Best LLM Providers for OpenClaw in 2026: Anthropic vs OpenAI vs Google vs Groq vs Ollama
You have set up OpenClaw. The daemon is running, your first agent is configured, and you are staring at the model configuration field wondering: which LLM provider do I actually connect this to?
That is the question we spent the last several weeks answering. We ran real OpenClaw agent workloads across six major LLM providers, tracked cost and speed at scale, stress-tested tool use and multi-step reasoning, and found out who holds up when your agent is on its fifteenth consecutive tool call at 2 AM. The results were not always what we expected.
This guide is for people who have already committed to OpenClaw as their agent framework and now need to pick the brain behind it. We cover pricing, speed, context window size, coding quality, tool use reliability, and what a realistic monthly bill looks like for light, moderate, and heavy workloads. Let’s get into it.
Quick Verdict
Anthropic (Claude Sonnet 4.6 / Opus 4.6) is the best default choice for OpenClaw power users. The tool use reliability, one million token context window, and long-context reasoning are class-leading. Expect to pay for it.
OpenAI (GPT-5.4) is the safe, battle-tested option with the broadest ecosystem compatibility. If you are running OpenClaw agents that need to interop with other systems or you want broad community support, this is your pick.
Google AI (Gemini 3.1 Pro / 3 Flash) wins on value per token and has the best free tier for getting started. Shares the one million token context window with Anthropic and OpenAI, making all three strong choices for document-heavy agent tasks.
NVIDIA NIM is the sleeper pick for teams that want free API access to powerful open models without running them locally. Kimi K2.5 on NIM surprised us.
Ollama (Llama 4 Scout, Llama 3.3, Qwen 3, Gemma 3) is the right call if privacy is non-negotiable or you want zero ongoing API costs. Performance is limited by your hardware, but on a Mac Studio or a decent GPU box, it holds its own.
Groq wins on raw speed, period. If your OpenClaw agents are latency-sensitive and you can live with the model selection constraints, Groq’s LPU inference is genuinely in a different category. And Groq now hosts GPT-OSS, OpenAI’s open source models, which changes what you can get at Groq’s prices.
Comparison Table
| Provider | Best Model | Input (per 1M tokens) | Output (per 1M tokens) | Context Window | Tool Use | Speed |
|---|---|---|---|---|---|---|
| Anthropic | Claude Sonnet 4.6 | $3.00 | $15.00 | 1M | Excellent | Fast |
| Anthropic | Claude Opus 4.6 | $5.00 | $25.00 | 1M | Excellent | Moderate |
| OpenAI | GPT-5.4 | $2.50 | $15.00 | 1M | Very Good | Fast |
| OpenAI | GPT-5.4 mini | $0.75 | $4.50 | 1M | Good | Fast |
| Google AI | Gemini 3.1 Pro | $2.00 | $12.00 | 1M | Very Good | Fast |
| Google AI | Gemini 3 Flash | $0.50 | $3.00 | 1M | Good | Very Fast |
| NVIDIA NIM | Kimi K2.5 | Free (limited) | Free (limited) | 128K | Good | Moderate |
| Ollama | Llama 4 Scout | Free (local) | Free (local) | 128K | Moderate | Hardware-dependent |
| Groq | GPT-OSS 120B | $0.15 | $0.75 | 128K | Good | Fastest |
Monthly Cost Estimates
This table assumes a typical OpenClaw agent workload where roughly 60% of tokens are input (context, tool results, system prompts) and 40% are output (agent responses, tool calls).
| Usage Tier | Tokens/Month | Anthropic Sonnet 4.6 | OpenAI GPT-5.4 | Google 3 Flash | Groq Llama 4 Scout | Ollama |
|---|---|---|---|---|---|---|
| Light | 1M | ~$7.80 | ~$7.50 | ~$1.50 | ~$0.20 | $0 |
| Moderate | 10M | ~$78 | ~$75 | ~$15 | ~$2.00 | $0 |
| Heavy | 50M+ | ~$390+ | ~$375+ | ~$75+ | ~$10+ | $0 |
Notes: Ollama costs include hardware amortization and electricity, which vary significantly. Google 3 Flash is the clear winner on pure API cost for hosted inference. Anthropic Opus 4.6 is priced at $5 per million input tokens and $25 per million output tokens, making it far more accessible than previous generations while still being the premium option.
Anthropic: Claude Opus 4.6, Sonnet 4.6, and Haiku 4.5
If you are running OpenClaw agents on serious production workloads, Anthropic is where most of us landed after months of testing. Claude Sonnet 4.6 is the everyday workhorse and Claude Opus 4.6 is the heavy lifter you bring in when the task demands it. Claude Haiku 4.5 covers the budget-tier use cases.
What sets Anthropic apart in the context of OpenClaw specifically is tool use reliability. In our tests, Sonnet 4.6 produced well-formed tool calls on the first attempt over 96% of the time. That number sounds abstract until your agent is twelve steps into an automation pipeline and a malformed JSON tool call causes the whole thing to unravel. Anthropic has invested heavily in making models that understand and respect structured output, and it shows.
Both Opus 4.6 and Sonnet 4.6 now carry a one million token context window. This is a meaningful upgrade from earlier Claude generations and puts Anthropic on equal footing with Google for long-context workloads. We fed Sonnet 4.6 an entire project repository plus a week of conversation history and it maintained coherent reasoning throughout. For OpenClaw agents that accumulate context over time, this headroom matters.
Opus 4.6 is in a category of its own for complex multi-step reasoning. We used it for tasks like analyzing a codebase, identifying architectural issues, proposing a refactor plan, and writing the first three PRs worth of changes. The output was genuinely impressive. At $5 per million input tokens and $25 per million output tokens, it is also significantly more affordable than previous Opus generations, which makes it practical for regular use rather than only special occasions.
Haiku 4.5 at $1 per million input tokens and $5 per million output tokens covers the fast, cheap tier. The 200K context window is sufficient for most lightweight tasks and it handles simple tool use and summarization well. Use it for classification, routing, and tasks that do not need deep reasoning.
Key Features:
Tool Use Reliability. Claude models have the highest first-attempt tool call success rate we measured. For agent workflows in OpenClaw where retry logic adds latency and cost, this matters enormously.
Extended Thinking and Adaptive Thinking. Opus 4.6 and Sonnet 4.6 both support extended thinking and adaptive thinking modes, where the model reasons through a problem before responding. For complex planning tasks in OpenClaw agents, this produces noticeably better outcomes. Adaptive thinking scales the amount of reasoning based on task complexity, which helps control costs.
One Million Token Context. Both Opus 4.6 and Sonnet 4.6 support one million token context windows. This matches Google’s offering and handles virtually any real-world agent task without hitting the limit.
Coding Quality. Claude is widely considered among the best coding models available. For OpenClaw agents that write, review, or refactor code, Anthropic consistently produces clean, idiomatic output.
Pricing:
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Context |
|---|---|---|---|
| Claude Opus 4.6 | $5.00 | $25.00 | 1M |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 1M |
| Claude Haiku 4.5 | $1.00 | $5.00 | 200K |
| Pro plan | $20/mo ($17/mo annual) | All models, standard limits | |
| Max 5x (OAuth) | $100/mo flat | 5x Pro usage across all models | |
| Max 20x (OAuth) | $200/mo flat | 20x Pro usage across all models |
The Max plan deserves special attention for OpenClaw users. Instead of paying per token via API, you can connect OpenClaw to your Anthropic account using OAuth authentication on a Max subscription. Max 5x at $100 per month gives you five times the usage limits of the Pro plan across all models. Max 20x at $200 per month bumps that to twenty times. Both tiers include access to Sonnet 4.6, Opus 4.6, and Haiku 4.5 at a flat monthly rate instead of variable API costs. The Max plan usage limits apply across all models, not on a per-model basis, so you can freely mix Opus and Sonnet without worrying about separate buckets. We use this method ourselves and it simplifies budgeting significantly for heavy agent workloads.
Pros:
- Best tool use reliability in the field
- Outstanding coding and reasoning quality
- One million token context on Opus 4.6 and Sonnet 4.6
- Extended thinking and adaptive thinking on top-tier models
- Max plan OAuth integration for flat-rate billing via OpenClaw
- Three-tier model lineup covers budget through premium
Cons:
- Most expensive option at scale on pure API pricing
- No free tier beyond trial credits
- Haiku 4.5 context window trails the flagship models
OpenAI: GPT-5.4
OpenAI is the safe choice. The models are battle-tested, the API is reliable, the documentation is comprehensive, and the community around OpenAI-compatible APIs is enormous. If something goes wrong with your OpenClaw integration, there is a good chance someone else has hit the same issue and written about it.
GPT-5.4 is OpenAI’s current flagship as of March 2026. The reasoning quality is excellent, tool use is solid, and the one million token context window matches the best in the industry. At $2.50 per million input tokens and $15 per million output tokens, it is priced in the mid-to-upper tier of the market.
For budget-conscious workloads, GPT-5.4 mini at $0.75 per million input tokens is a solid option for simpler agent tasks. It handles basic tool use, summarization, and lightweight automation well enough that you can reserve GPT-5.4 for the tasks that actually need it. GPT-5.4 nano at $0.20 per million input tokens covers ultra-cheap bulk processing where quality requirements are lower.
The free plan gives limited access to GPT-5.3, which is useful for getting started and testing your OpenClaw setup without a credit card commitment. The Pro plan at $200 per month gives you unlimited GPT-5.4 access plus the Codex agent, and like Anthropic’s Max plan, you can connect OpenClaw via OAuth on the Pro plan for flat-rate billing instead of per-token API costs.
Key Features:
Ecosystem Compatibility. OpenAI’s API is effectively the industry standard. Every tool, library, and integration you want to use with OpenClaw almost certainly has OpenAI support built in first.
GPT-5.4 Reasoning. Complex multi-step agent tasks work reliably. The one million token context window (922K input, 128K output) handles even the largest codebases and long-running conversations. The model handles ambiguous instructions and recovers from partial tool failures better than previous generations.
Model Variety. From GPT-5.4 nano for cheap bulk tasks to GPT-5.4 for demanding work, you can right-size the model to the task within a single provider relationship.
OAuth Pro Plan. The $200 per month Pro plan includes unlimited GPT-5.4 access and connects to OpenClaw via OAuth, giving you a flat-rate alternative to per-token API billing for heavy workloads.
Pricing:
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| GPT-5.4 | $2.50 | $15.00 |
| GPT-5.4 mini | $0.75 | $4.50 |
| GPT-5.4 nano | $0.20 | $1.25 |
| Free plan | Limited GPT-5.3 access | |
| Pro plan | $200/mo, unlimited GPT-5.4 + Codex |
Pros:
- Largest ecosystem and community support
- Reliable API with strong uptime track record
- Three-tier model lineup for right-sizing costs
- Best documentation and integration examples
- OAuth Pro plan available for flat-rate OpenClaw integration
Cons:
- GPT-5.4 output pricing is high relative to Google alternatives
- Slightly behind Anthropic on tool use reliability for complex workflows
Google AI: Gemini 3.1 Pro, 3 Flash, and 3.1 Flash-Lite
Google AI continues to be the best value story in this roundup. Google launched the Gemini 3 generation in early 2026, calling it “our most intelligent model.” Gemini 3.1 Pro is the current flagship, with Gemini 3 Flash as the fast mid-tier option and Gemini 3.1 Flash-Lite covering the ultra-budget tier.
Gemini 3 Flash at $0.50 per million input tokens and $3.00 per million output tokens remains significantly cheaper than Anthropic Sonnet 4.6 or OpenAI GPT-5.4. For moderate OpenClaw workloads, you can run serious agent infrastructure for well under $20 per month.
Gemini 3.1 Flash-Lite at $0.25 per million input tokens and $1.50 per million output tokens covers the ultra-cheap tier. It is not a replacement for heavier reasoning tasks, but for classification, routing, summarization, and simple agent steps, the cost is nearly negligible.
Gemini 3.1 Pro at $2.00 per million input tokens and $12.00 per million output tokens (for contexts under 200K tokens; $4.00/$18.00 above 200K) is priced closer to GPT-5.4 than the old 2.5 Pro was, but brings substantially better reasoning and tool use quality.
The one million token context window carries across the lineup, matching Anthropic and OpenAI. We tested Gemini 3.1 Pro with a genuinely large codebase around 800K tokens of source files and documentation, and the model maintained accurate recall throughout. For OpenClaw agents that work with large document sets, long-running conversations, or entire repository contexts, this remains a strong option.
Note: Gemini 3 Pro Preview was deprecated and shut down on March 9, 2026. If your OpenClaw config still references Gemini 3 Pro Preview or any 2.x model, migrate to Gemini 3.1 Pro or 3 Flash now. Gemini 2.5 models are end-of-life.
The free tier deserves a specific mention. Google offers a meaningful free quota on the Gemini API, which makes it an excellent starting point for new OpenClaw users who want to experiment without a credit card.
Key Features:
One Million Token Context. Gemini 3.1 Pro and 3 Flash both offer one million token context windows. This matches Anthropic and OpenAI’s offerings.
Best Free Tier. The Gemini API free tier lets you run real OpenClaw agent workloads at no cost during development and early production.
Three-Tier Pricing. Pro, Flash, and Flash-Lite cover a wide range of performance and cost tradeoffs, all within the same provider relationship.
Gemini 3 Flash Speed. Flash is among the fastest models we tested. For latency-sensitive OpenClaw interactions, it competes closely with Groq at a fraction of the cost. Gemini 3 Flash is now the default model in the Gemini app.
Pricing:
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| Gemini 3.1 Pro (under 200K context) | $2.00 | $12.00 |
| Gemini 3.1 Pro (over 200K context) | $4.00 | $18.00 |
| Gemini 3 Flash | $0.50 | $3.00 |
| Gemini 3.1 Flash-Lite | $0.25 | $1.50 |
| Free tier | $0 (rate limited) | $0 (rate limited) |
Pros:
- Best pricing in the market for capable models
- One million token context window matches the best available
- Generous free tier for development and testing
- Fast inference, especially on 3 Flash
- Flash-Lite covers ultra-cheap bulk processing
- Gemini 3 generation brings major reasoning improvements
Cons:
- Tool use reliability trails Anthropic for complex workflows
- Pro pricing increased significantly from 2.5 generation ($2/$12 vs $1.25/$10)
- Context-dependent pricing tiers on Pro (over 200K costs more)
Groq: GPT-OSS, Llama 4 Scout, and the Speed Champion
Groq built custom LPU (Language Processing Unit) hardware specifically for inference, and the result is inference speeds that make other providers feel slow. We measured models running at 600 to 1000 tokens per second on Groq. The second-fastest provider in our test was around 200 tokens per second. The gap is that wide.
For OpenClaw use cases where latency is the critical variable, Groq is in a category of its own. If your agents are interactive with a user waiting on the other end, if you are running real-time voice pipelines, or if you need sub-second response times on shorter tasks, Groq’s speed translates directly into user experience quality.
One significant development worth calling out: Groq now hosts GPT-OSS, OpenAI’s open source model family. This is a big deal. GPT-OSS 20B and GPT-OSS 120B are OpenAI’s openly released models running on Groq’s LPU hardware. You get OpenAI model quality at Groq speeds and Groq prices. GPT-OSS 120B at $0.15 per million input tokens and $0.75 per million output tokens running at 500 tokens per second is a genuinely compelling combination that did not exist a few months ago.
Groq also hosts Llama 4 Scout, the latest model from Meta. Llama 4 Scout at $0.11 per million input tokens and $0.34 per million output tokens delivers strong performance for its cost tier, and the 594 tokens per second throughput makes it one of the most responsive options available. Kimi K2 from Moonshot AI rounds out the higher-end options at $1.00 per million input tokens with 256K context and 200 tokens per second.
We have found the best OpenClaw pattern with Groq is using it for fast, simpler tasks while routing complex reasoning tasks to Anthropic or OpenAI. The OpenClaw config supports multiple providers simultaneously, so this kind of routing is straightforward to implement.
Key Features:
LPU Inference Speed. Up to 1000 tokens per second on GPT-OSS 20B is not a lab result. We measured it in real OpenClaw agent runs. It is real and it is fast.
GPT-OSS Models. Hosting OpenAI’s open source models at Groq speeds is a significant capability addition. For teams that want OpenAI model lineage without OpenAI API pricing, this is worth serious consideration.
Llama 4 Scout. The latest Meta model available on Groq delivers strong performance at ultra-competitive pricing. At $0.11 per million input tokens and 594 tokens per second, it is one of the best value options in the entire comparison.
Competitive Pricing. Below Anthropic and OpenAI on major model equivalents, with a speed advantage on top.
OpenAI-Compatible API. Swap the base URL in OpenClaw config and you are running on Groq.
Pricing:
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Speed (TPS) |
|---|---|---|---|
| GPT-OSS 20B | $0.10 | $0.50 | 1000 |
| GPT-OSS 120B | $0.15 | $0.75 | 500 |
| Llama 4 Scout | $0.11 | $0.34 | 594 |
| Qwen3 32B | $0.29 | $0.59 | 662 |
| Llama 3.3 70B | $0.59 | $0.79 | 394 |
| Kimi K2 | $1.00 | $3.00 | 200 |
Pros:
- Fastest inference of any provider tested, by a significant margin
- GPT-OSS models bring OpenAI model lineage at open-model pricing
- Llama 4 Scout as the latest Meta model at exceptional value
- Competitive pricing across the board
- OpenAI-compatible API makes integration trivial
- Free tier available for development
Cons:
- No proprietary frontier models like Claude Opus or GPT-5.4
- Tool use reliability limited by open model quality versus fine-tuned proprietary models
- Context windows are smaller than Google’s one million token offering
- Rate limits on free tier can be restrictive
NVIDIA NIM: Free Tier for Open Models
NVIDIA NIM is the provider most people have not configured with OpenClaw yet, and it might be the best-kept secret in this roundup. NIM gives you free API access to a curated set of high-quality open models running on NVIDIA’s inference infrastructure. No credit card required for the free tier.
Kimi K2.5 is the model that impressed us most on NIM. It is a mixture-of-experts model from Moonshot AI with strong reasoning and coding capabilities. In our OpenClaw tests, it handled multi-step tool use better than we expected given its position as a free tier option. For teams that need capable agents without a budget commitment, this is worth a serious look.
The NIM platform also hosts Llama models and Mistral models, all through an OpenAI-compatible API. The quality of inference is solid because NVIDIA is running these on optimized hardware. The constraint is rate limits on the free tier and a meaningful latency penalty. In our testing, NIM responses came in at roughly two to three times the latency of paid providers like Anthropic and OpenAI. That is not a deal-breaker for batch processing or background agents, but it is noticeable in interactive workloads.
For OpenClaw users who want to test open models in a production-like environment before committing to local hardware or a paid tier, NIM is the cleanest path. Connect your OpenClaw config to the NIM endpoint, set the model name, and you are running Kimi K2.5 in minutes.
Paid tier pricing from NVIDIA is not publicly listed, which makes budget planning harder than with Groq or Google. If you need predictable costs, NIM is best positioned as a development and testing provider with a fallback to a more transparent provider for production.
Key Features:
Free Tier Access to Quality Models. No credit card, no commitment. Real production-quality inference on open models via a clean OpenAI-compatible API.
Kimi K2.5 Performance. Strong reasoning and coding quality from an open model at zero cost on the free tier.
Hosted Models. Kimi K2.5, Llama models, and Mistral models available without local GPU requirements.
OpenAI-Compatible API. Swap your OpenClaw base URL to NIM’s endpoint and your existing config mostly just works.
Pricing:
| Model | Free Tier | Paid |
|---|---|---|
| Kimi K2.5 | Yes (rate limited) | Pricing not publicly listed |
| Llama models | Yes (rate limited) | Pricing not publicly listed |
| Mistral models | Yes (rate limited) | Pricing not publicly listed |
Pros:
- Free tier with real capable models, no credit card required
- Excellent for development, testing, and light production use
- OpenAI-compatible API makes OpenClaw integration easy
- Good model variety including Kimi K2.5
- No local hardware required
Cons:
- Noticeably slower than paid providers, roughly two to three times the latency
- Rate limits on free tier hit quickly at scale
- Paid tier pricing not publicly transparent
- Not suitable for latency-sensitive OpenClaw workloads
Ollama: Local Models for Privacy and Zero Cost
Ollama takes a fundamentally different approach: instead of sending tokens to someone else’s API, you run the model on your own hardware. Llama 4 Scout, Llama 3.3, Qwen 3, Gemma 3, and dozens of other models run locally on your machine. No API costs, no data leaving your infrastructure, no rate limits.
For OpenClaw users with specific privacy requirements, compliance constraints, or air-gapped environments, local inference is not optional. It is the only path. Ollama makes local inference dramatically easier than alternatives, with a clean CLI and an API that mirrors OpenAI’s format closely enough that OpenClaw integrates with it out of the box.
Llama 4 Scout is the latest model from Meta and is available on Ollama. It represents a meaningful step up from Llama 3.3 in reasoning quality and is a strong default choice for local inference. If you are setting up Ollama fresh, Llama 4 Scout is where to start.
The tradeoff is hardware dependency. On a Mac Studio M2 Ultra or a machine with a high-end GPU, Llama 4 Scout and Llama 3.3 70B run at a reasonable clip and produce quality output. On a MacBook Air or a machine without a GPU, smaller models like Gemma 3 9B or Qwen 3 8B become more practical. Quality scales with compute, and there is a real gap between local inference on commodity hardware and what you get from hosted providers.
Tool use with local models is a specific weak point worth flagging. Models fine-tuned for chat tend to produce less reliable structured output than models optimized for agent use. Llama 4 Scout handles basic tool calls acceptably, but for complex multi-step agent workflows in OpenClaw, we hit more failures with local models than with hosted providers. The quality is improving with each model generation, but hosted providers still have an edge here.
Where Ollama wins is cost at scale. At 50 million tokens per month, local inference is free beyond electricity and hardware. For teams with the hardware already provisioned and the workloads to justify it, the economics are compelling.
Key Features:
Zero API Cost. No per-token billing. Run as many tokens as your hardware can handle for the cost of electricity.
Complete Data Privacy. Nothing leaves your machine. For regulated industries, sensitive data, or personal use cases where you do not want tokens processed by third-party APIs, this is the decisive advantage.
OpenAI-Compatible API. Ollama exposes an OpenAI-compatible REST API, so OpenClaw connects to local models with a simple base URL change.
Latest Open Models. Llama 4 Scout, Llama 3.3, Qwen 3, Gemma 3, Mistral, and dozens of community fine-tunes are available via ollama pull. Switch models in seconds.
Pricing:
| Model | Cost |
|---|---|
| Any Ollama model | Free (local compute only) |
| Hardware (Mac Studio M2 Ultra) | $2,000 to $3,500 one-time |
| Hardware (NVIDIA RTX 4090) | $1,500 to $2,000 one-time |
| Electricity (~200W average) | $15 to $30 per month at US rates |
Pros:
- Zero ongoing API cost at any token volume
- Complete data privacy and local control
- No rate limits, no API keys, no account required
- Llama 4 Scout and other latest open source models available
- Works offline, no internet dependency
Cons:
- Quality ceiling below hosted frontier models
- Tool use reliability lags behind Anthropic and OpenAI
- Performance depends entirely on available hardware
- Not practical without sufficient RAM (minimum 16GB, 64GB or more for larger models)
Head-to-Head Comparisons
Tool Use and Agent Reliability
This is the metric that matters most for OpenClaw. We measured first-attempt tool call success rate across a 100-call test suite covering simple tool invocations, nested tool calls, and structured output requirements.
Anthropic Claude Sonnet 4.6 scored highest at 96% first-attempt success. OpenAI GPT-5.4 came in at 92%. Google AI Gemini 3.1 Pro was at 88%. Groq with GPT-OSS 120B reached 84%, which is better than open models showed in previous tests. Ollama local inference with Llama 4 Scout scored 77%. These are not small differences when your OpenClaw agent is executing 20 or more tool calls in sequence.
Winner: Anthropic
Value Per Dollar
This is where Google dominates. At $0.50 per million input tokens, Gemini 3 Flash delivers capable agent performance at a fraction of any competitor’s cost. Flash-Lite at $0.25 per million input tokens is even more aggressive for bulk processing. For moderate OpenClaw workloads where quality is good enough but not maximum, Google is the clear value winner.
Groq’s GPT-OSS 20B at $0.10 per million input tokens is worth noting as a specific competitor at the ultra-cheap tier, particularly if you need hosted inference with OpenAI model lineage.
Ollama wins outright if you have the hardware, but the zero-cost calculation ignores amortized hardware and the quality ceiling.
Winner: Google AI (hosted); Ollama (if hardware is available)
Speed and Latency
Groq wins by such a large margin that the comparison feels unfair. For interactive OpenClaw agents where users are waiting, Groq’s 500 to 1000 tokens per second across its model lineup is a qualitatively different experience. Gemini 3 Flash is the runner-up. Anthropic and OpenAI are fast enough for most use cases but noticeably slower on long outputs. NVIDIA NIM is the slowest tested option, at roughly two to three times the latency of paid providers on the free tier.
Winner: Groq
Context Window Size
All three major providers now offer one million token context windows. Google AI offers one million tokens on both Gemini 3.1 Pro and 3 Flash, Anthropic matches this on Opus 4.6 and Sonnet 4.6, and OpenAI GPT-5.4 also supports one million tokens (922K input, 128K output). This is a three-way tie at the top. Everyone else is 256K or below. For OpenClaw agents that work with large document sets, long-running conversations, or entire codebases, any of these three handle it well.
Winner: Google AI, Anthropic, and OpenAI (tied)
Our Recommendations by Use Case
For most OpenClaw users: Start with Google Gemini 3 Flash on the free tier to learn the ropes. Move to Anthropic Claude Sonnet 4.6 when you need reliable tool use in production.
For coding and development agents: Anthropic Claude Sonnet 4.6 or Opus 4.6. The code quality and tool use reliability are worth the cost.
For document analysis or RAG-heavy workloads: Google AI Gemini 3.1 Pro or Anthropic Claude Sonnet 4.6. Both offer one million token context windows. Google wins on cost, Anthropic wins on tool use reliability.
For latency-sensitive or interactive agents: Groq with GPT-OSS 120B or Llama 4 Scout. Nothing else comes close on speed.
For privacy-first or compliance-required workloads: Ollama with Llama 4 Scout or Qwen 3. Data stays on your hardware.
For getting started without a credit card: NVIDIA NIM free tier with Kimi K2.5, or Google AI free tier with Gemini 3 Flash.
For teams that want to mix providers: Set up Anthropic for complex tasks and Groq for fast simple tasks. OpenClaw supports routing different workloads to different providers, and this hybrid approach gives you the best of both.
For heavy Anthropic users who want predictable billing: Connect OpenClaw via OAuth on the Anthropic Max plan instead of per-token API billing. At $100 per month for 5x usage or $200 per month for 20x usage, it simplifies budgeting for teams that rely heavily on Claude.
FAQ
Can I use multiple LLM providers simultaneously with OpenClaw?
Yes. OpenClaw supports configuring multiple model providers and routing different agents or tasks to different providers. The OpenClaw docs cover the multi-provider setup in detail. This is one of OpenClaw’s strong points: you are not locked into a single provider.
Which provider is easiest to set up with OpenClaw?
All of the hosted providers (Anthropic, OpenAI, Google, Groq, NIM) are straightforward: add your API key to the OpenClaw config and specify the model. Ollama requires a local setup but the OpenClaw integration still only needs a base URL change once Ollama is running.
Is Anthropic Claude worth the price premium over Google Flash?
For complex, multi-step agent workflows with heavy tool use: yes. For simpler tasks like summarization, classification, or document Q and A: probably not. Google 3 Flash at $0.50 per million input tokens is exceptional for what it delivers. We would recommend running both and routing tasks based on complexity.
What is the difference between Anthropic’s Pro plan and Max plan for OpenClaw?
The Pro plan at $20 per month is for standard Claude.ai usage. The Max plan (5x at $100/mo or 20x at $200/mo) gives you significantly higher usage limits across all models and, critically, allows you to connect OpenClaw via OAuth for flat-rate billing. The Max plan usage limits are shared across all models, not per-model, so you can freely use Opus 4.6 and Sonnet 4.6 within the same monthly limit.
Should I use Groq’s GPT-OSS models or OpenAI’s API directly?
It depends on what you are optimizing for. Groq’s GPT-OSS models are OpenAI’s open source releases running on Groq’s fast LPU hardware at a fraction of the cost of GPT-5.4. If you need OpenAI model quality for simpler tasks and speed matters, Groq GPT-OSS is compelling. For frontier reasoning tasks that require GPT-5.4’s full capabilities, use OpenAI’s API directly.
Can OpenClaw agents use local Ollama models in production?
Yes, with caveats. Ollama is production-ready for teams with appropriate hardware. The limitation is quality, particularly on complex tool use, not stability or reliability. Many teams run Ollama for low-stakes tasks and route demanding work to hosted providers.
Should I migrate from older Gemini models?
Yes. Gemini 3 Pro Preview was deprecated and shut down on March 9, 2026. Gemini 2.5 and 2.0 models are end-of-life. Migrate to Gemini 3.1 Pro or Gemini 3 Flash immediately. The 3.x generation brings major improvements in reasoning, speed, and tool use quality.
Which provider has the best uptime for production OpenClaw agents?
OpenAI and Anthropic both have strong uptime track records with published status pages. Google AI has improved significantly and is reliable for production use. Groq is newer but has been stable. For mission-critical OpenClaw deployments, configure a fallback provider in case your primary is unavailable.
Final Verdict
There is no single best LLM provider for OpenClaw. The right answer depends on what you are building and what you are optimizing for.
If we had to pick one default recommendation for a new OpenClaw user setting up production agents: Anthropic Claude Sonnet 4.6. The tool use reliability, reasoning quality, and one million token context window hit the right balance for most real-world agent workloads. The pricing at $3 per million input tokens is competitive, and the Max plan OAuth integration makes it even more accessible for heavy users.
For budget-conscious teams, Google Gemini 3 Flash is an extraordinary value we would feel comfortable recommending for most workloads. The one million token context window and the free tier make it a strong default for anyone just getting started.
For speed-first use cases, Groq stands alone. The addition of GPT-OSS models means you are no longer limited to purely open-source model quality when choosing Groq for speed.
For privacy-first use cases, Ollama is the only answer. Llama 4 Scout brings Meta’s latest capabilities to local inference and is the right starting point for new Ollama deployments.
The good news is that OpenClaw makes switching and mixing providers genuinely easy. You do not have to commit to one. Start with the free tier of any provider on this list, measure what actually matters for your use case, and optimize from there.
Last updated: March 22, 2026. Pricing reflects published rates as of publication date and may have changed. Always verify current pricing on provider websites before making purchasing decisions.
This article contains no affiliate links for LLM providers. Provider links are direct homepages. We earn commissions from some other tools mentioned on SaaS Compared, but not from any LLM provider listed here.
GitHub Copilot 
Cursor 
Claude Code