Quick Answer: Claude Sonnet 4.6 ($3/$15 per 1M tokens) is the best all-around model for OpenClaw — #1 on PinchBench (86.9%), top-tier tool calling, and 1M context window. On a budget? DeepSeek V3.2 at $0.02/session scores a surprising 81.9% on PinchBench. For maximum horsepower, Claude Opus 4.6 (86.3%) leads every agentic coding benchmark.
OpenClaw connects to any LLM provider — Claude, GPT, Gemini, DeepSeek, Llama, or a local model running on your own hardware. That flexibility is one of the reasons it has 250K+ GitHub stars. But it also means you face a question on day one: which model should I actually pick?
The answer depends on what you use OpenClaw for. A model that's perfect for writing emails at $0.02/session will choke on a 15-step file-processing pipeline. A model that aces coding benchmarks might cost 50x more than you need for daily triage.
We tested five model families on real OpenClaw workflows — tool calling, multi-step reasoning, code generation, and long-context tasks — and combined our results with the latest public benchmarks. Here's what we found.
OpenClaw supports any OpenAI-compatible API endpoint. Most users connect through OpenRouter for access to every model with one API key, or directly to Anthropic, OpenAI, or Google's APIs. See the OpenClaw tutorial for setup details.
The Quick Comparison
If you want the bottom line before the deep dive, here it is:
| Model | Best For | Input / Output (per 1M tokens) | Context | PinchBench | Overall |
|---|---|---|---|---|---|
| Claude Sonnet 4.6 | Best all-around | $3 / $15 | 1M | 86.9% | Best balance |
| Claude Opus 4.6 | Maximum capability | $5 / $25 | 200K | 86.3% | Best quality |
| GPT-5 | Cost-effective power | $1.25 / $10 | 400K | 86.0% | Best value/power |
| GPT-4.1 | Enterprise tool use | $2 / $8 | 1M | — | Best action completion |
| Gemini 2.5 Pro | Budget + big context | $1.25 / $10 | 1M | 65.5% | Best context/dollar |
| Gemini 2.5 Flash | High-volume simple tasks | $0.15 / $0.60 | 1M | 65.3% | Cheapest capable |
| DeepSeek V3.2 | Budget daily driver | $0.28 / $1.10 | 128K | 81.9% | Cheapest smart |
| Llama 4 Maverick | Self-hosted / privacy | $0.11 / $0.34 (Groq) | 1M | — | Best open-source |
What Makes a Model Good for OpenClaw?
Not every LLM benchmark matters for a personal AI agent. OpenClaw's strengths — tool calling, persistent memory, multi-step workflows — put specific demands on the model that generic chatbot benchmarks don't capture.
Tool calling reliability
OpenClaw's 30+ built-in skills (web search, file management, shell commands, scheduling) all depend on the model generating correct function calls. A model that hallucinates parameters or picks the wrong tool wastes tokens and breaks workflows. Claude Sonnet 4.6 scores 71.6 on MCPAgentBench (the MCP-focused tool-use benchmark), the highest of any model tested. GPT-4.1 leads on the Galileo Agent Leaderboard with 62% action completion. On PinchBench — an OpenClaw-specific benchmark that grades models on real agent tasks — Claude Sonnet 4.6 leads at 86.9% success rate, with Claude Opus 4.6 (86.3%) and GPT-5.4 (86.0%) close behind.
Context window
OpenClaw accumulates context across a session — conversation history, tool results, memory files, skill definitions. Long sessions can easily burn through 50K–100K tokens. Models with larger context windows (Gemini's 1M, Claude Sonnet's 1M) handle marathon sessions without truncation. But raw window size isn't everything: research shows performance drops 50%+ beyond 100K tokens even in models advertising 1M.
Cost per session
For a personal agent you chat with daily, cost adds up fast. At DeepSeek rates ($0.28/1M input), a typical 10-message session costs ~$0.02. At Claude Opus rates ($5/1M input), the same session costs ~$0.35. Over a month of daily use, that's the difference between $0.60 and $10.50.
Reasoning depth
When you ask OpenClaw to "reorganize my project files by topic" or "draft a PR that fixes all the linting errors," the model needs to plan multiple steps, recover from errors, and adapt. Models with stronger reasoning capabilities (Claude Opus, GPT-5, DeepSeek V3.2-Speciale) handle these multi-hop tasks significantly better.
Claude (Anthropic) — Best for Tool Calling & Code
Anthropic's Claude family is the top performer on agentic benchmarks in 2026, and it's the model most OpenClaw power users reach for.
Claude is the model to beat for OpenClaw. Sonnet 4.6 at $3/$15 is the sweet spot — 1M context, top-tier tool calling, and the highest score on PinchBench (86.9%), the OpenClaw-specific benchmark. Opus 4.6 is close behind at 86.3% and remains the premium option when you need the absolute best output quality.
- #1 on PinchBench OpenClaw benchmark (86.9% — Sonnet 4.6)
- #1 on SWE-bench Verified (80.9% — Opus 4.5)
- Highest MCP tool-calling scores (71.6 — Sonnet 4.6)
- 1M context window on Sonnet 4.6
- 90% discount with prompt caching
- Best autonomous coding of any model
- Opus 4.6 limited to 200K context
- Most expensive premium tier ($5/$25)
- No free tier
When to use Claude with OpenClaw:
- Complex multi-step workflows (file processing, code refactoring, research pipelines)
- Any task that chains 5+ tool calls in sequence
- Code generation and review
- Tasks where reliability matters more than cost
OpenClaw config:
provider: anthropic
model: claude-sonnet-4-6 # or claude-opus-4-6 for max capability
Enable prompt caching on Anthropic's API to save 90% on repeated system prompts and skill definitions. OpenClaw's system prompt is largely static, so caching drops your effective input cost from $3 to ~$0.30 per million tokens. See our guide to prompt caching for AI agents.
GPT (OpenAI) — Best Value for Power Users
OpenAI's lineup is the broadest in the industry, from GPT-5 Nano at $0.05/1M to the o3 reasoning models. For OpenClaw, GPT-5 and GPT-4.1 are the most interesting options.
GPT-5 is the best price-to-performance model in the mid-tier. At $1.25/$10, it's cheaper than Claude Sonnet while still handling most OpenClaw workflows well. GPT-5.4 scores 86.0% on PinchBench — essentially matching Claude on real OpenClaw tasks. GPT-4.1 is the enterprise pick — excellent tool use, 1M context, and strong action completion.
- GPT-5.4 at 86.0% on PinchBench — near-Claude on OpenClaw tasks
- GPT-5 at $1.25/$10 — strong and affordable
- GPT-4.1 leads Galileo Action Completion (62%)
- 400K context (GPT-5) to 1M (GPT-4.1)
- Largest third-party tool ecosystem
- 50% off with Batch API
- GPT-5 underperformed on some agentic benchmarks (GAE: 0.40)
- No prompt caching as aggressive as Anthropic's 90%
- Function calling format more verbose than Claude's
When to use GPT with OpenClaw:
- General-purpose daily agent tasks at a reasonable cost
- Workflows that depend on OpenAI's function calling ecosystem
- Tasks that need 400K+ context without paying Claude Opus prices
- When you already have OpenAI API credits
OpenClaw config:
provider: openai
model: gpt-5 # or gpt-4.1 for enterprise-grade tool use
Gemini (Google) — Best for Context & Budget
Google's Gemini family offers the most aggressive pricing and the largest practical context windows. Gemini 2.5 Flash is the cheapest model that can still handle agent-level tasks.
Gemini is a paradox for OpenClaw. It's brilliant at picking the right tool (94% accuracy) but struggles to complete complex action sequences (38%). That makes it perfect for simple, high-volume tasks but frustrating for anything requiring 5+ chained steps.
- 1M context on all Pro/Flash models
- Gemini 2.5 Flash: $0.15/$0.60 — absurdly cheap
- 94% Tool Selection Quality (Galileo) — picks the right tool
- Free tier available
- Deep Google ecosystem integration
- 65.5% on PinchBench (2.5 Pro) — weakest of the major providers on OpenClaw tasks
- Only 38% Action Completion (Galileo) — starts tasks, doesn't finish them
- Thinking mode doubles output cost ($3.50/M vs $0.60)
- Long-context surcharge above 200K tokens
- Less reliable at complex multi-step tool chains
When to use Gemini with OpenClaw:
- Simple daily queries (weather, summaries, quick lookups)
- Tasks that need huge context (processing long documents, analyzing codebases)
- When cost is the primary constraint
- As the "cheap model" in a model routing setup
OpenClaw config:
provider: google
model: gemini-2.5-pro # or gemini-2.5-flash for maximum savings
DeepSeek — Best for Budget Daily Driving
DeepSeek V3.2 is 20–50x cheaper than comparable models from OpenAI or Anthropic. For the 80% of OpenClaw tasks that don't require frontier intelligence, it's an extraordinary value.
DeepSeek is the model that makes OpenClaw feel free. At $0.02 per session, you stop thinking about cost entirely. With 81.9% on PinchBench, V3.2 outperforms models costing 10x more — though function calling occasionally fails on complex tool chains and needs more retries than Claude or GPT.
- 81.9% on PinchBench — punches way above its price class
- $0.28/$1.10 per 1M tokens — ~$0.02/session
- V3.2-Speciale matches GPT-5 on some reasoning tasks
- Unified chat + reasoning in one model
- Strong at coding and general knowledge
- Function calling breaks under high concurrency
- 128K context limit (API) — smallest of the five
- Verbose outputs in agentic loops (wastes tokens)
- Slower inference than cloud-native providers
When to use DeepSeek with OpenClaw:
- Daily casual usage (questions, summaries, quick tasks)
- When you're running OpenClaw on a $5/month VPS and want to keep total cost under $1/month
- Simple automation (scheduled messages, basic web lookups)
- As the default model with escalation to Claude for complex tasks
OpenClaw config:
provider: openrouter
model: deepseek/deepseek-chat # V3.2, auto-routes to latest
DeepSeek's function calling has known reliability issues when multiple tool calls happen simultaneously. If your OpenClaw workflow triggers parallel tool use, consider using Claude or GPT for those specific skills.
Llama 4 (Meta) — Best for Self-Hosting & Privacy
Meta's Llama 4 is the leading open-source option. The Scout model offers an industry-record 10M token context window, and Maverick outperforms GPT-4o on multiple benchmarks — all with free weights.
Llama 4 is the privacy pick. If your OpenClaw agent handles sensitive data (medical records, financial info, proprietary code), running Llama locally means nothing ever leaves your network. Via Groq, it's also nearly as cheap as DeepSeek with better reliability.
- Free weights — zero model cost if self-hosted
- Scout: 10M context (largest production model)
- Maverick outperforms GPT-4o on many benchmarks
- Groq hosting: $0.11/$0.34 — nearly free
- Full privacy — nothing leaves your machine
- Self-hosting requires GPU infrastructure ($1.5K–$5K/month)
- Less mature tool-calling fine-tuning than Claude/GPT
- Groq free tier has rate limits
- Quantized versions lose quality on complex reasoning
When to use Llama with OpenClaw:
- Privacy-critical workflows (sensitive data must stay local)
- When you have existing GPU infrastructure
- Via Groq for a nearly-free alternative to DeepSeek
- Experimental / hobbyist setups where you want full control
OpenClaw config:
# Via Groq (cloud, fast, cheap)
provider: openrouter
model: meta-llama/llama-4-maverick
# Via local Ollama (self-hosted, private)
provider: ollama
model: llama4-maverick
Head-to-Head: Benchmarks That Matter for OpenClaw
Generic LLM benchmarks (MMLU, HumanEval) don't capture what matters for a personal AI agent. PinchBench is the first benchmark purpose-built for OpenClaw — it runs standardized agent tasks and grades via automated checks plus LLM judge. Combined with other agentic benchmarks, here's the full picture:
| Benchmark | What It Measures | Claude | GPT | Gemini | DeepSeek | Llama 4 |
|---|---|---|---|---|---|---|
| PinchBench | OpenClaw agent tasks | 86.9% (Sonnet 4.6) | 86.0% (GPT-5.4) | 65.5% (2.5 Pro) | 81.9% (V3.2) | — |
| SWE-bench Verified | Autonomous code fixes | 80.9% (Opus) | 74.9% (GPT-5) | 80.6% (3.1 Pro) | 67.8% | ~70% (est.) |
| MCPAgentBench | MCP tool use | 71.6 (Sonnet) | 66.0 (o3) | — | — | — |
| Galileo Action Completion | End-to-end task finish | — | 62% (GPT-4.1) | 38% (Flash) | — | — |
| Galileo Tool Selection | Picks the right tool | — | — | 94% (Flash) | — | — |
| GAE Success Rate | General agent tasks | 0.66 (Opus) | 0.40 (GPT-5.2) | 0.60 (Gemini 3) | — | — |
| GPQA Diamond | Scientific reasoning | 87% (Opus) | 92.4% (GPT-5.2) | — | — | — |
Benchmarks measure averages across thousands of tasks. Your specific OpenClaw workflows may favor different models. The only way to know for sure is to test your actual use cases.
The Smart Setup: Model Routing
The best OpenClaw configuration isn't one model — it's two or three. OpenClaw's multi-model support lets you assign different models to different scenarios:
| Tier | Model | Use Case | Cost/Session |
|---|---|---|---|
| Default | DeepSeek V3.2 | Daily chat, simple queries, scheduled tasks | ~$0.02 |
| Escalation | Claude Sonnet 4.6 | Complex tool chains, code generation, research | ~$0.15 |
| Heavy lift | Claude Opus 4.6 | Multi-file refactoring, long analysis, critical tasks | ~$0.35 |
This routing pattern cuts costs by 60–80% compared to running Claude for everything, while maintaining top-tier quality for the tasks that need it. Read more about AI agent cost optimization for detailed strategies.
Pricing Deep Dive
Real costs depend on session length, tool verbosity, and how much context OpenClaw carries. Here's what typical sessions actually cost at March 2026 prices:
| Model | Input (per 1M) | Output (per 1M) | Typical Session | Monthly (daily use) |
|---|---|---|---|---|
| Claude Opus 4.6 | $5.00 | $25.00 | ~$0.35 | ~$10.50 |
| Claude Sonnet 4.6 | $3.00 | $15.00 | ~$0.15 | ~$4.50 |
| GPT-5 | $1.25 | $10.00 | ~$0.08 | ~$2.40 |
| GPT-4.1 | $2.00 | $8.00 | ~$0.10 | ~$3.00 |
| Gemini 2.5 Pro | $1.25 | $10.00 | ~$0.08 | ~$2.40 |
| Gemini 2.5 Flash | $0.15 | $0.60 | ~$0.01 | ~$0.30 |
| DeepSeek V3.2 | $0.28 | $1.10 | ~$0.02 | ~$0.60 |
| Llama 4 (Groq) | $0.11 | $0.34 | ~$0.01 | ~$0.30 |
Anthropic's prompt caching drops cached input reads to just 10% of the base price. Since OpenClaw's system prompt and skill definitions are mostly static, your effective Claude Sonnet cost drops from $3 to ~$0.30 per million input tokens. That brings typical sessions from $0.15 down to ~$0.05.
Our Recommendations by Use Case
"I just want it to work — what's the best single model?" Claude Sonnet 4.6. Best tool calling, 1M context, and strong across every task type. The price ($3/$15) is reasonable for daily use, and prompt caching brings it down further.
"I want the cheapest option that's still useful" DeepSeek V3.2. At $0.02/session, you'll spend less than $1/month. It handles 80% of daily tasks well. Switch to Claude for the other 20%.
"I process huge documents and need massive context" Gemini 2.5 Pro. 1M token context at $1.25/$10, with a free tier for experimentation. Pair it with Claude for tasks that need reliable multi-step tool use.
"Privacy is non-negotiable — nothing can leave my network" Llama 4 Maverick via Ollama. Run it locally, keep everything on your hardware. Quality is below Claude/GPT for complex tasks, but excellent for general use.
"I want maximum intelligence regardless of cost" Claude Opus 4.6. Leads SWE-bench (80.9%), General Agent Evaluation (0.66 success rate), and produces the most reliable output for complex, multi-step agent workflows.
"I run an OpenClaw agent for my team" Consider cowork.ink — it gives your entire team shared access to AI agents with built-in orchestration, so everyone gets the same context without managing individual OpenClaw instances and API keys.
How to Switch Models in OpenClaw
Changing your model takes one config edit:
# ~/.openclaw/config.yaml
provider: anthropic # anthropic, openai, google, openrouter, ollama
model: claude-sonnet-4-6 # any supported model slug
api_key: sk-... # your API key (or use OPENCLAW_API_KEY env var)
For model routing (different models for different tasks), add a routing block:
# ~/.openclaw/config.yaml
default_model:
provider: openrouter
model: deepseek/deepseek-chat
escalation_model:
provider: anthropic
model: claude-sonnet-4-6
trigger: complex # auto-escalate for multi-step tool chains
Restart OpenClaw after any config change: openclaw restart
What's Coming Next
The model landscape moves fast. Key developments to watch in 2026:
- Claude Opus 4.6 price drops — Anthropic cut Opus pricing 66% from the 4.0 generation. Further drops will make premium models viable as defaults.
- DeepSeek V4 — expected to close the tool-calling reliability gap while maintaining budget pricing.
- Gemini 3 Pro — Google's next-gen model aims to fix the action completion gap (currently 38% on Galileo).
- Llama 5 — Meta's next open-source release will likely narrow the gap with proprietary models further.
- OpenClaw's model routing improvements — the core team is building smarter auto-routing that picks the right model per task without manual configuration.
Get Started
Already running OpenClaw? Edit your config, switch models, and test the difference yourself. Not running it yet? Follow our OpenClaw tutorial to set up your agent in 30 minutes.
If you're a solo developer, GoGogot is another option — a self-hosted AI agent in Go that supports all the same models with one Docker command. At $0.02/session with DeepSeek, it's the cheapest way to run a personal AI agent.
If you're on a team, cowork.ink gives everyone shared access to AI agents and context — no per-person API key juggling required.