OpenClaw Best AI Models for Specific Tasks
Comprehensive analysis of the best AI models for OpenClaw across 8 categories including Opus 4.6, GPT-5.3-Codex, Gemini Veo 3.1, Nano Banana Pro, MiniMax M2.5, Kimi K2.5, and GLM-5. February 2026 Extended Edition.
Executive Summary
OpenClaw (formerly Clawdbot/Moltbot) is an open-source AI agent platform created by Peter Steinberger that has rapidly grown to over 188,000 GitHub stars. Unlike traditional chatbots, OpenClaw operates as a persistent, autonomous assistant that connects AI models to your local machine, messaging apps, and 50+ third-party integrations. It runs 24/7, maintains long-term memory, and proactively executes tasks without prompting.
On February 14, 2026, Steinberger announced he would be joining OpenAI, with the project moving to an open-source foundation.
Because OpenClaw is model-agnostic, selecting the right AI model for each task category is the single most impactful decision users can make. This report analyzes the best model choices across eight key categories: Primary, Programming, Images, Copy, SEO, Creatives, Ads, and Video.
It features expanded analysis of the rapidly-emerging open-source contenders — MiniMax M2.5, Kimi K2.5, and GLM-5 — which are disrupting pricing by delivering near-frontier performance at 10–50x lower cost. It also covers GPT-5.3-Codex (OpenAI's new SOTA coding model), Google's Nano Banana Pro for image generation, and Gemini Veo 3.1 for video.
Quick-Reference: Model Recommendations by Category
| Category | TOP PICKS | Cost | Key Strengths |
|---|---|---|---|
| 🧠 Primary | Claude Sonnet 4.5 Kimi K2.5 | $$ / $ | Best daily driver; 90% Opus quality at 1/5 cost. Kimi: free on OpenClaw, ~90% Opus capability |
| 💻 Code | GPT-5.3-Codex MiniMax M2.5 | $$$ / $ | SOTA agentic coding; SWE-Bench Pro leader. M2.5: 80.2% SWE-Bench Verified, ~95% cheaper than Opus |
| 🎨 Images | Nano Banana Pro FLUX.1 [dev] | $$ / $ | Semantic understanding; superior text rendering. FLUX: 6x cheaper, faster for prototyping |
| ✍️ Copy | Claude Sonnet 4.5 MiniMax M2.5 | $$ / $ | Persistent brand voice via memory; conversion frameworks. M2.5: high-volume at 1/50th cost |
| 🔍 SEO | Claude Sonnet 4.5 GLM-5 | $$ / $ | Strong reasoning; prompt-injection resistant. GLM-5: best factual reliability for knowledge tasks |
| 🎯 Creatives | Gemini 2.5 Pro Kimi K2.5 | $$ / $ | 1M+ context for long briefs; strong synthesis. Kimi: 2M context, Agent Swarm, vision-text |
| 📢 Ads | Claude Sonnet 4.5 MiniMax M2.5 | $$ / $ | Reliable tool-use for multi-platform monitoring. M2.5: viable tool-calling at fraction of cost |
| 🎬 Video | Gemini Veo 3.1 Kling 2.6 Pro | $$$ / $$ | Native audio, 4K, vertical video; best-in-class. Kling: image-to-video, ClawVid pipeline |
Cost Tier: $ = Under $5/mo | $$ = $5–$50/mo | $$$ = $50–$200/mo | $$$$ = $200+/mo
All models in this guide are available via OpenRouter in AI Team OS.
We highly recommend using Claude Opus and Sonnet via a Claude Pro or Claude Max subscription when you want the best quality and value for Anthropic models.
1. 🧠 Primary Model — The Daily Driver
Top pick: Claude Sonnet 4.5 · Runner up: Kimi K2.5
Top Pick: Claude Sonnet 4.5 ($3/$15 per million input/output tokens)
Claude Sonnet 4.5 is the consensus daily driver across community forums, independent guides, and official documentation. It delivers approximately 90% of Claude Opus's capability at one-fifth the cost. It handles the bread-and-butter OpenClaw tasks reliably: email management, calendar scheduling, web browsing, research queries, and standard multi-step automations.
Its tool-calling reliability is the decisive factor — OpenClaw's 50+ integrations depend on consistent function-calling accuracy, and Sonnet leads the field. Prompt-injection resistance is non-negotiable since OpenClaw processes untrusted content from emails, websites, and messaging platforms. Community testing consistently rates Claude models highest on this axis, which is why Anthropic's models dominate the recommendations despite competitive benchmark scores from alternatives.
Runner up: Kimi K2.5
Kimi K2.5 is the strongest open-source daily driver. Community members report it can do roughly 90% of what Opus 4.5 can do, at 7x cheaper cost. It is available as the first free premium model on OpenClaw, making it the zero-cost entry point for new users.
Budget Alternative: MiniMax M2.5 at $0.30/$1.20 per million tokens is 33–42x cheaper than Opus 4.6 and handles routine assistant tasks well. Claude Haiku 4.5 ($0.80/$4.00) is 20x cheaper than Sonnet for simple tasks like heartbeat checks and lightweight routing.
2. 💻 Programming & Code
Top pick: GPT-5.3-Codex · Runner up: MiniMax M2.5
Top Pick: GPT-5.3-Codex (OpenAI)
Released February 5, 2026, GPT-5.3-Codex is the most capable agentic coding model to date. It is the first model to combine Codex and GPT-5 training stacks, bringing together best-in-class code generation, reasoning, and general-purpose intelligence in one unified model.
It achieves state-of-the-art performance on SWE-Bench Pro (a multi-language, contamination-resistant benchmark spanning Python, Go, TypeScript, and Rust) and far exceeds previous models on Terminal-Bench 2.0. Critically, it accomplishes these results using fewer tokens than any prior model.
GPT-5.3-Codex supports real-time mid-turn steering — you can interact with the model while it's working without losing context. It is 25% faster than its predecessor and excels at long-running tasks involving research, tool use, and complex execution.
Independent reviewer Nathan Lambert noted that Codex 5.3 feels more Claude-like in its feedback quality and handles git operations reliably (previous Codex versions regularly failed basic git tasks). However, it is currently available only via ChatGPT paid plans; API access is pending, which limits its use as an OpenClaw backend today.
Premium Alternative: Claude Opus 4.6 ($15/$75) remains the top choice when reliable tool-calling within OpenClaw's specific agent architecture matters most. It handles multi-file edits and complex debugging with the highest instruction-following precision. For developers running always-on OpenClaw coding agents (automated testing, PR reviews, codebase management), Opus delivers the reliability that justifies its cost.
Runner up: MiniMax M2.5
MiniMax M2.5 scores 80.2% on SWE-Bench Verified, matching Claude Opus 4.6 speeds. It was trained across 10+ languages and 200,000+ real-world environments via reinforcement learning, developing a "spec-writing" behavior where it plans architecture before writing code. At $0.30/$1.20 per million tokens, it is approximately 95% cheaper than Opus.
Other options: Kimi K2.5 scores 76.8% on SWE-Bench Verified with native multimodal capabilities (it can debug from screenshots). GLM-5, while slightly weaker on coding benchmarks, is trained entirely on Huawei chips — a milestone in China's push toward self-reliant AI infrastructure.
3. 🎨 Image Generation
Top pick: Nano Banana Pro · Runner up: FLUX.1 [dev]
Top Pick: Nano Banana Pro (Google Gemini 3 Pro Image) via fal.ai
Nano Banana Pro represents a generational leap in AI image generation. Built on Google's Gemini 3 Pro foundation, it processes prompts through a multimodal architecture that understands nuance, context, and creative intent rather than simple keyword matching. Unlike traditional diffusion models that treat prompts as weighted token collections, Nano Banana Pro interprets creative direction holistically, capturing relationships between concepts that single-modality systems miss.
Standout capabilities:
- Industry-leading text rendering — accurate typography in multiple languages directly within images
- Native 4K resolution output
- Character consistency across multiple generations
- Multi-image fusion — up to 14 reference images
- Physics-aware scene composition that simulates gravity and causal logic before rendering
At $0.15 per image, it delivers roughly 7 generations per dollar. Google's own Veo 3.1 documentation recommends using Nano Banana Pro to create ingredient images for video generation — making it the ideal front-end for the image-to-video pipeline.
| Model | Price/Image | Best For | Resolution |
|---|---|---|---|
| Nano Banana Pro | $0.15 | Typography, infographics, semantic accuracy | Up to 4K |
| GPT-Image-1.5 | ~$0.04–$0.17 | Photorealistic, commercial creative | Up to 1536px |
| FLUX.1 [dev] | ~$0.025 | Fine detail, resolution control, open-source | Up to 2K |
| Kling Image v3 | ~$0.08 | Stylized/creative imagery; video pipeline input | Up to 1536px |
| DALL-E 3 | ~$0.04–$0.12 | Quick concepts; wide style range | Up to 1792px |
Runner up: FLUX.1 [dev]
FLUX.1 [dev] via fal.ai at ~$0.025/image is 6x cheaper and faster, ideal for rapid prototyping and high-volume generation where maximum semantic accuracy is less critical.
Alternative for Commercial Creative: GPT-Image-1.5 (OpenAI) excels at photorealistic commercial content and is natively integrated with OpenClaw's openai-image-gen skill supporting transparent backgrounds and WebP output.
4. ✍️ Copywriting & Content
Top pick: Claude Sonnet 4.5 · Runner up: MiniMax M2.5
Top Pick: Claude Sonnet 4.5 (Anthropic)
OpenClaw's persistent memory system is a transformative advantage for copywriting. The model retains brand voice rules, product positioning, compliance constraints, and audience segments across sessions, reducing the "brand drift" common in AI-generated content.
Claude Sonnet 4.5 pairs with dedicated copywriting skills on ClawHub that enforce conversion-focused frameworks: clarity over cleverness, benefits over features, specificity over vagueness, and customer language over company language.
The copywriting skill ecosystem includes tools for homepage copy, landing pages, pricing pages, feature pages, ad variations, email sequences, and A/B test headline generation. The marketing-mode skill combines 23 marketing disciplines into a single framework covering psychology-based persuasion, funnel strategy, and conversion optimization. Sonnet's instruction-following precision ensures consistent adherence to these frameworks.
Runner up: MiniMax M2.5
MiniMax M2.5 was trained in collaboration with domain experts in finance, law, and social sciences. In evaluations on office tasks including Word documents and PowerPoint presentations, it achieved a 59% win rate against mainstream models. For high-volume content production where cost matters more than peak quality, M2.5 at 1/50th the cost of Opus is compelling.
5. 🔍 SEO & Search Optimization
Top pick: Claude Sonnet 4.5 · Runner up: GLM-5
Top Pick: Claude Sonnet 4.5 (Anthropic)
SEO analysis through OpenClaw relies on the model's reasoning capability and safe web content processing. Claude Sonnet 4.5 is explicitly recommended by the OpenClaw SEO community for its combination of analytical reasoning and prompt-injection resistance.
Dedicated SEO skills automate technical audits (schema markup, robots.txt, redirect chains), content gap analysis, keyword research, competitor monitoring, and ranking tracking. The model runs seven audit categories in a single pass, scored out of 70, and checks content for AI-detection patterns and optimization for Google AI Overviews.
OpenClaw's cron job system enables weekly automated SEO monitoring. Users report that a DigitalOcean VPS at ~$6/month plus API costs of ~$15/month provides a complete SEO automation stack significantly cheaper than tools like Ahrefs ($99/mo) or SEMrush ($129/mo).
Runner up: GLM-5
GLM-5 excels at knowledge-intensive tasks, scoring 92.7% on AIME 2026 math reasoning and 50.4 on Humanity's Last Exam. Its industry-leading factual reliability (35-point improvement over predecessors on hallucination benchmarks) makes it well-suited for SEO workflows requiring highly accurate, factual content generation.
6. 🎯 Creative Strategy & Design Briefs
Top pick: Gemini 2.5 Pro · Runner up: Kimi K2.5
Top Pick: Gemini 2.5 Pro (Google)
Creative strategy work often involves synthesizing large volumes of input — brand guidelines, competitor analyses, market research, audience data, and creative briefs. Gemini 2.5 Pro's 1M+ token context window makes it uniquely suited for this kind of synthesis without context degradation.
For creative workflows, OpenClaw users configure multi-agent setups where a dedicated marketing agent handles research and ideation using Gemini, while the primary agent (Sonnet) coordinates execution.
Runner up: Kimi K2.5
Kimi K2.5 supports up to 2 million tokens of context and its native vision-text architecture enables "coding while watching video" and design analysis from screenshots. Its Agent Swarm capability can self-decompose complex creative tasks and deploy multiple sub-agents for parallel execution, reportedly improving efficiency by up to 4.5x.
7. 📢 Advertising & Paid Media
Top pick: Claude Sonnet 4.5 · Runner up: MiniMax M2.5
Top Pick: Claude Sonnet 4.5 (Anthropic)
Advertising workflows in OpenClaw span campaign monitoring, budget pacing, creative performance analysis, audience refinement, and multi-platform reporting across Google Ads, Meta, LinkedIn, TikTok, and email platforms. The model's tool-calling reliability is decisive — ad management requires consistent, accurate interactions with APIs and dashboards.
OpenClaw's cron system enables always-on monitoring: tracking spend, flagging creative fatigue, and generating automated client reports that compile data from multiple platforms into narrative summaries.
Runner up: MiniMax M2.5
MiniMax M2.5 scored 76.8% on BFCL (Berkeley Function Calling Leaderboard) for agentic tool-calling, making it viable for monitoring and reporting tasks. At $0.30/$1.20, running three always-on agents through Telegram costs a fraction of frontier models. Teams report running specialized agents (dev, marketing, business) simultaneously, each handling different campaign aspects.
8. 🎬 Video Generation
Top pick: Gemini Veo 3.1 · Runner up: Kling 2.6 Pro
Top Pick: Gemini Veo 3.1 (Google DeepMind)
Gemini Veo 3.1, released January 2026, is Google's state-of-the-art video generation model offering native audio synthesis, vertical video (9:16) for platforms like YouTube Shorts, scene extension for longer narratives, and upscaling to 1080p and 4K resolution.
On the MovieGenBench benchmark, Veo 3.1 outperforms competitors on overall preference, prompt alignment, visual quality, and physics realism. Its "Ingredients to Video" feature accepts up to four reference images per generation, enabling precise control over subjects, styles, and compositions with consistent character identity across scene changes.
Veo 3.1 is accessible through the Gemini API, Vertex AI, the Gemini app, YouTube Shorts, and Flow. The Veo 3.1 Fast variant is specifically optimized for backend services that programmatically generate ads, rapid A/B testing of creative concepts, and apps producing social media content at scale.
| Model | Resolution | Key Feature | OpenClaw Integration |
|---|---|---|---|
| Gemini Veo 3.1 | 720p–4K | Native audio, scene extension, 9:16 vertical | Gemini API; direct skill integration |
| Kling 2.6 Pro | Up to 1080p | Image-to-video; up to 2 min clips | ClawVid skill pipeline |
| Runway Gen-4.5 | Up to 4K | #1 benchmark Elo; motion brushes | API via custom skill |
| Sora 2 | Up to 1080p | Long-form narrative; text integration | OpenAI API skill |
| Luma Ray3 | 4K HDR | Superior physics simulation | API via custom skill |
Runner up: Kling 2.6 Pro
The ClawVid skill orchestrates a six-phase pipeline using Kling 2.6 Pro (image-to-video via fal.ai), Qwen-3-TTS for narration with voice cloning, and Beatoven for sound effects. This provides more granular control over each phase but requires more configuration. Users have generated fully automated UGC-style content, short-form horror videos, and product demos entirely through conversational prompts.
Alternative for Highest Benchmark: Runway Gen-4.5 holds the #1 position on the Artificial Analysis Text-to-Video benchmark with 1,247 Elo points. It offers motion brushes, scene consistency, and superior understanding of physics and human motion.
🔥 Open-Source Model Showdown: MiniMax M2.5 vs. Kimi K2.5 vs. GLM-5
The open-source landscape for OpenClaw has been transformed by three models released in rapid succession in late January and February 2026. Each brings distinct strengths, and understanding their differences is critical for cost-optimized deployments.
| Model | Parameters | SWE-Bench V. | Input $/1M | Output $/1M | Best For |
|---|---|---|---|---|---|
| Kimi K2.5 | 1T MoE | 76.8% | ~$0.60 | ~$2.40 | Agent swarm, visual coding, 2M context |
| MiniMax M2.5 | 229B MoE (10B active) | 80.2% | $0.30 | $1.20 | Budget coding agent; 95% cheaper than Opus |
| GLM-5 | 744B MoE (40B active) | ~76% | ~$0.50 | ~$2.00 | Reasoning, factual reliability, knowledge tasks |
| Llama 3.3 70B | 70B dense | ~45% | Free (local) | Free (local) | Privacy-first; simple tasks on local hardware |
MiniMax M2.5: The Budget Coding Powerhouse
MiniMax M2.5 is arguably the most disruptive model for OpenClaw economics. Released February 12, 2026, it achieves 80.2% on SWE-Bench Verified — matching Claude Opus 4.6 — while costing 33x less on input tokens and 42x less on output tokens.
Despite having 229 billion total parameters, only 10 billion are activated per token (MoE architecture), making it the smallest model among all Tier-1 performers. For self-hosting users, this translates to dramatically lower compute and memory requirements.
MiniMax trained M2.5 using their proprietary Forge reinforcement learning framework across 200,000+ real-world environments spanning 10+ programming languages. The result is what MiniMax calls an "Architect Mindset": the model proactively plans project architecture, structure, and UI design before writing code.
MiniMax reports that M2.5 now autonomously completes 30% of daily tasks within their company, with 80% of newly committed code generated by the model.
The M2.5 Lightning variant runs at 100 tokens per second (double the standard variant) at a modest price premium, ideal for interactive OpenClaw sessions where latency matters.
Community users report successfully running three specialized M2.5-powered agents simultaneously through Telegram (a development agent, a marketing agent, and a business operations agent) at minimal cost.
Limitations: Despite strong benchmark scores, real-world reports are mixed. Artificial Analysis rated MiniMax 2.1's Coding Index at 33, well below frontier models. Some users report degraded instruction-tracking in very long sessions. Tool-calling reliability in OpenClaw's specific agent architecture has not been validated as extensively as Claude's.
Kimi K2.5: The Visual Agentic Leader
Kimi K2.5, released by Moonshot AI in late January 2026, is the consensus leading open-weights model. It uses a 1-trillion-parameter MoE architecture trained on 15 trillion tokens with a native vision-text architecture (not a bolted-on vision encoder).
On the Artificial Analysis GDPval-AA Leaderboard, it debuted with an Elo score of 1309, implying a 66% win rate against GLM-4.7 (the prior open-weights leader).
Kimi K2.5's standout feature is its Agent Swarm system: when handling complex tasks, it can self-decompose and deploy multiple sub-agents working in parallel, reportedly improving efficiency by up to 4.5x. Its visual coding capabilities allow debugging from screenshots and analyzing UI designs directly. Context extends to 2 million tokens, exceeding even Gemini for ultra-long document analysis.
OpenClaw announced Kimi K2.5 as its first free premium model, making it the zero-cost entry point. Multiple providers (Fireworks AI, Baseten, NVIDIA NIM) serve the model, with Baseten advertising performance on par with closed-source models at a fraction of the cost.
Community testers report it handles approximately 90% of what Claude Opus 4.5 can do for coding tasks, though deeper code quality analysis shows subtle differences in edge cases.
Limitations: Scores -11 on the AA-Omniscience hallucination index (compared to Opus 4.5 at +10 and Gemini 3 Pro at +13), indicating less factual reliability. Scores 46% on WeirdML versus 64% for Opus and 72% for GPT-5.2, suggesting weaker out-of-distribution reasoning. Some users report it operates "very permissively," attempting fixes that are not properly scaffolded.
GLM-5: The Reasoning and Knowledge Specialist
GLM-5, released February 11, 2026 by Zhipu AI under MIT license, is the largest of the three at 744 billion parameters (40 billion active per token). Its defining feature is knowledge reliability: it achieved industry-leading results on the AA-Omniscience hallucination evaluation with a 35-point improvement over its predecessor, making it the most factually reliable open-source model available.
It scored:
- 92.7% on AIME 2026 math reasoning
- 86.0% on GPQA-Diamond scientific reasoning
- 50.4 on Humanity's Last Exam (with tools) — outperforming Claude Opus 4.5's score of 43.4
For knowledge-intensive OpenClaw workflows like technical documentation, academic research assistance, and knowledge base construction, GLM-5 is the most reliable choice among open-source options.
Perhaps most significantly, GLM-5 is 100% trained on Huawei chips, marking a milestone in China's self-reliant AI infrastructure. It supports a 128K output token limit, far exceeding most competitors, and is available through multiple providers including Fireworks AI.
Limitations: User feedback on general-purpose agent tasks has been mixed. While benchmark scores approach Claude Opus 4.5 parity, real-world OpenClaw usage reports suggest it is not yet as reliable for the consistent tool-calling and multi-step automation chains that define the agent workflow. Community consensus positions it as better suited for reasoning-heavy tasks than for always-on agent operation.
Head-to-Head: Which Open-Source Model to Choose?
- For coding-heavy workflows: MiniMax M2.5. Highest SWE-Bench Verified score (80.2%), cheapest pricing, smallest active parameters. Best cost-performance ratio for automated development agents.
- For visual and multimodal tasks: Kimi K2.5. Native vision-text architecture, Agent Swarm for parallel task decomposition, 2M token context, free via OpenClaw. Best for workflows involving screenshots, UI analysis, and creative coding.
- For research and knowledge tasks: GLM-5. Industry-leading factual reliability, strongest math and science reasoning, 128K output limit. Best for SEO content generation, technical documentation, and academic research workflows.
- For general daily driver (budget): Kimi K2.5. Broadest capability coverage, free access, and the most community validation on OpenClaw. MiniMax M2.5 is the runner-up for users who prioritize coding quality and minimal cost.
🚀 GPT-5.3-Codex: The New Frontier of Agentic Coding
GPT-5.3-Codex, released February 5, 2026, represents a step change in agentic coding capability. It is the first model that was instrumental in creating itself — the Codex team used early versions to debug its own training, manage deployment, and diagnose evaluations.
Key characteristics:
- Unified Architecture: First model combining Codex and GPT-5 training stacks, delivering best-in-class code generation, reasoning, and general-purpose intelligence in one model.
- Benchmark Leadership: State-of-the-art on SWE-Bench Pro (multi-language, contamination-resistant) and Terminal-Bench 2.0, using fewer tokens than any prior model.
- Interactive Steering: Supports real-time mid-turn interaction — ask questions, discuss approaches, and redirect while the model is actively working, without losing context.
- Security Classification: First OpenAI model rated "high" for cybersecurity risk under their Preparedness Framework, with API access delayed and comprehensive safety stack deployed.
For OpenClaw specifically, GPT-5.3-Codex's API access is not yet available (as of mid-February 2026), limiting its immediate utility as an OpenClaw backend. Once API access arrives, it will be a strong contender for the programming category, particularly for long-running autonomous coding tasks.
The Codex-Spark variant delivers 1000+ tokens per second on Cerebras hardware for interactive coding where latency matters.
💰 Comprehensive Cost Analysis
The table below provides a complete pricing overview of all models discussed, with estimated monthly costs based on real OpenClaw community usage data.
| Model | Input/1M Tokens | Output/1M Tokens | Monthly Estimate (OpenClaw) |
|---|---|---|---|
| Claude Opus 4.6 | $15.00 | $75.00 | $500–$750 (heavy) / $50–$150 (moderate) |
| GPT-5.3-Codex | API pending | API pending | ChatGPT Pro subscription; API pricing TBD |
| Claude Sonnet 4.5 | $3.00 | $15.00 | $15–$30 (light) / $50–$100 (moderate) |
| Claude Haiku 4.5 | $0.80 | $4.00 | $5–$15 (background tasks) |
| GPT-4o | $2.50 | $10.00 | $20–$60 (general use) |
| Gemini 2.5 Pro | $0.10–$1.25 | $0.50–$5.00 | $10–$40 (varies by tier) |
| Kimi K2.5 | ~$0.60 | ~$2.40 | $5–$25 / Free via OpenClaw promo |
| MiniMax M2.5 | $0.30 | $1.20 | $3–$15 (budget powerhouse) |
| MiniMax M2.5 Lightning | $0.30 | $2.40 | $5–$20 (100 tok/s, faster variant) |
| GLM-5 | ~$0.50 | ~$2.00 | $5–$20 (reasoning-heavy workflows) |
| DeepSeek V3 | ~$0.27 | ~$1.10 | $3–$15 (budget option) |
| Llama 3.3 70B (local) | Free | Free | Hardware cost only |
Note: Monthly estimates based on community-reported usage. Actual costs vary by workflow complexity. MiniMax M2.5 completes SWE-Bench Verified tasks in an average of 22.8 minutes consuming 3.52M tokens per task.
🎯 Recommended Multi-Model Strategy
The most effective OpenClaw deployments use model routing: different models for different tasks rather than a single expensive model for everything. Community guides and power users converge on a four-tier approach:
Tier 1 — Background & Heartbeat: Claude Haiku 4.5 or MiniMax M2.5. Handles heartbeat checks, simple summaries, lightweight routing. Keeps costs near-zero for the 80% of agent interactions that are routine.
Tier 2 — Daily Operations: Claude Sonnet 4.5 or Kimi K2.5 (free). Handles email, calendar, research, content drafting, SEO, ad monitoring, and standard automations. Best balance of capability and cost.
Tier 3 — High-Stakes Operations: Claude Opus 4.6 or GPT-5.3-Codex (when API available). Complex coding sprints, sensitive financial workflows, multi-file refactoring. Invoked intentionally, not left as default.
Tier 4 — Specialized Models: Gemini 2.5 Pro for ultra-long context. GLM-5 for knowledge-intensive reasoning. Nano Banana Pro for image generation. Veo 3.1 for video production. Kling 2.6 Pro for image-to-video pipelines.
Tools like ClawRouter can auto-route requests to the cheapest model capable of handling each task using 15-dimension local scoring, and Higress AI Gateway enables hot-swappable model configuration without restarting OpenClaw — critical given the pace of new model releases (MiniMax released three versions in 108 days).
🔒 Security Considerations
Model choice has direct security implications. Because OpenClaw processes untrusted content, prompt-injection resistance is a critical selection criterion. Claude models are consistently rated highest for injection resistance. GPT-5.3-Codex is OpenAI's first model rated "high" for cybersecurity risk — its coding capabilities could enable real-world cyber harm if misused.
Best practices:
- Run OpenClaw in an isolated environment (Docker, VM, or separate VLAN)
- Never route consumer subscriptions through OpenClaw (use API billing)
- Review ClawHub skill source code before installation (a Bitdefender audit flagged ~17% of skills as potentially malicious)
- Bind to 127.0.0.1 instead of 0.0.0.0
- Never commit API keys to version control
Conclusion
The OpenClaw model landscape in February 2026 is characterized by two major trends: frontier models getting more capable (Codex 5.3, Opus 4.6, Veo 3.1) and open-source models closing the gap dramatically (MiniMax M2.5 matching Opus on SWE-Bench at 1/50th the cost, Kimi K2.5 offered free as a premium model).
For most users, Claude Sonnet 4.5 remains the optimal daily driver. For coding, the choice between Opus 4.6 (best tool-calling in OpenClaw's architecture), Codex 5.3 (SOTA benchmarks, pending API access), and MiniMax M2.5 (95% cheaper, strong benchmarks) depends on budget and workflow criticality. For media generation, the stack of Nano Banana Pro (images) + Veo 3.1 (video) through Google's ecosystem represents the current state-of-the-art. And for budget-conscious or privacy-focused users, Kimi K2.5 and MiniMax M2.5 via self-hosting eliminate API costs entirely.
The platform's model-agnostic architecture means switching models mid-session is a single command. Treat model selection as infrastructure you revisit regularly — the landscape is shifting monthly, and the right choice today may not be the right choice next quarter.
Michael Serres is the founder of AI Team OS and Digital Central, helping businesses deploy AI agents that actually work. Follow him on LinkedIn for weekly insights on the AI workforce revolution.
Michael Serres
Founder & CEO at AI Team OS. Former BlackRock/HSBC/Manulife digital executive. Building the future of AI-powered teams.