If you ask, “What’s the best LLM for OpenClaw?”, you’re already asking the wrong question.
OpenClaw deployments usually mix very different workloads: cheap cron jobs, repetitive tool calls, customer-facing agent runs, approval-gated writes, and occasional hard reasoning tasks. The model that is best for one of those jobs is often the wrong choice for the others.
The more useful question is: which model should handle which part of the system?
The model list in this article started with Reddit. We processed the most popular posts and threads in communities discussing OpenClaw to surface which models practitioners actually reach for — not which ones score best on benchmarks. That community signal was then used as the basis for deeper research, pricing analysis, and the comparison tables below. It is a practical guide for choosing a stack that fits OpenClaw, grounded in what real users report working for them.
The short version
If you want the blunt takeaway:
- Budget-first: Minimax or DeepSeek V3
- Balanced default: Kimi K2.5
- Premium hard tasks: Claude Sonnet or Claude Opus
- Cheap helper model: GPT-5.4 Mini
- Lowest-cost experimentation: Gemini 3 Flash
- Local stack: Qwen3.5 or GLM
- Best overall architecture: route tasks across multiple models instead of forcing one model to do everything
That last point matters the most. For serious OpenClaw deployments, the strongest recommendation is not “pick the single best model.” It is “build a routing strategy.”
Comparison table
| Model / Option | Main Strength | Best OpenClaw Uses | Cost Profile | Tradeoffs / Caveats |
|---|---|---|---|---|
| Minimax | Strong price-performance ratio | General-purpose agents, cost-sensitive deployments, mixed workloads | Low | Can fall behind premium models on harder reasoning |
| Kimi K2.5 | Good balance of capability and cost | Everyday OpenClaw usage, cron jobs, stable recurring tasks | Low-Medium | Not the strongest option for the hardest reasoning workloads |
| DeepSeek V3 | Very cost-efficient for repetitive work | Cron jobs, repetitive automations, high-volume routine tasks | Low | Less favored when output quality matters more than cost |
| Claude Sonnet | Strong quality and reliable reasoning | Higher-quality runs, nuanced tasks, tool-heavy workflows where output reliability matters | High | More expensive than budget-oriented models |
| Claude Opus | Best-in-class reasoning in this set | Complex reasoning, difficult multi-step flows, high-stakes agent work | Very High | Expensive and unnecessary for routine work |
| ChatGPT Plus via OAuth | Simple setup and predictable monthly cost | General use, easy onboarding, avoiding direct API complexity | Fixed monthly | Less flexible than a model-routing stack |
| GPT-5.4 Mini | Cheap lightweight worker model | Low-cost defaults, helper model, classification and lightweight agent steps | Low | Not ideal for demanding reasoning |
| Gemini 3 Flash | Very low-cost entry point | Lightweight agents, experimentation, budget-constrained usage | Very Low / Free tier | Usually not the first choice for difficult tasks |
| Qwen3.5 (local) | Strong local inference option | Privacy-sensitive workflows, local-only setups, long-context local use | Hardware-based | Requires capable hardware and more setup work |
| GLM (local) | Reliable local option | Local cron jobs, self-hosted agents, simple dependable workflows | Hardware-based | Less associated with top-end capability |
| OpenRouter | Cost and provider optimization | Fallback routing, provider abstraction, task-specific switching | Depends on routed models | Adds operational complexity because it is routing, not a model |
Scoring matrix
Ratings on a 1–5 scale. Cost is rated inversely — 5 means cheapest (best value). Local indicates whether self-hosting is the primary deployment mode.
| Model | Cost (5=cheapest) | Output Quality | Tool Reliability | Context Window | Setup Ease | Local? |
|---|---|---|---|---|---|---|
| Minimax | ★★★★★ | ★★★☆☆ | ★★★☆☆ | ★★★★★ | ★★★★☆ | No |
| Kimi K2.5 | ★★★★☆ | ★★★★☆ | ★★★★☆ | ★★★☆☆ | ★★★★☆ | No |
| DeepSeek V3 | ★★★★★ | ★★★☆☆ | ★★★☆☆ | ★★★☆☆ | ★★★★☆ | No |
| Claude Sonnet | ★★☆☆☆ | ★★★★★ | ★★★★★ | ★★★★☆ | ★★★★★ | No |
| Claude Opus | ★☆☆☆☆ | ★★★★★ | ★★★★★ | ★★★★☆ | ★★★★★ | No |
| ChatGPT Plus (OAuth) | ★★★☆☆ | ★★★★☆ | ★★★★☆ | ★★★☆☆ | ★★★★★ | No |
| GPT-5.4 Mini | ★★★★★ | ★★★☆☆ | ★★★☆☆ | ★★★☆☆ | ★★★★★ | No |
| Gemini 3 Flash | ★★★★★ | ★★★☆☆ | ★★★☆☆ | ★★★★★ | ★★★★☆ | No |
| Qwen3.5 (local) | ★★★★★ | ★★★★☆ | ★★★☆☆ | ★★★★☆ | ★★☆☆☆ | Yes |
| GLM (local) | ★★★★★ | ★★★☆☆ | ★★★☆☆ | ★★★☆☆ | ★★★☆☆ | Yes |
API pricing reference
Approximate costs per million tokens as reported by the community and public pricing pages. Local models are excluded since cost is hardware-dependent.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Free tier? |
|---|---|---|---|
| Minimax | ~$0.20 | ~$1.10 | No |
| Kimi K2.5 (via OpenRouter) | ~$0.07–0.15 | ~$0.30–0.60 | Limited |
| DeepSeek V3 | ~$0.27 | ~$1.10 | No |
| Claude Sonnet | ~$3.00 | ~$15.00 | No |
| Claude Opus | ~$15.00 | ~$75.00 | No |
| GPT-5.4 Mini | ~$0.15 | ~$0.60 | No |
| Gemini 3 Flash | ~$0.00–0.10 | ~$0.00–0.40 | Yes |
| ChatGPT Plus (OAuth) | $20/mo flat | — | No |
Prices are indicative and change frequently. Check provider pricing pages before committing to a stack.
Context window comparison
Context window size matters when an OpenClaw agent needs to read long threads, large documents, or extended conversation history in a single pass.
| Model | Context Window | Notes |
|---|---|---|
| Minimax | 1M tokens | One of the largest available |
| Gemini 3 Flash | 1M tokens | Large context, suitable for document-heavy workflows |
| Claude Sonnet / Opus | 200K tokens | Strong long-context reliability |
| Kimi K2.5 | ~128–200K tokens | Varies by access method |
| DeepSeek V3 | 128K tokens | Sufficient for most agent tasks |
| GPT-5.4 Mini | 128K tokens | Standard modern context size |
| Qwen3.5-27B (local) | ~115K reliable | Community-reported with 32GB VRAM at Q4-K-M quantization |
| GLM (local) | Varies | Depends on quantization and hardware |
Task-type model matching
| Task Type | Recommended Model(s) | Avoid |
|---|---|---|
| High-volume cron jobs | DeepSeek V3, Kimi K2.5, GLM (local) | Opus, Sonnet |
| Inbox triage / classification | GPT-5.4 Mini, DeepSeek V3, Gemini Flash | Opus |
| Complex multi-step reasoning | Claude Opus | Minimax, GPT-5.4 Mini |
| Tool-heavy agent workflows | Claude Sonnet, Claude Opus | Local models (less tested) |
| Experimentation / prototyping | Gemini 3 Flash, Kimi K2.5 | Opus |
| Privacy-sensitive / self-hosted | Qwen3.5 (local), GLM (local) | Any cloud API |
| Long-document context tasks | Minimax, Gemini Flash | DeepSeek V3 |
| Budget-first general use | Minimax, Kimi K2.5 | Claude Opus |
| Simple onboarding | ChatGPT Plus (OAuth) | Local models |
What matters for OpenClaw
1. Repetitive agent work should usually be cheap
If you are running scheduled jobs, inbox cleanup, enrichment passes, queue triage, or other repeatable automations, premium reasoning is usually wasted money.
That is where DeepSeek V3, Minimax, Kimi K2.5, or a local option like GLM make sense. These jobs are high-volume, low-drama, and predictable. Optimize for cost first.
2. Tool-heavy workflows need reliability, not just raw intelligence
For OpenClaw, plenty of failure modes are not about abstract reasoning. They are about whether the model follows instructions, uses tools consistently, and returns stable outputs under repeated execution.
That is why Claude Sonnet stands out as a premium default for higher-quality runs. It is not just about being “smarter.” It is about being more dependable when an agent needs to read context, call tools, and produce something you can trust.
3. Save premium reasoning for the tasks that deserve it
Claude Opus belongs on the expensive side of the stack. That is where you put the genuinely hard tasks:
- complicated multi-step planning
- exception handling
- ambiguous or high-context decisions
- high-stakes drafts before human review
Using a model like Opus for routine recurring work is the classic way to overpay for an agent system.
4. Local models are for privacy, control, and predictable infrastructure
If you need self-hosted operation, tighter data control, or the ability to run workloads without depending on an external API, the community feedback points to Qwen3.5 and GLM as practical local choices.
That does not automatically make them the best general-purpose answer. It means they are strong when your constraints are about control, locality, or compliance rather than absolute frontier performance.
Recommended setups by scenario
Cheapest useful default
Pick Minimax, DeepSeek V3, or GPT-5.4 Mini if the goal is simple: keep the system useful while keeping the bill down.
This is a good starting point for internal automations, cron jobs, and non-critical agent loops.
Best balanced default
Pick Kimi K2.5 if you want one model that feels reasonable across most everyday OpenClaw tasks without immediately jumping to premium pricing.
It is the “sane default” option in this comparison.
Best for repetitive cron-style work
Pick DeepSeek V3, Kimi K2.5, or GLM local.
These workloads reward consistency and low cost more than premium reasoning.
Best quality for hard reasoning
Pick Claude Opus first, then Claude Sonnet if you want a cheaper premium option.
This is where you optimize for outcome quality instead of cost per token.
Best for low-cost experimentation
Pick Gemini 3 Flash if you want a cheap place to prototype workflows, test assumptions, or validate whether a use case is worth scaling.
Best local or self-hosted route
Pick Qwen3.5 or GLM if your operating model depends on local inference.
Best architecture for scale
Use OpenRouter or an equivalent routing layer and assign different model classes to different job types.
That is the most scalable answer because it matches cost to task difficulty.
A practical OpenClaw routing strategy
For many teams, a good OpenClaw stack looks something like this:
- Use a cheap model for repetitive background work, classification, enrichment, and scheduled jobs.
- Route normal interactive tasks to a balanced default like Kimi K2.5 or a quality-focused model like Claude Sonnet.
- Escalate only the hard or high-risk steps to a premium reasoner like Claude Opus.
- Keep a local model in reserve if privacy, resilience, or offline operation matters.
This approach is better than picking a single “best” model because OpenClaw systems are not one workload. They are a bundle of workloads with different economics.
Final recommendation
If you are early, start simple:
- choose Kimi K2.5 if you want a balanced default
- choose Minimax or DeepSeek V3 if you are cost-sensitive
- choose Claude Sonnet if reliability matters more than cost
Then evolve toward routing.
That is the main lesson from community feedback: the winning move is usually not finding the perfect single model. It is designing a system where cheap models handle cheap work and expensive models are reserved for tasks that can actually justify them.