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 12 October 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 mid-October 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.

Prices are in USD per million tokens and reflect standard API rates unless otherwise noted. Pricing varies by provider, region, context length, caching and promotional offers. Last checked: 12 October 2026.

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-6.1-sol or openai/gpt-6-luna.

Sol is the practical daily OpenAI workhorse at $2 / $10 before long-context uplifts. Luna is the cheaper value tier at $0.10 / $0.50 when the job needs tool use and multi-step follow-through, but isn’t so highly complex that Sol is needed. Both resolve in current OpenClaw catalogs.

If your account still runs openai/gpt-5.4 cleanly, that’s also a viable OpenAI option. Don’t stay on it forever out of habit though when Sol and Luna are available.

Difficult coding and orchestration

Use openai/gpt-6.1-sol when the model has to plan across a repository, preserve constraints and recover from tool errors. OpenAI lists Sol at $2 / $10 before long-context uplifts, so the output rate is not cheap. That’s the point of routing though.

If Sol stalls or starts getting sidetracked with senseless test cases, escalate to openai/gpt-6-astra. Official short-context rates are $10 / $50, but it’s very token efficient and seems to get more work done with less.

Bear in mind though that Anthropic models are heavily censored, ideologically biased, and that Anthropic now permanently watermarks all output as its own, including any of your own work and code that you input. Personally, I do not support Anthropic and therefore none of their models are part of the default stack in this guide.

Browser research and synthesis

xai/grok-4.7 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-6.1-sol 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.

I really 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 V4.1 Flash / Pro
Official rates remain extremely low for a 1M context window at $0.15 / $0.60 off-peak. 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 5.3
Z.ai’s current flagship band is GLM-5.3 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. GLM-5.3 is not in the OpenClaw picker though as of writing; current refs are still 5.2.

In OpenClaw the ref depends on the gateway: venice/zai-org-glm-5-2, nvidia/z-ai/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.

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 3.7 Plus
International Qwen 3.7 Plus is $0.40 / $1.60 up to 256k and $1.20 / $4.80 above that, with a 1M window. Qwen 3.8 Max is the current flagship at $1.65 / $4.951 with no 256k split. It brings the strongest reasoning in the Qwen family while the new Qwen 3.8 Flash is a cheap/fast 3.8 at $0.15 / $0.47. Qwen 3.5 Plus is still available, too, and slightly cheaper on short context if that’s what’s on your route.

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. MiniMax’s official API pricing puts M3 at $0.30 / $1.20 for requests up to 512k input tokens, doubling to $0.60 / $2.40 above that threshold. It has a 1M context window. M2.7 remains the previous tier at $0.30 / $1.20, so it is no longer cheaper than M3 for shorter contexts.

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 GPT-6 Luna, GPT-6.1 Sol or Astra.

OpenClaw Models for cheap recurring jobs

Use openai/gpt-6-luna, Xiaomi’s MiMo V2.6 Flash, or github-copilot/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-6.1-sol, or DeepSeek V4.1 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

You no longer need to edit configuration files manually to switch models in OpenClaw or Hermes. Both offer interactive model selection, making it very straightforward to assign different models to your different workloads.

My recommended setup:

• Primary: GPT-6.1 Sol for everyday agent work.
• Fallback: Grok 4.7 for provider redundancy, with GPT-6 Astra reserved for difficult tasks.
• Utility tasks: GPT-6 Luna for heartbeats, classification, summarization and other lightweight jobs.
• Budget alternative: DeepSeek V4.1 Flash or MiniMax M3 for lower-cost workflows.
• Research: Grok 4.7 when web and social media research matter.

In OpenClaw, configure your preferred models through the model picker or configuration interface. Hermes lets you assign a main model and separate auxiliary models directly from its dashboard.

One important distinction: Fallbacks handle model or provider failures. They don’t automatically route every task to the cheapest suitable model. For cost optimization, assign smaller models explicitly to recurring or lightweight workloads.

For advanced configuration, see the official OpenClaw model documentation and Hermes model configuration guide.

Keeping OpenClaw and Hermes 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

Don’t 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 subscriptions are different products. xAI OAuth and API keys are different routes. MiniMax Coding Plan/OAuth and pay-as-you-go API keys are different routes. Check the current provider docs before you build a production budget around subscription access.

Bottom Line

Start with GPT-6 Luna for routine agent work or GPT-6.1 Sol for more demanding tasks. Escalate difficult coding and recovery to GPT-6 Astra. Use Grok 4.7 when you want a strong non-OpenAI research or coding option. For budget agents, definitely have a look at GLM 5.3, DeepSeek V4.1, Kimi K3, Qwen 3.8, and MiniMax M3 before assuming only flagships can handle your agentic workloads. Keep GPT-5.4 Mini, MiMo V2.6 Flash, or Haiku for bounded utility work.

Use a local Ollama model only when the workload fits a really 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 or Hermes model reference, test the route, and keep your fallbacks simple.

Changelog

• 12 October 2026: Updated model pricing and recommendations to reflect GPT-6.1 Sol, GPT-6 Luna, Grok 4.7, DeepSeek V4.1 Flash, and MiMo V2.6 Flash. Updated Anthropic, Qwen, and MiniMax pricing. Removed deprecated GPT-5.4 Nano and obsolete editorial note with outdated information. Simplified model configuration guidance for OpenClaw and Hermes. Added new pricing sources.
• 06 September 2026: Added GPT-6 Astra as the OpenAI flagship.
• 02 September 2026: Corrected GPT-5.6 Sol, Terra and Luna pricing. Updated to reflect Grok, Fable, Qwen, GLM developments. Sonnet 5 at $2/$10 is now standard rate.
• 27 July 2026: Full rewrite of the original 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 12 October 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. OpenClaw CLI documentation: https://docs.openclaw.ai/cli/models
9. OpenAI API pricing: https://developers.openai.com/api/docs/pricing/
10. Anthropic model overview: https://platform.claude.com/docs/en/about-claude/models/overview
11. Anthropic pricing: https://platform.claude.com/docs/en/about-claude/pricing
12. xAI models and pricing: https://docs.x.ai/docs/models
13. xAI Grok 4.7 model page: https://docs.x.ai/developers/grok-4-7
14. Z.ai model pricing: https://z.ai/model-api
15. DeepSeek models and pricing: https://api-docs.deepseek.com/quick_start/pricing
16. Ollama Llama 3.2 3B page: https://ollama.com/library/llama3.2:3b
17. OpenAI Astra: https://developers.openai.com/api/docs/models/gpt-6-astra
18. Kimi API pricing: https://platform.kimi.ai/docs/pricing/chat
19. MiniMax API pricing: https://platform.minimax.io/docs/pricing/overview
20. Xiaomi MiMo pricing: https://mimo.mi.com/docs/price
21. Alibaba Cloud Model Studio pricing: https://www.alibabacloud.com/help/en/model-studio/model-pricing
22. Hermes model configuration: https://hermes-agent.nousresearch.com/docs/user-guide/configuring-models
23. Live catalog commands: openclaw models list --all --plain (full catalog) and openclaw models list --refresh --provider <id> (refresh and list models for a specific provider, such as openai, anthropic, xai, deepseek, or 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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