The AI Brain Showdown: Gemini 3.1 Pro Just Dropped. Which Model Runs the Best OpenClaw Agent?
Gemini 3.1 Pro scored 69.2% on MCP Atlas — the benchmark built to test exactly what OpenClaw does. Claude Opus 4.6 is still the official recommendation. We break down five benchmarks, five models, and which configuration wins for your actual workflow.
The AI Brain Showdown: Gemini 3.1 Pro Just Dropped. Which Model Runs the Best OpenClaw Agent?
Two days ago, Anthropic shipped Claude Sonnet 4.6. Yesterday, Google shipped Gemini 3.1 Pro. The benchmark that actually matters for OpenClaw users shows a surprising winner — and it’s creating a genuine debate.
Agent Infrastructure Series · Part 11 | Research date: February 19, 2026
Two days ago — February 17 — Anthropic released Claude Sonnet 4.6.
Yesterday — February 19 — Google released Gemini 3.1 Pro.
The benchmark table Google published alongside the release has been circulating widely. One row in particular stopped OpenClaw users mid-scroll: MCP Atlas.
MCP Atlas is a benchmark built by Scale AI’s research team (arxiv 2602.00933). It uses 36 real MCP servers, 220 tools, and 1,000 tasks specifically designed to evaluate how well an AI model can discover, select, and orchestrate multi-step tool calls across multiple servers — without being told which tools to use upfront.
That’s not an abstract definition. That’s exactly what OpenClaw does every time you run a Skill.
Gemini 3.1 Pro scored 69.2% on MCP Atlas. Claude Opus 4.6 scored 59.5%.
And yet, the OpenClaw official documentation’s recommended default configuration is still:
{ "model": { "primary": "anthropic/claude-opus-4-6" } }
Both of these things are true at the same time. Here’s why — and what it means for how you should configure your agent today.
First: Most Benchmarks Are the Wrong Question
Before comparing models, we need to establish what to measure. The standard AI benchmark circuit — Humanity’s Last Exam, GPQA Diamond, MMLU — tests knowledge recall and reasoning about academic topics. For a general-purpose chatbot, these matter. For an OpenClaw agent managing your email, calendar, GitHub repos, and browser, they’re almost irrelevant.
The benchmarks that actually predict agent performance:
| Benchmark | What it tests | OpenClaw relevance |
|---|---|---|
| MCP Atlas | Cross-server tool discovery, selection, and multi-step orchestration (36 real MCP servers, 220 tools) | ★★★★★ This is literally what OpenClaw Skills do |
| APEX-Agents | Long-horizon, multi-step professional tasks | ★★★★★ Real-world agent workflows |
| τ2-bench | Tool-use stability in retail and telecom simulations | ★★★★★ Production reliability |
| GDPval-AA Elo | Expert-task ELO across high-value knowledge work | ★★★★ Composite professional performance |
| BrowseComp | Agentic web search with multi-hop reasoning | ★★★★ Browser and Search Skills |
| Terminal-Bench 2.0 | Terminal command execution accuracy | ★★★★ System administration Skills |
| SWE-Bench Verified | Single-attempt code bug repair | ★★★ Coding Skills (useful, not primary) |
| ARC-AGI-2 | Novel abstract logic patterns | ★★★ Complex planning tasks |
| GPQA Diamond / MMLU | Graduate-level knowledge recall | ★★ OpenClaw isn’t taking exams |
With that filter, here’s how the contenders actually compare.
The Contenders
Gemini 3.1 Pro — The New Challenger
Released yesterday (February 19), Gemini 3.1 Pro is Google’s upgraded core reasoning layer — the intelligence that powers Gemini Deep Think, now rolling out to developers through the Gemini API, Vertex AI, and Google AI Studio.
Where it leads:
- MCP Atlas: 69.2% — highest of all models tested, nearly 10 points ahead of Claude Opus 4.6 (59.5%)
- APEX-Agents: 33.5% — highest of all models tested
- SWE-Bench Verified: 80.6% — effectively ties Claude Opus 4.6 (80.8%) on coding reliability
- BrowseComp: 85.9% — highest of all models tested (all models benchmarked with tool-assisted browsing: search + Python + browse)
- ARC-AGI-2: 77.1% — more than double Gemini 3 Pro’s 31.1%, well ahead of Opus 4.6 (68.8%)
- 1M token context window — matches Claude’s context scale; no Context Compaction API
Where it falls short:
- GDPval-AA Elo: 1317 — 300+ Elo points behind Claude Sonnet 4.6 (1633) and Opus 4.6 (1606) on expert professional tasks evaluated by human raters
- SWE-Bench Pro: 54.2% — behind GPT-5.3-Codex (56.8%)
- Humanity’s Last Exam (with tools): 51.4% — behind Opus 4.6 (53.1%)
- Pricing: $2 per million input tokens, $12 per million output tokens (standard, ≤200K context); $4/$18 above 200K — same rate as Gemini 3 Pro, now at 2x+ the reasoning performance
How to use it in OpenClaw:
export GEMINI_API_KEY="your-google-ai-studio-key"
openclaw models set google/gemini-3.1-pro-preview
Claude Opus 4.6 — The Incumbent
Released February 5, Claude Opus 4.6 is what OpenClaw’s official documentation recommends and what most ClawHub developers have been debugging their Skills against for weeks.
Where it leads:
- SWE-Bench Verified: 80.8% — highest of all models
- Humanity’s Last Exam (with tools): 53.1% — highest of all models
- τ2-bench Telecom: 99.3% — tied for highest with Gemini 3.1 Pro (also 99.3%)
- GDPval-AA Elo: 1606 — second overall, behind only Claude Sonnet 4.6
Where it falls short:
- MCP Atlas: 59.5% — nearly 10 percentage points behind Gemini 3.1 Pro on the benchmark most aligned with OpenClaw’s architecture
- Cost: $5 per million input tokens, $25 per million output tokens (standard, up to 200K context). When tasks exceed 200K tokens, pricing shifts to $10/$37.50 — applied to all tokens in the request, not just the excess
Key new features (February 5 release):
- 1M token context window (beta): The first Opus-class model to reach this scale. Access requires meeting Anthropic tier requirements
- Context Compaction API (beta): Automatically summarizes older conversation segments as sessions approach context limits, enabling long-running agent tasks without manual interruption — a capability Gemini 3.1 Pro currently does not have
- Agent Teams (alpha): Multiple specialized sub-agents running in parallel (frontend/backend/testing simultaneously), available in Claude Code v2.1.32+ and the Cowork platform
- Adaptive Thinking (4 levels): Automatically adjusts reasoning depth — low/medium/high/max — to control token consumption on simpler tasks
- 128K output tokens: Doubled from the previous generation
How to use it in OpenClaw:
openclaw models set anthropic/claude-opus-4-6
Claude Sonnet 4.6 — The Hidden Standout
Released February 17, Sonnet 4.6 contains the benchmark result most people find genuinely surprising:
GDPval-AA Elo: 1633 — the highest score of any model in the comparison.
This isn’t a niche measurement. GDPval-AA evaluates performance on high-value professional tasks — the kind of knowledge work where errors have real consequences. Claude Sonnet 4.6 outranks Claude Opus 4.6 (1606), GPT-5.2 (1462), and Gemini 3.1 Pro (1317) on this measure.
It also beats Gemini 3.1 Pro on τ2-bench Retail (91.7% vs 90.8%) and ties on MRCR v2 long-context retrieval (84.9%). In internal testing, Claude Code users preferred Sonnet 4.6 over Opus 4.5 in 59% of head-to-head comparisons.
Pricing is unchanged from Sonnet 4.5: $3 per million input tokens, $15 per million output tokens — 60% the cost of Opus 4.6 at standard rates, and about one-fifth the cost when long-context pricing applies. Like Opus 4.6, Sonnet 4.6 also includes the 1M token context window (beta), Context Compaction API, and Adaptive Thinking.
How to use it in OpenClaw:
openclaw models set anthropic/claude-sonnet-4-6
GPT-5.3-Codex — The Coding Specialist
GPT-5.3-Codex belongs in a separate category from the general-purpose agent discussion. It’s a specialist:
- SWE-Bench Pro: 56.8% — highest of all models, beating Gemini 3.1 Pro (54.2%)
- Terminal-Bench 2.0: 77.3% — highest on OpenAI’s Codex harness (self-reported); on the standard Terminus-2 harness, Gemini 3.1 Pro leads at 68.5%
- APEX-Agents: 23.0% — lowest of all models tested
For OpenClaw workflows centered on code — automated debugging, refactoring, CI/CD pipeline management — Codex 5.3 is worth evaluating. For general agent orchestration, it’s not the right choice.
How to use it in OpenClaw:
openclaw onboard --auth-choice openai-codex
openclaw models set openai-codex/gpt-5.3-codex
Kimi K2.5 — The Cost Disruptor
Not in the official benchmark table, but worth knowing: Kimi K2.5 from Moonshot AI currently ranks #1 on tool-selection tasks in the OpenRouter agent leaderboard, and usage has surged sharply this week. OpenClaw’s official documentation includes native support:
openclaw models set moonshot/kimi-k2.5
For cost-sensitive workflows — especially those with Chinese-language context — Kimi K2.5 offers competitive agent performance at a fraction of Claude’s API cost. It’s the fastest-growing model among Chinese-language OpenClaw deployments right now.
Five Benchmarks, Side by Side
| Benchmark | Gemini 3.1 Pro | Opus 4.6 | Sonnet 4.6 | GPT-5.3-Codex | Winner |
|---|---|---|---|---|---|
| MCP Atlas (tool orchestration) | 69.2% | 59.5% | 61.3% | — | 🏆 Gemini |
| APEX-Agents (long-horizon) | 33.5% | 29.8% | — | 23.0% | 🏆 Gemini |
| GDPval-AA Elo (expert tasks) | 1317 | 1606 | 1633 | — | 🏆 Sonnet |
| τ2-bench Retail (tool reliability) | 90.8% | 91.9% | 91.7% | — | 🏆 Opus |
| BrowseComp (agentic search) | 85.9% | 84.0% | 74.7% | — | 🏆 Gemini |
| SWE-Bench Pro (coding) | 54.2% | — | — | 56.8% | 🏆 Codex |
Gemini 3.1 Pro wins 3 of 5 core agentic benchmarks. Claude Sonnet 4.6 tops the expert task ELO. Claude Opus 4.6 leads on tool reliability. GPT-5.3-Codex owns coding. No single model wins everything — and the right answer depends on which benchmarks match your actual OpenClaw workflow.
Which Model for Which Workflow?
| OpenClaw Use Case | Recommended Model | Core Reason |
|---|---|---|
| Email triage + calendar management (gog, mail Skills) | Sonnet 4.6 | GDPval-AA 1633 (global #1), strong on professional tasks, 40% cheaper than Opus |
| Complex cross-system automation (10+ step chains) | Gemini 3.1 Pro | MCP Atlas 69.2%, built for cross-server multi-step orchestration |
| Long-running projects + persistent memory (SOUL.md, para-second-brain) | Opus 4.6 | Context Compaction API + 1M window = sessions that survive hours of tool calls |
| Browser automation + intelligence gathering | Gemini 3.1 Pro | BrowseComp 85.9%, highest agentic search score |
| Code debugging / development sprints | GPT-5.3-Codex or Opus 4.6 | Codex for raw repair accuracy (self-reported); Gemini and Opus essentially tied on SWE-Bench Verified (80.6% vs 80.8%) |
| Daily lightweight tasks, high-frequency chat | Sonnet 4.6 | Best cost-to-performance ratio — ~$0.90 per 100 complex steps |
| Budget-constrained, Chinese-language workflows | Kimi K2.5 | #1 tool selection on OpenRouter leaderboard, fraction of Claude’s cost |
| Zero budget / privacy-first | Gemini 2.5 Flash (free) or Ollama | 1,500 free requests/day; fully local alternatives via Ollama |
Cost at a glance (100-step complex workflow):
| Model | Estimated cost | Notes |
|---|---|---|
| Gemini 2.5 Flash | $0 (free tier) | 1,500 req/day via Google AI Studio |
| Kimi K2.5 | ~$0.03 | Moonshot API |
| Sonnet 4.6 | ~$0.90 | $3/$15 per M tokens |
| Gemini 3.1 Pro | ~$0.60 | $2/$12 per M tokens (≤200K); $4/$18 above 200K |
| Opus 4.6 | ~$3.60 | Triggers long-context rate above 200K tokens |
Why Is the Community Still Using Claude?
The real question: if Gemini 3.1 Pro leads on MCP Atlas — the benchmark most relevant to OpenClaw’s architecture — why hasn’t the community switched?
Reason 1: Standardized benchmarks vs. production Skill quality
MCP Atlas tests models against 36 well-structured, schema-compliant MCP servers. OpenClaw’s 3,286 community Skills vary enormously — some SKILL.md files have vague tool descriptions, incomplete error handling, and non-standard formatting. Claude handles malformed tool calls with higher tolerance and better recovery. Gemini’s higher benchmark scores assume clean, well-formed inputs. In production, a model’s ability to recover from bad inputs often matters more than its score on well-formed ones.
Reason 2: The ecosystem was built around Claude’s behavior
Thousands of ClawHub Skills were developed and debugged against Claude’s specific tool-calling conventions, response patterns, and error recovery sequences. Switching models isn’t just changing a config value — it’s recalibrating how your entire Skills stack behaves. That’s a real migration cost that benchmark numbers don’t capture.
Reason 3: Context Compaction API is a meaningful practical moat
Both models now have 1M token context windows. But Claude Opus 4.6 (and Sonnet 4.6) include Context Compaction API — which automatically summarizes older conversation as sessions approach the limit, enabling indefinitely long agent runs without manual restarts. For OpenClaw sessions running for hours across hundreds of tool calls, this is a capability Gemini 3.1 Pro currently does not have.
The bottom line: Gemini 3.1 Pro is the most compelling model to test right now — especially for cross-system automation and browser workflows. But “it scores higher on this benchmark” and “it will perform better in your specific OpenClaw setup” are different claims. Test it on your actual workflows before deciding.
How to Switch Models in OpenClaw
OpenClaw uses provider/model notation for all LLM references. Switching is a single command:
# See current model
openclaw models list
# Switch to Gemini 3.1 Pro (set GEMINI_API_KEY from Google AI Studio first)
export GEMINI_API_KEY="your-key"
openclaw models set google/gemini-3.1-pro-preview
# Switch back to Claude Opus 4.6 (official recommended default)
openclaw models set anthropic/claude-opus-4-6
# Switch to Sonnet 4.6 (better cost efficiency)
openclaw models set anthropic/claude-sonnet-4-6
# Switch to GPT-5.3-Codex (OAuth login required)
openclaw onboard --auth-choice openai-codex
openclaw models set openai-codex/gpt-5.3-codex
# Kimi K2.5 (cost-sensitive / Chinese-language)
openclaw models set moonshot/kimi-k2.5
# Fully local model via Ollama (free, private)
openclaw models set ollama/qwen3.5
Or set it in your config file (~/.openclaw/openclaw.json):
{
"agents": {
"defaults": {
"model": {
"primary": "google/gemini-3.1-pro-preview"
}
}
}
}
One important note: OpenClaw does not currently support automatic per-task model routing in a single config — there’s no built-in way to say “use Gemini for browser tasks, Claude for reasoning tasks” automatically. Power users achieve this by running multiple OpenClaw instances with different model configurations, coordinated via the Agent2Agent protocol. For most users: pick one model and benchmark it against your actual workflow.
If You Don’t Want to Deal with Any of This: TinyClaw
Here’s a fair description of the situation: six model contenders, ten relevant benchmarks, different winners in different scenarios, API keys to manage, context pricing thresholds to track, and a major new model release every eleven days.
Most OpenClaw users don’t want to manage this constantly. They want an agent that works.
TinyClaw handles the model decision for you:
- 60-second deployment — OpenClaw running in under a minute, no Node.js setup
- Smart model recommendation — recommends the best model for your workflow based on actual usage patterns
- One-click model switching — Gemini 3.1 Pro launched yesterday; TinyClaw already supports it
- Cost controls — built-in usage dashboard with monthly budget caps
The model landscape changes every eleven days. TinyClaw tracks it so you don’t have to.
→ tinyclaw.dev · Free to start · Your agent running in 60 seconds
The Bigger Picture
Gemini 3.1 Pro: February 19. Claude Sonnet 4.6: February 17. Claude Opus 4.6: February 5. Days between the last three major releases: eleven.
This pace means your OpenClaw configuration has a shorter shelf life than it used to. The model that’s optimal today has a real chance of being suboptimal by next month.
The practical response isn’t to re-evaluate every benchmark table as it comes out. It’s to understand which three or four benchmarks actually predict performance in your specific workflow — and know which levers to pull when a better option emerges.
For cross-system automation and browser workflows: test Gemini 3.1 Pro. For expert professional tasks on a budget: Sonnet 4.6. For long-running sessions where context persistence is critical: Opus 4.6 with Context Compaction. For pure code work: GPT-5.3-Codex.
For everyone else: TinyClaw.
Benchmark data: Gemini 3.1 Pro official benchmark table (Google DeepMind, February 19, 2026). MCP Atlas methodology: Scale AI Research, arxiv 2602.00933, scale.com/research/mcpatlas. Pricing: Anthropic official documentation (platform.claude.com/docs/en/about-claude/pricing). OpenClaw model configuration: docs.openclaw.ai/providers and docs.openclaw.ai/concepts/model-providers. Gemini 3.1 Pro pricing: $2/$12 per M tokens (standard, ≤200K); $4/$18 above 200K.
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