high-end alternatives currently resolving in OpenClaw. For me, Anthropic models are only escalation options and therefore not the default stack in this guide.

Browser research and synthesis

xai/grok-4.5 is really good when you want a strong non-OpenAI research default. xAI lists $2 / $6 below 200k prompt tokens and double that at or above. Realtime information still needs search tools.

openai/gpt-5.6-terra is the OpenAI-side research default if you want one stack for everything. For legal, financial or reputational synthesis, rerun the source check and hand the evidence to Sol. That’s a review step, not an excuse to browse the web on a flagship rate.

If 4.5 is still missing from your picker, xai/grok-4.3 at $1.25 / $2.50 is its cheaper sibling with a 1M window and does an excellent job with research tasks. I like using Grok for trend reports and anything current where social media is involved.

Budget agency

If the agent has to use tools and keep state across steps, but the bill has to stay boring, these are still serious options:

DeepSeek V4 Flash / Pro
Official rates remain extremely low for a 1M context window: Flash at $0.14 / $0.28, Pro at $0.435 / $0.87 on cache miss. OpenClaw docs center deepseek/deepseek-v4-flash and deepseek/deepseek-v4-pro. Some installs still show only the retiring deepseek-chat / deepseek-reasoner names. Check your local catalog before you wire production.

GLM 5.2
Z.ai’s current flagship band is GLM-5.2 at $1.40 / $4.40, with GLM-5 at $1.00 / $3.20 and GLM-4.7 at $0.60 / $2.20. For coding and tool-using agents, this is one of the strongest non-OpenAI options in the mid-price lane.

In OpenClaw the ref depends on the gateway: venice/zai-org-glm-5-2, nvidia/z-ai/glm-5.1, venice/zai-org-glm-5, and other mirrors all show up. Pick the route you have authenticated and priced. If you only see older GLM-4.7/GLM-5 IDs, those still work as cheaper stand-ins, too.

Kimi K3
K3 is Moonshot’s current flagship with $3.00 in / $15.00 out on cache miss, and a 1M context window. It’s the Kimi model to mention first.

OpenClaw is the catch though. On my last check, the clean Moonshot refs were still moonshot/kimi-k2.6 and moonshot/kimi-k2.7-code, plus mirrors on Venice, OpenRouter, NVIDIA and others. I did not yet see a kimi-k3 ref in the live catalog. If K3 is on your provider route, use it. If not, K2.7 Code is the coding-focused fallback and K2.6 is the general one. Pick the route you have authenticated and priced.

Side note for anons: OpenClaw supports Venice for privacy-focused inference, which I’ve wired and can recommend. If you’d like to try it out, use my link for 1,000 free tokens for new users. OpenClaw supports both fully private models and anonymized proxy access to major proprietary ones.

Qwen 3.6 Plus
International Qwen 3.6 Plus is $0.50 / $3.00 up to 256k and $2 / $6 above that, with a 1M window. Qwen 3.5 Plus is still slightly cheaper on short context. Refs vary: qwen-oauth/qwen3.6-plus, Venice mirrors, OpenRouter variants. Pricing varies by provider; my above table cited Alibaba Cloud Model Studio international rates.

MiniMax M3
MiniMax is still in the game. OpenClaw has a first-class MiniMax provider, official MiniMax docs include an OpenClaw setup path, and the current default is M3. OpenClaw cost metadata puts M3 around $0.60 / $2.40 with a 1M context. M2.7 remains the cheaper previous tier at about $0.30 / $1.20.

Where MiniMax sits in practice: strong budget/coding-agent lane and a common sub-agent or fallback, not the model I would make the only brain on a high-stakes job. If you already like MiniMax’s Coding Plan/OAuth route, M3 is the current ref to test.

All of these are viable budget options. They’re not automatic Sol or Opus replacements. I’d put a validator on the output, cap retries, and escalate any failures to Luna, Terra or Sol.

Worth watching

• Claude Opus 5: in Anthropic’s docs at $5 / $25, but not yet in the OpenClaw catalog.
• Claude Mythos 5: same specs/pricing as Fable, invitation-only under Project Glasswing.
• Gemini 3.5 Flash: shows up as google-vertex/gemini-3.5-flash; add it once your route and pricing are clean.

OpenClaw Models for cheap recurring jobs

Use openai/gpt-5.4-nano, Xiaomi’s cheap MiMo tier, or anthropic/claude-haiku-4-5 for tight inputs, narrow schemas and validators. Titles, classification, extraction, simple summaries.

If your small model keeps failing, escalate the check first: shorter prompt, tighter schema, no history dump. If you still need a better model, step up to openai/gpt-5.4-mini, openai/gpt-5.6-luna, or DeepSeek V4 Flash.

Local and private work

ollama/llama3.2:3b is a clean local reference in the current catalog. It’s a super small model but it can be harnessed for light local utility: titles, classification, short extraction, simple summaries, heartbeat.

A Practical Primary, Fallback and Utility Stack

{
  agents: {
    defaults: {
      model: {
        primary: "openai/gpt-5.6-terra",
        fallbacks: [
          "openai/gpt-5.6-sol",
          "xai/grok-4.5"
        ]
      },
      utilityModel: "openai/gpt-5.4-nano",
      heartbeat: {
        model: "openai/gpt-5.4-nano",
        lightContext: true,
        isolatedSession: true
      },
      subagents: {
        model: "openai/gpt-5.4-mini"
      }
    }
  }
}

Sol-first alternate for hardcore coding days:

{
  agents: {
    defaults: {
      model: {
        primary: "openai/gpt-5.6-sol",
        fallbacks: [
          "openai/gpt-5.6-terra",
          "xai/grok-4.5"
        ]
      }
    }
  }
}

Budget-first alternate (if you have k3, substitute k2.7 with it):

{
  agents: {
    defaults: {
      model: {
        primary: "deepseek/deepseek-v4-flash",
        fallbacks: [
          "venice/zai-org-glm-5-2",
          "moonshot/kimi-k2.7-code",
          "minimax/MiniMax-M3",
          "openai/gpt-5.6-luna"
        ]
      }
    }
  }
}

Research-heavy alternate:

{
  agents: {
    defaults: {
      model: {
        primary: "xai/grok-4.5",
        fallbacks: [
          "openai/gpt-5.6-terra",
          "openai/gpt-5.6-sol"
        ]
      }
    }
  }
}

OpenClaw tries fallbacks when the primary route fails. It doesn’t inspect every prompt and choose the cheapest model for you.

Keeping OpenClaw Costs Under Control

Model price is only one part of the bill. There are a number of other factors at play, which can all help keep things in budget.

Watch context growth

Long sessions resend history. A harmless-looking tool dump can be paid for again on later turns. Start a clean session when the old conversation no longer helps.

Compact before the session becomes a landfill

Compact at clear task boundaries. Preserve decisions, file paths and unresolved questions; drop raw browsing debris.

Trim tool output

Do not feed an agent a 20,000-line log when the error and surrounding lines will do.

Make heartbeats boring

Small model, short prompt, deterministic check. Don’t attach the full history of the agent it’s monitoring.

Cap retries

Retries can turn a cheap model into an expensive option. Define the failure that justifies a retry, set a small maximum, escalate after repeated identical errors.

Route by workload

Give each agent or scheduled job the cheapest model that can pass its validator reliably. Keep Sol for difficult work and review.

Check provider billing separately

OpenAI API keys and ChatGPT/Codex subscription credentials are different routes. Anthropic API keys and Claude CLI reuse are different routes. xAI OAuth and API keys are different routes. MiniMax Coding Plan/OAuth and paygo API keys are different routes. Ergo: check the current provider docs before you build a production budget around subscription access.

Bottom Line

Start with Terra or Luna for ordinary agent work. Escalate hard coding and recovery to Sol. Use Grok 4.5 when you want a strong non-OpenAI research or coding lane. For budget agency, definitely look at GLM 5.2, DeepSeek V4, Kimi K3, Qwen 3.6 Plus and MiniMax M3 before you assume only flagships can handle your agentic workloads. Keep Nano, Mini, MiMo or Haiku for bounded utility work. Use a local Ollama model only when the workload fits a small local utility model, or after you have installed something larger than llama3.2:3b.

The model list will keep moving. Check the exact OpenClaw reference, test the route, and keep fallbacks simple.

Changelog

• 27 July 2026: Full rewrite of the Opus 4.6 budget roundup into current workload guide. OpenAI Sol/Terra/Luna as default stack. Claude retained in the market comparison, not as house default. Restored pricing-table layout. Brought DeepSeek, Kimi, Qwen, MiniMax M3 and MiMo back into the comparison. Replaced Grok 4.3 with Grok 4.5 as current xAI pick.
• 23 March 2026: Updated to reflect developments at OpenAI.
• 05 March 2026: Original Opus 4.6 alternatives roundup for OpenClaw users.

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Sources

Accessed 27 July 2026:

1. OpenClaw model selection and configuration: https://docs.openclaw.ai/concepts/models
2. OpenClaw provider directory: https://docs.openclaw.ai/providers
3. OpenClaw Anthropic provider: https://docs.openclaw.ai/providers/anthropic
4. OpenClaw OpenAI provider: https://docs.openclaw.ai/providers/openai
5. OpenClaw xAI provider: https://docs.openclaw.ai/providers/xai
6. OpenClaw DeepSeek provider: https://docs.openclaw.ai/providers/deepseek
7. OpenClaw Ollama provider: https://docs.openclaw.ai/providers/ollama
8. OpenAI API pricing: https://developers.openai.com/api/docs/pricing/
9. Anthropic model overview: https://platform.claude.com/docs/en/about-claude/models/overview
10. Anthropic pricing: https://platform.claude.com/docs/en/about-claude/pricing
11. xAI models and pricing: https://docs.x.ai/docs/models
12. xAI Grok 4.5 model page: https://docs.x.ai/developers/models/grok-4.5
13. Z.ai model pricing: https://z.ai/model-api
14. DeepSeek models and pricing: https://api-docs.deepseek.com/quick_start/pricing
15. Ollama Llama 3.2 3B page: https://ollama.com/library/llama3.2:3b
16. Live catalogue commands: openclaw models list --all --plain, openclaw models list --provider anthropic|xai|openai|deepseek|ollama

Author

Jenna Brinning

A localization consultant, writer, editor, and content strategist with over two decades of experience in tech and language ops, Jenna holds an M.A. in journalism and communication science from Freie Universität Berlin, and is a certified PSPO and PSM who loves helping startups and small businesses reach international users.

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