Claude Opus 4.8 and GPT-5.5 are the two most capable coding models from Western AI labs as of May 2026. Both cost $5 per million input tokens. Both target developers building with AI. But they take different approaches and excel at different things.

Opus 4.8 leads on agentic coding benchmarks by a wide margin. GPT-5.5 has a stronger native CLI tool (Codex) and higher raw Terminal-Bench scores with that harness. This guide breaks down where each model wins so you can pick the right one for your workflow.

Head-to-head benchmarks

*GPT-5.5’s 83.4% Terminal-Bench score uses the Codex CLI harness, which is purpose-built for that benchmark. On the standard Terminus-2 harness used for all models, GPT-5.5 scores 72.1%.

The pattern is clear: Opus 4.8 wins on coding benchmarks by a significant margin. The 10.6-point gap on SWE-bench Pro is not close — it means Opus 4.8 resolves substantially more real-world coding problems.

Pricing comparison

Opus 4.8 is 17% cheaper on output ($25 vs $30 per million tokens). It also offers a fast mode at $10/$50 that trades some quality for 2.5× speed — useful for latency-sensitive applications.

Agentic coding: where Opus 4.8 dominates

The 10.6-point SWE-bench Pro gap is the most important number in this comparison. SWE-bench Pro measures a model’s ability to resolve real GitHub issues end-to-end — reading the issue, understanding the codebase, writing the fix, and verifying it works.

Opus 4.8’s advantages in agentic coding:

• Better self-correction: 4× less likely to let flawed code pass without flagging it
• More efficient tool calling: Fewer steps for the same intelligence (confirmed by Cursor and Devin)
• Dynamic workflows: Can spawn hundreds of parallel subagents for large-scale tasks
• Longer coherent sessions: Maintains context better over 1,000+ tool calls

GPT-5.5’s advantages:

• Codex CLI integration: Purpose-built terminal agent with 83.4% Terminal-Bench score
• Assistants API: Persistent threads with file storage and code interpreter
• Ecosystem: Deeper integration with GitHub Copilot, Azure, and Microsoft tools

Terminal and CLI work

This is where the comparison gets nuanced. GPT-5.5 scores 83.4% on Terminal-Bench — but only with the Codex CLI harness. On the standard harness, it scores 72.1%, which is lower than Opus 4.8’s 74.2%.

What this means: if you use Codex CLI as your primary coding tool, GPT-5.5 has an edge in that specific environment. If you use Claude Code, Aider, Continue, or any other tool, Opus 4.8 is the better model.

Honesty and reliability

This is Opus 4.8’s strongest differentiator. Anthropic’s evaluations show it is four times less likely than its predecessor to produce flawed code without acknowledging the issue. Multiple enterprise testers confirmed this:

• Devin: “Fixes the comment-verbosity and tool-calling issues we saw with Opus 4.7”
• Cursor: “Exceeds prior Opus models across every effort level”
• Harvey (legal AI): “First model to break 10% on the all-pass standard”

GPT-5.5 does not have equivalent honesty benchmarks published. In practice, GPT-5.5 is more likely to confidently produce code that looks correct but has subtle bugs — a known issue with the GPT family that has not been specifically addressed.

Real-world cost comparison

For a typical developer workload (8 hours of coding agent use per day):

Metric	Claude Opus 4.8	GPT-5.5
Tokens per hour (typical)	~200K in + 50K out	~250K in + 70K out
Cost per hour	~$2.25	~$3.35
Cost per day (8hr)	~$18	~$27
Monthly cost	~$400	~$590

Opus 4.8 is cheaper per hour for two reasons: lower output pricing ($25 vs $30) and more efficient tool calling (fewer tokens per task).

Feature comparison

Opus 4.8 has more features. Dynamic workflows alone is a significant capability that GPT-5.5 has no equivalent for — the ability to spawn hundreds of parallel agents for codebase-scale work.

When to choose Claude Opus 4.8

• Complex agentic coding tasks (multi-file, multi-step)
• Projects where reliability matters more than speed
• Large-scale migrations or refactoring (dynamic workflows)
• When you need the model to catch its own mistakes
• Computer use and browser automation
• Cost-sensitive production workloads (17% cheaper on output)

When to choose GPT-5.5

• You are already invested in the OpenAI ecosystem (Copilot, Azure, Assistants API)
• Your workflow centers on Codex CLI specifically
• You need the Assistants API features (persistent threads, file storage)
• Your team is more familiar with OpenAI’s API patterns
• You need DALL-E, Whisper, or other OpenAI-specific services alongside coding

The budget alternative

Both models cost $5+ per million input tokens. If cost is your primary concern, Chinese models like DeepSeek V4-Pro and MiMo V2.5 Pro offer competitive coding performance at $0.435/$0.87 per million tokens — 30-60× cheaper. See our migration guide for how to switch.

FAQ

Which is better for autonomous coding agents?

Claude Opus 4.8. The 10.6-point SWE-bench Pro lead, better self-correction, and dynamic workflows make it the clear choice for agents that run unattended.

Which is faster?

GPT-5.5 has slightly lower latency for standard requests. Opus 4.8’s fast mode (2.5× speed at $10/$50) can match or beat GPT-5.5’s latency when enabled.

Can I use both?

Yes. Both use standard chat completion APIs. You can route different task types to different models. Use Opus 4.8 for complex coding and GPT-5.5 for tasks that benefit from the OpenAI ecosystem.

Which has better context handling at 1M tokens?

Both support 1M token contexts. Opus 4.8 has better long-context retrieval scores in Anthropic’s evaluations, but real-world differences are minimal for most coding tasks.

Is the SWE-bench Pro gap real?

Yes. 69.2% vs 58.6% is a large, reproducible difference. It means Opus 4.8 successfully resolves about 10% more real GitHub issues than GPT-5.5 in controlled testing. This translates directly to fewer failed attempts and less wasted tokens in production.

What about Gemini 3.5 Flash as a cheaper alternative?

Gemini 3.5 Flash costs $0.15/$0.60 per million tokens — 33× cheaper than Opus 4.8. It scores lower on coding benchmarks (54.2% SWE-bench Pro) but wins on some tool-use tasks. See our Opus 4.8 vs Gemini 3.5 Flash comparison for the full breakdown.