Claude Opus 4.8 vs Codex 5.5 Pro
Two frontier agentic-coding stacks, one hard choice: Anthropic's flagship reasoning model vs OpenAI's Codex agent running GPT-5.5 Pro.
Opus 4.8 wins on multi-file agentic coding, tool orchestration, and long-running autonomy; Codex/GPT-5.5 wins on terminal-first workflows, raw reasoning benchmarks like ARC-AGI-2, and token efficiency per task.
At a glance
Claude Opus 4.8
Anthropic
Codex (GPT-5.5 Pro)
OpenAI
How they compare
Claude Opus 4.8 is Anthropic's flagship Opus-class model, tuned for long-running autonomous agents, multi-service codebase work, and enterprise knowledge tasks. Codex is OpenAI's coding agent product, and as of mid-2026 it runs on GPT-5.5, with the higher-accuracy GPT-5.5 Pro variant available for the hardest problems. Both ship 1M-token context on their raw APIs, both support parallel sub-agent style workflows, and both are priced at the premium end of the market. The practical difference shows up in behavior: Opus 4.8 plans, questions its own assumptions, and self-corrects mid-task; Codex/GPT-5.5 follows instructions with near-literal precision and leans on terminal/CLI workflows.
What each one costs
Pricing varies by provider and tier. The right pick depends on whether you pay per token, per subscription, or self-host.
Claude Opus 4.8
Unchanged from Opus 4.7. US-only inference available at 1.1x.
Codex (GPT-5.5 Pro)
API access to GPT-5.5 rolled out shortly after the April 2026 ChatGPT/Codex launch.
Real-world cost: A 100K-output-token agentic coding task: Opus 4.8 standard ≈ $2.50; GPT-5.5 standard ≈ $3.00; GPT-5.5 Pro ≈ $18.00. Token efficiency narrows these gaps in practice — GPT-5.5 typically needs fewer output tokens per completed Codex task than its predecessor, and Opus 4.8 needs 15% fewer passes on GDPval-AA than Opus 4.7.
Head-to-head numbers
Cross-lab benchmark methodology differs, so treat these as directional. The chart below shows each model's published scores on shared benchmarks.
Figures compiled from Anthropic's Opus 4.8 system card, OpenAI's GPT-5.5 launch materials, and independent trackers (Vellum, llm-stats, buildfastwithai). Cross-lab benchmark methodology differs, so treat these as directional, not lab-audited apples-to-apples scores.
Side-by-side capabilities
Key capabilities
Claude Opus 4.8
- —1M-token context at flat pricing
- —Effort control: low/high/extra/max
- —Dynamic workflows: parallel subagent orchestration (research preview)
- —Mid-task system prompt updates without breaking prompt cache
- —Vision, tool use, computer use, JSON schema output
Codex (GPT-5.5 Pro)
- —400K context in Codex, 1M via raw API
- —Fast Mode for latency-sensitive pipelines
- —Deep IDE, CLI, and GitHub integration
- —Multi-hour autonomous cloud task execution
- —Adjustable reasoning effort per request
Pros & cons
Claude Opus 4.8
Pros
- +Highest SWE-bench Pro score among frontier models (69.2%)
- +Best-in-class agentic reliability over very long tasks
- +New dynamic workflows feature spins up parallel subagents for codebase-scale work
- +Strong honesty/self-correction gains vs prior Opus versions
- +1M context at flat standard pricing, no long-context surcharge
Cons
- −Premium pricing vs mid-tier models like Sonnet 5 or GLM-5.2
- −GPT-5.5 still wins on terminal/CLI-specific benchmarks
- −Higher effort settings can significantly increase token spend
Codex (GPT-5.5 Pro)
Pros
- +Leads on Terminal-Bench 2.0, OSWorld-Verified, and long-context MRCR v2
- +Uses ~40% fewer output tokens per Codex task than GPT-5.4, partly offsetting the price hike
- +Extremely literal, predictable instruction-following — good for strict specs and test-driven work
- +Bundled into familiar ChatGPT/Codex subscription tiers ($20–$200/month)
Cons
- −No product literally called 'Codex 5.5 Pro' — naming is shorthand for GPT-5.5 Pro in Codex, which can confuse buyers
- −GPT-5.5 Pro API pricing ($30/$180) is the most expensive tier in this comparison
- −Trails Opus 4.8 on SWE-bench Pro, SWE-bench Verified, and MCP-Atlas tool orchestration
- −Some developers report over-literal execution that technically satisfies instructions while missing intent
Who should use which
Reach for 4.8 if you're…
- —Long-running autonomous engineering agents (Claude Code, Devin, Cognition)
- —Multi-service codebase migrations and refactors
- —Enterprise knowledge work: legal, finance, analysis with heavy tool use
- —Teams that value self-correction and flagged uncertainty over blind confidence
Reach for Pro) if you're…
- —Terminal-first, CLI-driven coding workflows
- —Teams already inside the ChatGPT/Codex subscription ecosystem
- —Tasks needing very literal instruction-following (test suites, strict specs)
- —Scientific research and computer-use tasks where GPT-5.5 leads benchmarks
Where to use
What developers actually say
Pulled from public threads, not submitted testimonials — paraphrased, with the source linked so you can read the full context.
Hacker News
mixedOne developer said heavier agentic tuning has, in their experience, made recent models worse at collaborative, assisted coding rather than fully autonomous runs.
Read the threadHacker News
mixedA developer described Codex as extremely literal — it follows instructions to the letter, sometimes to a comically counterproductive degree, unlike Claude's tendency to infer intent.
Read the threadHacker News (Ask HN)
negativeA user compared an earlier, more reliable Codex experience to working with a senior engineer, versus a newer version that felt like an overconfident junior burning tokens.
Read the threadCommon questions
Is there really a model called 'Codex 5.5 Pro'?
Not exactly. Codex is OpenAI's coding product; the model powering it is GPT-5.5, with a higher-accuracy GPT-5.5 Pro tier. 'Codex 5.5 Pro' is commonly used shorthand for that pairing.
Which is cheaper, Opus 4.8 or Codex/GPT-5.5?
Opus 4.8 ($5/$25 per million tokens) is cheaper than GPT-5.5 ($5/$30) on output pricing, and far cheaper than GPT-5.5 Pro ($30/$180).
Which model is better for terminal-based coding agents?
GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs Opus 4.8's field), making Codex the stronger default for CLI-first agentic workflows.
Which model is better for large, long-running refactors?
Claude Opus 4.8 leads on SWE-bench Pro and introduces dynamic workflows for spinning up parallel subagents across large codebases, making it the stronger pick for long-horizon engineering.
So which one do you actually pick?
Opus 4.8 wins on multi-file agentic coding, tool orchestration, and long-running autonomy; Codex/GPT-5.5 wins on terminal-first workflows, raw reasoning benchmarks like ARC-AGI-2, and token efficiency per task.