OpenRouter model

OpenAI: o1-pro

The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide...

Provider
OpenRouter
Availability
Available
Model ID
openai/o1-pro
Snapshot
2026-10-07
Evaluation Verdict

OpenAI o1 was the foundation of frontier reasoning models, achieving 83.3% on AIME 2024 and 48.9% on SWE-bench Verified through reinforcement-learned internal thinking.

OpenAI: o1-pro Benchmarks & Agentic Accuracy

Verified primary research & developer preference metrics

Primary Sources Verified
SWE-bench Verified
48.9%
Standard ReasoningOpenAI System Card ↗
LiveCodeBench
61.7%
Standard ReasoningLiveCodeBench ↗
LMSYS Coding Elo
1355Elo
Coding ArenaLMSYS Arena ↗
AIME 2024 / 2025
83.3%
Pass@1OpenAI System Card ↗
Pricing ($ / 1M tokens)
$15/ $60
$3.75 cached readStandard API Pricing

What This Means in Real Software Development

  • Pioneered large-scale reinforcement learning for step-by-step reasoning and mathematical deduction.
  • Achieves 75.6% on GPQA Diamond, demonstrating PhD-level scientific reasoning.
  • Deep chain-of-thought reasoning handles complex edge-case analysis before generating output.
  • Priced at $15.00/M input and $60.00/M output, optimal for high-stakes problem deconstruction.

Swarm Multi-Agent Topology

Tier 1: Architectural Deductive PlannerRole: Primary / Planner

Best Paired With: Gemini 3.8 Flash for subagent execution

High-accuracy reasoning prevents early architectural missteps in large software design tasks.

Git Worktree Isolation Advantage:

Analyzes complex codebase dependencies inside read-only worktree sessions.

What Developers & Practitioners Are Saying

Real-world field observations, production strengths, and developer consensus

Field Consensus

"The pioneering chain-of-thought reasoning model from OpenAI. Still revered for deep mathematical proofs, though largely superseded by o3 and o3-mini in speed."

Verified Real-World Strengths

  • Deep multi-step deduction on complex physics and math problems
  • Strong resilience against adversarial prompting and trick questions
  • Solid 48.9% on SWE-bench Verified

Production Caveats & Gaps

  • High latency (no streaming for internal thoughts)
  • Expensive ($15/M input, $60/M output)
  • Slower throughput than modern alternatives
Benchmark Reality Check

What the benchmark tests: Captures raw deliberative compute scaling on difficult PhD-level challenges.

Production caveat: Interactive CLI feel is sluggish; developers prefer o3-mini for daily coding.

Primary Sources & Methodology Citations

OpenAI: o1-pro model overview

OpenAI: o1-pro is listed in the public Swarm catalog through OpenRouter. This page summarizes the verified catalog fields used by Swarm without claiming real-time access for every provider account.

Provider
OpenRouter
Family
OpenRouter
Context window
200K tokens
Thinking support
off
Availability status
Available
Catalog ID
openrouter/openai/o1-pro
  • File input
  • Image input
  • Text input
  • Text output
  • Reasoning
  • Structured output
  • JSON

Run OpenAI: o1-pro in Swarm

Verified CLI workflow & multi-agent architecture

Quickstart Guide →
bash — swarm run
$swarm ctl model set --provider openrouter --model openrouter/openai/o1-pro

# Or switch interactively inside the terminal TUI:

>/models

Subagent Role Matrix

Assign OpenAI: o1-pro to specialized system roles in ~/.swarm/config.json:

  • Primary / Planner: Orchestrates full task DAGs and multi-stage pipelines.
  • Coder Subagent: Generates code and unit tests inside private Git worktrees.
  • Finder Subagent: Performs read-only code search, symbol discovery, and mapping.
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Public catalog snapshot. Catalog availability is not a real-time provider status or an access guarantee.