Which is best: Claude Opus 4.8, GPT-5.5, or Gemini 3.5 Flash?

There is no overall winner — the three models target different jobs as of late May 2026. Claude Opus 4.8 (released May 28, 2026) leads on agentic coding and complex reasoning (SWE-Bench Pro 69.2%, coding aggregate 76.4), best for code review, multi-file refactors, and reliability-critical unattended agents. GPT-5.5 leads on terminal-coding benchmarks and OpenAI's Expert-SWE 20-hour task (73.1%), best for long-horizon CI fixers and token-sensitive agent runs. Gemini 3.5 Flash is roughly 4x faster and one-third the cost of Opus 4.8, with a 1M-token context window, best for high-volume, multimodal, and document-heavy workloads. Production teams now route by job rather than picking one model.

How much does each model cost?

As of May 31, 2026: Claude Opus 4.8 standard mode is roughly $15/M input and $75/M output (unchanged from Opus 4.7); Fast Mode is roughly 3x cheaper and 2.5x faster than Opus 4.7 Fast. GPT-5.5 is roughly $1.25–3/M input depending on tier (Mini 80% cheaper, Nano 96% cheaper). Gemini 3.5 Flash is dramatically cheaper — roughly 100x lower input/output cost than Opus 4.8 standard. For a token-heavy 1M-token RAG run, Gemini 3.5 Flash costs cents while Opus 4.8 costs dollars. For a 50K-token code review, Opus 4.8's quality premium often justifies the spend.

Which is best for coding in May 2026?

It splits by coding sub-task. Opus 4.8 leads SWE-Bench Pro at 69.2% (GPT-5.5 58.6%, Gemini lower) and shines on multi-file refactors, code review, security audits, and codebase migrations — Dynamic Workflows let it spawn up to 1,000 parallel subagents in Claude Code. GPT-5.5 wins terminal-coding benchmarks and Expert-SWE 20-hour tasks (73.1%), best for unattended CI fixers and long-horizon agent runs. Gemini 3.5 Flash is structurally advantaged for very large codebases (1M-token context) and cheap enough to brute-force search through whole repos. Many engineering teams now run all three behind a router.

Which has the longest context window?

Gemini 3.5 Flash with 1 million tokens — enough to fit most full codebases, large legal corpora, or hundreds of papers. GPT-5.2 ships 400K total context (272K input plus 128K reasoning/output); GPT-5.5 is 128K. Claude Opus 4.8 is 200K (Sonnet 4.5 has a 1M beta but not Opus). For very large context, Gemini 3.5 Flash wins on raw capacity and cost; Opus 4.8 wins on per-token reasoning quality and is usually paired with a retrieval/agent layer rather than naive long-context.

Quick Answer

Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.5 Flash: May 2026 Guide

Published: May 31, 2026

Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.5 Flash (May 2026)

Claude Opus 4.8 shipped May 28, 2026 with Dynamic Workflows. GPT-5.5 Instant landed May 5. Gemini 3.5 Flash + Spark dropped at Google I/O 2026. The three top US frontier models target meaningfully different jobs as of late May 2026 — here’s the router.

Last verified: May 31, 2026.

TL;DR by use case

Use case	Best pick
Code review, multi-file refactor, codebase migration	Claude Opus 4.8
Long-horizon CI fixer, terminal-coding agent	GPT-5.5
1M-token RAG, document-heavy workflows, video/image multimodal	Gemini 3.5 Flash
High-volume cheap inference	Gemini 3.5 Flash
Reliability-critical unattended agent	Claude Opus 4.8
Personal proactive agent (always-on)	Gemini Spark (built on 3.5 Flash)
General chat / writing / reasoning	Any — pick by ecosystem

Benchmark scorecard (May 30, 2026)

Pulled from Scale Labs SWE-Bench Pro public leaderboard, BenchLM aggregates, LLM-Stats, and Anthropic’s launch numbers:

Benchmark	Opus 4.8	GPT-5.5	Gemini 3.5 Flash
SWE-Bench Pro	69.2%	58.6%	lower
OpenAI Expert-SWE (~20h)	strong	73.1%	lower
Coding aggregate (BenchLM)	76.4	competitive	54.5
HLE (humanity’s last exam)	57.9%	competitive	40.2
Terminal coding	strong	leads	weaker
Multimodal grounded	strong	strong	leads
Context window	200K	128K	1M
Long-context retrieval	strong	strong	leads

Claude Mythos Preview (Anthropic’s next frontier, invite-only) sits at 77.8% SWE-Bench Pro but is not GA — none of these three should be benchmarked against Mythos for production planning.

Pricing (May 31, 2026)

Approximate published rates:

Model	Input	Output	Notes
Opus 4.8 standard	~$15/M	~$75/M	Unchanged from Opus 4.7
Opus 4.8 Fast	~$5/M	~$25/M	~3x cheaper than Opus 4.7 Fast
GPT-5.5	~$1.25–3/M	~$10–15/M	GPT-5.5 Mini 80% cheaper, Nano 96% cheaper
Gemini 3.5 Flash	~$0.15/M	~$0.60/M	Dramatically cheapest of the three

Real-world cost intuition:

1M-token RAG sweep: Gemini 3.5 Flash ~$0.75. Opus 4.8 ~$90. ~100x ratio.
50K-token code review with 5K output: Opus 4.8 ~$1.13. GPT-5.5 ~$0.13. Gemini 3.5 Flash ~$0.01.
Dynamic Workflows full codebase refactor (10M tokens): Opus 4.8 standard ~$900. Fast Mode ~$300. Gemini equivalent is cheaper but quality is materially worse for this job.

Headline: Opus 4.8 is a premium product; pricing makes sense for premium work. Gemini 3.5 Flash is the cheap horse for high-volume.

What each model is actually best at

Claude Opus 4.8 — premium agentic coding & reasoning

Anthropic’s pitch: most reliable agent, sharpest judgment, highest honesty. Concrete strengths:

Dynamic Workflows in Claude Code — spawn up to 1,000 parallel subagents to handle codebase-scale tasks. Demo: 750K-line Zig → Rust migration in 11 days, 99.8% test pass rate.
Multi-file refactors, security audits, code review — top of SWE-Bench Pro.
Unattended agents — flags uncertainty rather than confabulating, which matters when no human is watching.
Financial analysis — significant lift in this domain per Anthropic’s launch numbers.
Microsoft 365 add-ins — Excel, PowerPoint, Word GA; Outlook public beta.

Weaknesses: 200K context (not 1M); standard pricing is the priciest of the three; no native image generation.

GPT-5.5 — long-horizon coding & ChatGPT ecosystem

OpenAI’s pitch: best general intelligence and memory, hardest-working agent for 20-hour task profiles. Strengths:

Terminal-coding benchmarks — leads the field.
OpenAI Expert-SWE (~20h human-equivalent task) — 73.1%.
Memory across conversations — ChatGPT recall of prior sessions is genuinely useful for ongoing work.
Reduced hallucinations — GPT-5.5 Instant cut hallucinated claims by 52.5% on high-stakes prompts vs predecessor.
Tiered pricing — Mini and Nano dramatically cheaper for production.
Codex IDE / Codex CLI / GPT-5.5 Spark — strong coding-product surface.

Weaknesses: smaller context window (128K for 5.5, 400K for 5.2); SWE-Bench Pro behind Opus 4.8; less polished MCP ecosystem than Anthropic.

Gemini 3.5 Flash — speed, scale, and the Google ecosystem

Google’s pitch: fast, cheap, massive context, and deeply integrated. Strengths:

1M-token context — fits most full codebases, large legal corpora, or hundreds of documents.
~4x faster than Opus 4.8 with roughly 1/100th the cost — high-volume work changes from “if budget allows” to “yes, just do it.”
Multimodal ingestion — strong on PDF, audio, video including Chrome-tab inputs in Search.
Gemini Spark — built on 3.5 Flash, Google’s always-on agent ($100/mo AI Ultra, US, May 30, 2026 launch).
Generative UI in Search — custom layouts on the fly.
Antigravity harness — strong agentic platform for the Google stack.

Weaknesses: reasoning and coding quality material behind Opus 4.8 and GPT-5.5 on hard tasks; less mature developer ecosystem than Anthropic / OpenAI; image generation strong but workflow tools less polished than ChatGPT.

The router pattern (what production teams actually do)

By late May 2026 the consensus among teams running these models in production is to route by job:

incoming request
    │
    ├── high-stakes code review / multi-file refactor → Opus 4.8
    ├── long-horizon unattended agent (CI fixer, 8h+ task) → GPT-5.5
    ├── multimodal / 500K+ token context / RAG sweep → Gemini 3.5 Flash
    ├── high-volume cheap classification / extraction → Gemini 3.5 Flash or GPT-5.5 Nano
    ├── chat / writing / general reasoning → user preference (any of the three)
    └── personal proactive automation → Gemini Spark

OpenRouter, Portkey, LiteLLM, and Helicone all ship templated routers for this pattern. Anthropic’s own AI Router post-coding-cost-optimization (covered in earlier May 2026 posts) walks through the economics.

Quick decision tree

Is the workload coding?
├── Yes
│   ├── Multi-file refactor or code review → Opus 4.8
│   ├── Long-horizon unattended → GPT-5.5
│   └── Very large codebase scan → Gemini 3.5 Flash
└── No
    ├── High volume / multimodal / big docs → Gemini 3.5 Flash
    ├── Critical reasoning, financial, agentic → Opus 4.8
    └── General chat with memory → GPT-5.5

What changed in late May 2026 specifically

Three near-simultaneous moves that reshape this comparison:

May 28: Opus 4.8 + Dynamic Workflows + 3x cheaper Fast Mode — Anthropic moved the agentic-coding frontier.
May 28–30: Gemini Spark launch + Gemini Omni preview — Google pushed Gemini from chatbot to platform, deeply integrated with Workspace.
May 5: GPT-5.5 Instant — OpenAI cut hallucinations and pushed personalized memory.

Net: no single winner, three sharper-edged models. Lock into one only if your stack constrains you; otherwise route.

Verdict

Opus 4.8 if quality matters most. GPT-5.5 if length and terminal-agent staying power matters most. Gemini 3.5 Flash if cost or context size matters most. Production teams running serious agents are using all three behind a router — it’s the cheapest path to capturing the best of each model. If forced to pick one in May 2026: Opus 4.8 for engineering-heavy orgs, GPT-5.5 for general-purpose product teams, Gemini 3.5 Flash for cost-sensitive high-volume.

Sources: Anthropic Opus 4.8 launch (May 28, 2026), OpenAI GPT-5.5 Instant launch (May 5, 2026), Google I/O 2026 (May 19–20), BenchLM model comparison, Scale Labs SWE-Bench Pro public leaderboard, LLM-Stats benchmarks, Vasundhara model-war breakdown, Lushbinary cost/value comparison, Hindustan Times coverage (verified May 31, 2026).