The Best AI Models for OpenClaw in 2026

The AI landscape evolves rapidly, and keeping track of the best models for your tools can be overwhelming. OpenClaw supports dozens of models across multiple providers, each with unique strengths. This guide breaks down the top models to use with OpenClaw in early 2026, organized by use case.

The State of LLMs in 2026

We’re currently in what many call the “reasoning era” of large language models. The biggest advancement isn’t just raw knowledge; it’s the ability to think through complex problems step-by-step before responding. Models now excel at:

• Agentic coding: Writing, debugging, and refactoring code autonomously
• Multi-step reasoning: Breaking complex tasks into manageable chunks
• Tool use: Calling functions, APIs, and external tools intelligently
• Long-context processing: Handling hundreds of thousands of tokens

Let’s explore the best options for OpenClaw users.

For Coding and Development

Claude Fable 5 (Anthropic) [Added June 2026]

Model ID: claude-fable-5 (Bedrock: anthropic.claude-fable-5; Vertex: claude-fable-5)

Anthropic’s most capable widely released model, launched June 9, 2026 as the first Mythos-class tier above Opus. Fable 5 sits at the top of Anthropic’s lineup: it leads frontier models on FrontierCode (Cognition’s agentic coding benchmark at medium effort), posts state-of-the-art results on CursorBench, and is the first model to cross 90% on a core analytics benchmark. It also achieves the highest score among tested models on ViBench and the highest senior-level reasoning score on Hebbia’s finance benchmark. Anthropic has not published numeric scores for any of these; the rankings are Anthropic-reported. Pricing is $10/$50 per million input/output tokens, exactly twice Opus 4.8’s rate and the same as Opus 4.8’s fast mode. Adaptive thinking is always on; extended thinking (budget_tokens) is not supported. Fable 5 is included on subscription plans through June 22, 2026; from June 23 it draws usage credits. For OpenClaw users evaluating whether the 2x cost over Opus 4.8 is justified, see Claude Fable 5 vs Opus 4.8: Is the New Tier Worth Double the Price?.

Best for: The most demanding agentic coding tasks, long-running autonomous workflows, finance and analytics reasoning

Context: 1M tokens; max output 128k tokens (synchronous Messages API)

Trade-off: 2x the cost of Opus 4.8; no published numeric benchmarks to compare directly against competitors’ scores

Claude Opus 4.8 (Anthropic) [Updated May 2026]

Model ID: anthropic/claude-opus-4-8 (alias: opus)

Anthropic’s most capable Opus-tier model. Released May 28, 2026, Opus 4.8 is a quality upgrade over Opus 4.7 at identical pricing ($5/$25 per million input/output tokens) and the same 1M-token context window. Anthropic reports it is four times less likely than Opus 4.7 to allow flaws in code. On SWE-Bench Pro (the agentic coding benchmark), it scores 69.2% versus Opus 4.7’s 64.3% and GPT-5.5’s 58.6%. It also scores 74.6% on Terminal-Bench 2.1 and 83.4% on OSWorld-Verified. Beyond raw benchmark numbers, Anthropic says the model flags uncertainties more readily, makes fewer unsupported claims, and sustains independent work for longer stretches. A fast mode is available at $10/$50 per million tokens at roughly 2.5x the speed.

Best for: Complex refactoring, multi-file changes, understanding large codebases, debugging tricky issues

Context: 1M tokens (200k on Microsoft Foundry); max output 128k tokens

Trade-off: Premium pricing compared to smaller models

Kimi K2.5 (Moonshot AI)

Model ID: kimi-coding/k2p5 (alias: Kimi K2.5)

The default model for many OpenClaw installations, and for good reason. Kimi K2.5 offers exceptional coding performance with a massive 256k context window. It excels at tool calling and handles long conversations without losing track of earlier context. For a full technical breakdown of Kimi’s architecture (including its trillion-parameter MoE design and open-source licensing), see Kimi Claw: Moonshot AI’s Answer to Claude and ChatGPT.

Best for: Daily development work, tool-heavy workflows, long coding sessions

Context: 256k tokens

Special feature: Also available in “thinking” mode (kimi-k2-thinking) for deeper reasoning

GLM-5.2 (Z.ai / Zhipu) [Added June 2026]

Model ID: glm-5.2 (1M-context variant: glm-5.2[1m])

Zhipu’s flagship coding model, launched June 13, 2026, is the most capable member of the GLM-5 family and the first in that line to offer MIT-licensed public weights. The 753B-parameter MoE model (approximately 40B active, implied by the 744B-A40B README designation) posts SWE-bench Pro at 62.1% and Terminal-Bench 2.1 at 81.0 — above GLM-5.1’s 58.4% and 62.0 respectively, and within 4 points of Claude Opus 4.8 on Terminal-Bench. Its context window is 1M tokens, a 5x jump from GLM-5.1’s 200K, enabled by IndexShare sparse attention that Zhipu says cuts per-token FLOPs by 2.9x at long context. The architecture also includes an MTP speculative decoding layer. Weights are downloadable today: BF16 at zai-org/GLM-5.2 and FP8 at zai-org/GLM-5.2-FP8 on HuggingFace; deployment is supported via SGLang, vLLM, Transformers, and KTransformers. The hosted API at chat.z.ai uses an Anthropic Messages API-compatible endpoint, so migrating a Claude Code or OpenClaw configuration is a base-URL swap. OpenClaw is one of eight coding agents listed in Zhipu’s official launch materials. For a broader look at the MIT-weight licensing angle, see Zhipu Open-Sources GLM-5.2 Under MIT While Anthropic Tightens Model Access.

Best for: Cost-sensitive agentic coding workloads; teams that want to self-host a frontier-tier MoE without per-token API fees

Context: 1M tokens; max output 128K tokens

Pricing: Z.ai GLM Coding Plan subscription: Lite $18/month (~400 prompts/week), Pro 5x Lite usage, Max 20x Lite ($112/month yearly). Self-hosted via MIT weights incurs hardware cost only.

Trade-off: Opus 4.8 leads on Terminal-Bench 2.1 (85.0 vs 81.0); no per-token API pricing option if you need pay-as-you-go

GPT-5.2 Codex (OpenAI)

Model ID: github-copilot/gpt-5.2 or github-copilot/gpt-5.1-codex

OpenAI’s latest coding-focused models, available through GitHub Copilot integration. GPT-5.2 represents a significant leap in code understanding and generation, with the Codex variants specifically optimized for IDE-style autocomplete and multi-step coding workflows.

Best for: Rapid prototyping, IDE integration, code completion

Context: 400k tokens [Updated March 2026]

For Writing and Content Creation

Claude Sonnet 4.6 (Anthropic) [Updated March 2026]

Model ID: anthropic/claude-sonnet-4-6 (alias: sonnet)

The sweet spot for creative work. Sonnet 4.6 delivers excellent writing quality at a more reasonable price point than Opus. Released February 17, 2026, it’s particularly strong at maintaining tone, structuring long-form content, and creative brainstorming.

Best for: Articles, documentation, creative writing, editing

Context: 1M tokens (beta)

Gemini 3.1 Pro Preview (Google) [Updated March 2026]

Model ID: github-copilot/gemini-3-pro-preview (alias: gemini)

Google’s flagship model, released February 19, 2026, now leads the Artificial Analysis Intelligence Index (score 57.05) and scores 77.1% on ARC-AGI-2. It excels at research-heavy writing, integrates well with Google services, and handles multimodal inputs (text + images) exceptionally well.

Best for: Research summaries, technical writing with visuals, comprehensive reports

Context: 1M tokens [Updated March 2026]

GLM-4.7 (Z.ai) [Superseded by GLM-5.2]

Model ID: nanogpt/zai-org/glm-4.7

According to OpenClaw’s own testing, GLM models perform “a bit better for coding/tool calling” and rival top-tier models for writing and general tasks. GLM-4.7 remains available for users on lower-cost tiers, but the current Z.ai flagship is GLM-5.2 (see above), which offers a 1M-token context window and substantially higher benchmark scores.

Best for: Balanced writing and coding tasks where the GLM-5.2 subscription plan is not needed

Context: ~200k tokens (GLM-4.7 documentation reports approximately 200K–205K tokens; OpenRouter lists 202,752 tokens) [Updated March 2026]

For Reasoning and Analysis

Kimi K2.5 Thinking

Model ID: kimi-coding/kimi-k2-thinking (alias: Kimi K2.5 Thinking)

When you need deep analysis rather than quick answers, the thinking variant of Kimi K2.5 shines. It processes complex problems more thoroughly before responding, making it ideal for architecture decisions, research synthesis, and debugging ambiguous issues.

Best for: Architecture planning, research analysis, debugging complex problems

Context: 256k tokens

Note: Text-only (no image support in thinking mode)

Qwen 3 235B Thinking (Alibaba)

Model ID: nanogpt/qwen/qwen3-235b-thinking

Alibaba’s massive 235B parameter model with explicit thinking capabilities. While the native context window is smaller than frontier models (32k natively, 131k with YaRN scaling), the reasoning quality rivals top western models and it’s particularly strong at mathematical and logical tasks. [Updated March 2026]

Best for: Mathematical reasoning, logic puzzles, structured analysis

Context: 32k tokens natively (131k with YaRN scaling) [Updated March 2026]

Budget-Friendly Options

GPT-5 Mini (OpenAI)

Model ID: github-copilot/gpt-5-mini (alias: gpt-mini)

Significantly cheaper than flagship models while maintaining solid performance for most day-to-day tasks.

Best for: Quick queries, simple tasks, high-volume workflows

Context: 400k tokens [Updated March 2026]

Gemini 3 Flash Preview (Google)

Model ID: github-copilot/gemini-3-flash-preview (alias: gemini-flash)

Google’s speed-optimized model offers near-instant responses with surprisingly good quality. It’s the go-to choice when latency matters more than cutting-edge reasoning.

Best for: Chat interfaces, quick summaries, real-time assistance

Context: 1M tokens [Updated March 2026]

Quick Reference Table

What’s Changed Since February 2026

The model landscape shifted notably in the weeks after this guide was first published. Two developments are worth flagging for OpenClaw users.

GPT-5.4: OpenAI’s March Upgrade [Updated March 2026]

OpenAI released GPT-5.4 on March 5, 2026, effectively superseding GPT-5.2 for most production workflows. The upgrade delivers measurable improvements: 33% fewer factual errors per claim compared to GPT-5.2, record scores on OSWorld-Verified and WebArena-Verified computer-use benchmarks, and a 1M token context window at the API level (matching Claude and Gemini). GPT-5.4 is also the first general-purpose OpenAI model with native computer-use capabilities built in rather than bolted on.

For OpenClaw users, this changes the IDE-integration calculus. GPT-5.4 is available through the GitHub Copilot integration (github-copilot/gpt-5.4), and OpenAI explicitly positions it as the recommended replacement for GPT-5.2. If you’re currently using GPT-5.2 for coding tasks and haven’t switched, the error-rate reduction alone is a practical reason to upgrade your config.

A lightweight variant, GPT-5.4 mini, was released alongside the flagship and inherits the 400k context window of GPT-5 mini at a lower cost tier, making it a direct upgrade path for users running GPT-5 Mini in budget-conscious workflows.

Context Window Parity

One headline trend across this entire table: the 125k–200k context window is no longer a meaningful differentiator. As of March 2026, every major frontier model (Claude Opus and Sonnet 4.6, Gemini 3.1 Pro, Gemini 3 Flash, and GPT-5.4) offers at least 1M tokens of context. Even GPT-5 Mini and GPT-5.2 now provide 400k tokens. The context arms race has effectively resolved at the top, pushing differentiation back toward reasoning quality, latency, pricing, and tool-calling reliability.

For OpenClaw workflows specifically, large context windows matter most when you’re feeding entire codebases or long conversation histories into a single session. With 1M tokens now standard, the practical bottleneck has shifted: it’s less about whether a model can hold your codebase and more about how accurately it reasons about it at scale. For a deeper look at how these benchmark differences translate to real coding tasks, see AI Code Generation Benchmarks 2026: Which Model Actually Writes Better Code?.

Claude Opus 4.7: Anthropic’s April Upgrade [Added May 2026]

Anthropic shipped Claude Opus 4.7 on April 16, 2026, a meaningful enough leap that it changes the coding-tier recommendation in this guide. The headline numbers from independent first-week verdicts:

• SWE-bench Verified: 87.6% (up from 80.8% on Opus 4.6), a 6.8-point absolute improvement on what is widely treated as the most reliable real-world coding benchmark
• Visual acuity: 98.5% with native 3.75-megapixel input, large enough to read most full-resolution screenshots without downscaling
• Adaptive thinking: the model auto-tunes its thinking-budget per query rather than requiring developers to choose between extended-thinking-on and extended-thinking-off modes

For OpenClaw users running Opus 4.6 as the complex-coding tier, the upgrade path is mechanical: switch to anthropic/claude-opus-4-7 (alias: opus). Anthropic has not announced an immediate deprecation of 4.6, but historically the prior version moves to legacy status within 90 days of a successor’s release.

Important nuance for the Sonnet question: there is no Claude Sonnet 4.7. Anthropic has decoupled Opus and Sonnet version numbers. If you are running Sonnet 4.6 for writing or general-purpose work, that branch remains at 4.6 until Anthropic ships a Sonnet successor.

Claude Fable 5: A New Tier Above Opus [Added June 2026]

Anthropic shipped Claude Fable 5 on June 9, 2026, introducing a Mythos-class tier above Opus for the first time. Fable 5 is positioned as “Anthropic’s most capable widely released model,” while Opus 4.8 retains the title of “Anthropic’s most capable Opus-tier model” and is not deprecated. The practical difference for OpenClaw users comes down to price and task profile: Fable 5 costs $10/$50 per million input/output tokens, exactly double Opus 4.8’s $5/$25. That matches Opus 4.8’s fast mode rate, so the question is whether the capability ceiling justifies paying fast-mode prices for standard-latency results. On agentic coding specifically, Fable 5 leads FrontierCode at medium effort and tops CursorBench, though Anthropic has not published numeric scores to compare against Opus 4.8’s SWE-Bench Pro 69.2% figure directly. Both models share the same 1M-token context window and 128k max output. Note that Fable 5 is included on subscription plans only through June 22, 2026; from June 23 it draws usage credits. To update your OpenClaw config: /model claude-fable-5. See also Claude Fable 5 vs Opus 4.8: when the 2x price is worth it for a deeper look at long-running task behavior.

Claude Opus 4.8: The May 2026 Flagship [Updated May 2026]

Anthropic shipped Claude Opus 4.8 on May 28, 2026, superseding Opus 4.7 as the Opus-tier coding model. The upgrade is quality-only: pricing ($5/$25 per million input/output tokens), context (1M tokens), and max output (128k tokens) are unchanged from Opus 4.7. The headline coding result is SWE-Bench Pro at 69.2%, up from Opus 4.7’s 64.3%, with GPT-5.5 at 58.6% and Gemini 3.1 Pro at 54.2% in the same run. Anthropic also reports the model is four times less likely than Opus 4.7 to allow flaws in code, and independently flags more uncertainties rather than asserting claims it cannot support. For OpenClaw users, the migration path is straightforward: update your primary model to anthropic/claude-opus-4-8. Note that GPT-5.5 leads the Terminal-Bench 2.1 ranking (78.2% vs Opus 4.8’s 74.6%), so Opus 4.8 is not uniformly first across every benchmark. For a deeper look at what SWE-Bench Pro and similar coding benchmarks actually test, see SWE-bench Verified Explained: What the Coding Agent Leaderboard Actually Measures (and What It Misses) and AI Code Generation Benchmarks 2026: Which Model Actually Writes Better Code?. Anthropic also ships Opus 4.8 alongside a fast mode ($10/$50 per million tokens, roughly 2.5x the speed) and a Claude Code research preview for dynamic workflows that can run many parallel subagents in a single session.

GLM-5.2: A New Option at the Frontier Tier [Added June 2026]

Zhipu shipped GLM-5.2 on June 13, 2026, and it changes the Z.ai tier in this guide from a budget-adjacent pick to a genuine frontier-tier option. The prior entry in this guide (GLM-4.7) was a reasonable cost-conscious choice with a 200K-token context window; GLM-5.2 extends that to 1M tokens and posts SWE-bench Pro at 62.1% and Terminal-Bench 2.1 at 81.0, both verified figures from Zhipu’s GitHub README. That Terminal-Bench score is 4 points behind Claude Opus 4.8 (85.0) but 19 points ahead of GLM-5.1 (62.0), a substantial generational jump. The key differentiator versus Opus 4.8 and Fable 5 is the licensing model: MIT weights on HuggingFace mean teams with the hardware can run GLM-5.2 at zero per-token cost. The hosted path uses a flat subscription ($18/month Lite, with Pro and Max tiers at 5x and 20x Lite usage) rather than per-token billing, which changes the economics for high-volume coding sessions. The API endpoint is Anthropic Messages API-compatible, so switching in OpenClaw is a base-URL change. To update your config: /model glm-5.2. For a full accounting of what the benchmarks show and what Zhipu has not yet published, see Zhipu Ships GLM-5.2 With 1M Context and MIT Weights, but Zero Benchmarks at Launch.

How to Switch Models in OpenClaw

OpenClaw makes model switching seamless:

# Interactive picker
/model

# Set specific model
/model anthropic/claude-opus-4-8

# Use an alias
/model opus

You can also configure default models and fallbacks in your OpenClaw config:

{
  "agent": {
    "model": {
      "primary": "kimi-coding/k2p5",
      "fallbacks": [
        "anthropic/claude-sonnet-4-6",
        "github-copilot/gemini-3-pro-preview"
      ]
    }
  }
}

Recommendations by Workflow

For Software Developers

1. Primary: Kimi K2.5 (256k context for large codebases)
2. Complex tasks: Claude Opus 4.8 or GLM-5.2 (comparable benchmark tier; GLM-5.2 favors subscription or self-hosted deployments)
3. Maximum-effort tasks: Claude Fable 5
4. Quick help: GPT-5 Mini

For Content Creators

1. Primary: Claude Sonnet 4.6 (excellent tone control)
2. Research-heavy: Gemini 3.1 Pro
3. Fast drafting: Gemini 3 Flash

For Data Analysts

1. Primary: Kimi K2.5 Thinking
2. Math-heavy: Qwen 3 235B Thinking
3. Visualizations: Gemini 3.1 Pro

For Budget-Conscious Users

1. Primary: GPT-5 Mini
2. Fallback: Gemini 3 Flash
3. Occasional heavy lifting: Kimi K2.5

The Bottom Line

The “best” model depends entirely on your workflow and cost tolerance. For most OpenClaw users, Kimi K2.5 hits the sweet spot of capability, context size, and cost. When facing genuinely difficult problems within the Anthropic lineup, Claude Opus 4.8 remains the Opus-tier standard, with SWE-Bench Pro at 69.2% and a four-times reduction in code-flaw rate versus its predecessor. Claude Fable 5 sits above Opus 4.8 as Anthropic’s most capable widely released model, leading FrontierCode and CursorBench among frontier models, but at $10/$50 per million tokens it costs twice as much as Opus 4.8, so reserve it for tasks where the ceiling genuinely matters. GLM-5.2 (June 2026) enters this tier as a competitive alternative: 62.1% on SWE-bench Pro and 81.0 on Terminal-Bench 2.1, with a 1M-token context window and MIT-licensed weights that allow self-hosting at hardware cost only. Teams running high-volume coding sessions on a subscription budget or with on-prem hardware should evaluate it alongside Opus 4.8 rather than treating it as a second-tier pick. For writing and creative work, Claude Sonnet 4.6 offers the best balance of quality and affordability.

OpenClaw’s model switching makes it trivial to experiment. Try different models for different tasks and find what works best for your specific needs. OpenClaw’s Gateway architecture is what makes this flexibility possible. See also GitHub Copilot vs Cursor vs Claude Code (2026 AI Coding) for how these models compare in different editor environments.

Last updated: June 19, 2026. Model availability and pricing subject to change.