Claude Opus 4.8 vs GPT-5.5: Which AI Model Wins in 2026?
A data-driven head-to-head of Claude Opus 4.8 and GPT-5.5 — benchmarks, pricing, context, speed, and a use-case-by-use-case verdict.
A data-driven head-to-head of Claude Opus 4.8 and GPT-5.5 — benchmarks, pricing, context, speed, and a use-case-by-use-case verdict.
Claude Opus 4.8 and GPT-5.5 are the two most capable general-purpose models you can buy access to in mid-2026. They are close enough that the right pick depends entirely on what you are building — and the gap that does exist runs in opposite directions depending on whether you care about agentic coding, raw reasoning throughput, multimodal range, or cost per token.
This comparison is structured to settle that decision. Every section below leads with a table, the benchmark numbers are sourced from each lab's published model cards plus independent third-party evaluations, and the verdict at the end maps concrete workloads to a recommended model rather than crowning a single winner.
| Attribute | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | Q2 2026 | Q1 2026 |
| Context window | 1M tokens (standard), 200K default | 400K tokens |
| Max output tokens | 64K | 128K |
| Knowledge cutoff | January 2026 | October 2025 |
| Native modalities | Text, image, PDF, code | Text, image, audio, video, code |
| Extended thinking | Yes (interleaved, tool-aware) | Yes (reasoning effort levels) |
| Best at | Agentic coding, long-context, tool use | Multimodal, math, voice, broad ecosystem |
| API input price | $5 / 1M tokens | $4 / 1M tokens |
| API output price | $25 / 1M tokens | $20 / 1M tokens |
The short read: Opus 4.8 is the better coding and long-context agent; GPT-5.5 is the broader multimodal generalist and is slightly cheaper per token. Both are frontier-class. The detail is where the decision actually lives.
No single benchmark decides this. The two models trade leads across reasoning, coding, math, and multimodal suites. The tables below group results by capability. Figures reflect each lab's reported scores alongside independent reproductions where available; treat single-point benchmark deltas under ~2 points as noise.
| Benchmark | Claude Opus 4.8 | GPT-5.5 | Edge |
|---|---|---|---|
| SWE-bench Verified | 79.2% | 74.6% | Opus 4.8 |
| Terminal-bench (agentic) | 52.4% | 46.1% | Opus 4.8 |
| LiveCodeBench v6 | 74.8% | 76.3% | GPT-5.5 |
| Aider polyglot edit | 84.1% | 80.7% | Opus 4.8 |
| Multi-file refactor (internal) | Strong | Good | Opus 4.8 |
Opus 4.8 is the stronger agentic coder — it holds context across large repositories, makes fewer destructive edits, and recovers from failed tool calls more gracefully. GPT-5.5 edges ahead on isolated competitive-programming-style problems (LiveCodeBench), where single-shot algorithmic reasoning matters more than multi-step repo navigation. For day-to-day work inside a real codebase, the agentic numbers matter more. Our deeper look at coding-specific performance lives in Gemini 3.5 Flash vs Claude Opus 4.7 for coding.
| Benchmark | Claude Opus 4.8 | GPT-5.5 | Edge |
|---|---|---|---|
| GPQA Diamond (science) | 83.6% | 85.1% | GPT-5.5 |
| MMLU-Pro | 88.4% | 89.0% | GPT-5.5 |
| Humanity's Last Exam | 27.3% | 29.8% | GPT-5.5 |
| BBH (Big-Bench Hard) | 92.1% | 91.4% | Opus 4.8 |
| Long-context QA (200K+) | Excellent | Good | Opus 4.8 |
GPT-5.5 has a small but consistent lead on knowledge-dense, single-pass reasoning. Opus 4.8 pulls ahead the moment the task spans a large context window — multi-document synthesis, codebase-wide reasoning, or long transcripts. If your reasoning happens over a big pile of source material, Opus 4.8 is the safer bet.
| Benchmark | Claude Opus 4.8 | GPT-5.5 | Edge |
|---|---|---|---|
| AIME 2025 | 91.2% | 94.5% | GPT-5.5 |
| MATH-500 | 96.1% | 97.0% | GPT-5.5 |
| FrontierMath (hard) | 18.7% | 23.4% | GPT-5.5 |
| Quantitative word problems | Strong | Excellent | GPT-5.5 |
Math is GPT-5.5's clearest win. Across competition math and frontier problem sets it holds a real, repeatable lead. If your workload is quantitative — financial modeling, scientific computation, olympiad-grade problem solving — GPT-5.5 is the default.
| Capability | Claude Opus 4.8 | GPT-5.5 | Edge |
|---|---|---|---|
| Image understanding (MMMU) | 78.9% | 82.3% | GPT-5.5 |
| Document / chart extraction | Excellent | Excellent | Tie |
| Audio input | No native support | Native | GPT-5.5 |
| Video understanding | No native support | Native | GPT-5.5 |
| Voice mode | No | Yes (real-time) | GPT-5.5 |
This is not close. GPT-5.5 is a true omni-model with native audio and video; Opus 4.8 is text-and-vision only. If you need voice agents, video analysis, or real-time audio, GPT-5.5 is the only option of the two. For document, chart, and screenshot understanding, both are excellent and the choice comes down to other factors.
| Spec | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| Max context | 1,000,000 tokens | 400,000 tokens |
| Max output | 64,000 tokens | 128,000 tokens |
| Effective recall at full context | Very high (near-perfect needle retrieval) | High (some mid-context degradation) |
| Extended thinking | Interleaved with tool calls | Reasoning-effort presets (low/med/high) |
| Tool / function calling | Parallel, agentic, MCP-native | Parallel, mature ecosystem |
| Structured output | JSON, tool-schema enforced | JSON mode, strict schemas |
| Prompt caching | Yes (up to 1hr TTL) | Yes (automatic) |
| Fine-tuning | Limited / enterprise | Available |
Opus 4.8's 1M-token window is the headline spec advantage — 2.5x GPT-5.5's ceiling — and its recall across that window is unusually strong, which matters for whole-repository and whole-corpus work. GPT-5.5 counters with double the output ceiling (useful for long-form generation in a single call) and a more mature fine-tuning path.
| Metric | Claude Opus 4.8 | GPT-5.5 | Edge |
|---|---|---|---|
| Output speed (tokens/sec) | ~62 | ~78 | GPT-5.5 |
| Time to first token | ~0.9s | ~0.6s | GPT-5.5 |
| Latency with extended thinking | Higher (deliberate) | Moderate | GPT-5.5 |
| Throughput under load | Stable | Stable | Tie |
GPT-5.5 is the faster model for interactive, latency-sensitive applications — chat UIs, autocomplete, voice. Opus 4.8 trades some speed for deliberation, which is the right tradeoff for agentic and long-context tasks where correctness beats responsiveness but a poor fit for a snappy real-time assistant.
Both labs price per million tokens, split between input and output. GPT-5.5 is modestly cheaper on raw rates, but real cost depends on how much extended thinking and context you burn.
| Cost component | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| Input (per 1M tokens) | $5.00 | $4.00 |
| Output (per 1M tokens) | $25.00 | $20.00 |
| Cached input | $0.50 | $0.40 |
| Batch API discount | 50% | 50% |
| Consumer plan | Claude Pro $20 / Max $100–$200 | ChatGPT Plus $20 / Pro $200 |
A representative agentic task — 50K tokens of context in, 8K tokens of reasoning and output out, run 1,000 times:
| Model | Input cost | Output cost | Total (1,000 runs) |
|---|---|---|---|
| Claude Opus 4.8 | $250 | $200 | $450 |
| GPT-5.5 | $200 | $160 | $360 |
GPT-5.5 lands roughly 20% cheaper at list price on a like-for-like workload. Prompt caching narrows the gap sharply for repeated-context agents — if 80% of your input is cacheable, Opus 4.8's effective input cost drops to about $1.40/1M, and the two models land within a few percent of each other. For high-volume, latency-tolerant batch jobs the difference is rarely the deciding factor.
| If your priority is... | Pick | Why |
|---|---|---|
| Building an AI coding agent | Claude Opus 4.8 | Leads agentic coding and tool-use benchmarks. |
| Whole-codebase or large-doc reasoning | Claude Opus 4.8 | 1M context with near-perfect recall. |
| Voice assistant or audio app | GPT-5.5 | Native audio and real-time voice. |
| Video understanding | GPT-5.5 | Only one of the two with native video. |
| Math, finance, scientific compute | GPT-5.5 | Clear lead on quantitative benchmarks. |
| Autonomous multi-step agents | Claude Opus 4.8 | Reliable parallel tool calls + MCP. |
| Real-time chat / autocomplete | GPT-5.5 | Lower latency, faster output. |
| Long-form writing and editing | Claude Opus 4.8 | Preferred prose quality. |
| Lowest cost at scale | GPT-5.5 | ~20% cheaper before caching. |
| Strict structured-output pipelines | Claude Opus 4.8 | Stronger instruction adherence. |
Benchmarks generalize; your workload does not. Before committing, run the same realistic task through both models and score the outputs on the dimensions you actually care about. The structured eval prompt below is the one we use to compare models on a fixed task.
You are an impartial evaluator comparing two AI model outputs. Task given to both models: [paste the exact task / prompt you ran] Output A (Claude Opus 4.8): [paste output A] Output B (GPT-5.5): [paste output B] Score each output 1-10 on: 1. Correctness — factually and logically right 2. Instruction adherence — followed every constraint 3. Completeness — nothing important missing 4. Usefulness — would a professional ship this 5. Format — matched the requested structure Output a table of scores, a one-line justification per dimension, and a final recommendation with the single deciding factor. Do not favor either model by default. Penalize confident errors hardest.
The teams getting the most out of 2026 frontier models rarely pick one and standardize on it. The common pattern is to route by task: Opus 4.8 for the coding agent and long-context jobs, GPT-5.5 for voice, video, and math-heavy paths, and a cheaper, faster small model for high-volume classification and routing. If you are wiring multiple models behind one interface, an orchestration layer earns its keep quickly — we cover that tooling in our Genspark review.
Claude Opus 4.8 and GPT-5.5 are both frontier-class, and neither is a clean winner across the board. The honest framing is by workload, not by leaderboard:
For the broader field — including how Gemini fits alongside these two — see our three-way comparison of Gemini 3.5 Flash, Claude Opus 4.7, and GPT-5.5 High. And if you have settled on Claude, our 100 best Claude Opus prompts for power users and 50+ Next.js prompts for Claude Opus will get more out of it.
Browse the full PromptsRush blog, our prompt library, and the AI model directory.
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