Claude Opus 5 vs GPT-5.5: Benchmarks, Price, Side-by-Side (2026)

Comparison · August 2026

Claude Opus 5 and GPT-5.5 are the Anthropic and OpenAI flagships teams actually route between in mid-2026. This guide is a decision piece, not a leaderboard dump: live pricing caveats, three workload cost scenarios, public benchmark signals, charts, and our own side-by-side runs on writing, coding, false premises, research caution, and product routing. Multi-model AI means you can keep both. Start in a multi-model AI workspace or on i10X.

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Persona picker

You are…	Start with	Why
Writer / CS / marketer	GPT-5.5 (often); Opus for warm narrative	In our rewrite test, GPT stayed tighter and more subject-line clean; Opus added warmer greeting energy.
Developer / agent builder	Opus default for deep coding; GPT for tool loops	Public SWE-bench Pro / deep-coding writeups often favor Opus; Terminal-Bench / orchestration coverage often favors GPT-5.5. Our micro bug-fix was a tie.
Researcher / analyst	Either (~1M class); GPT slight context edge	Both sit in the million-token class; GPT lists ~1.05M vs Opus 1M at current API list rates.
Budget / high volume API	Claude Opus 5	Same $5/M input, lower output ($25 vs $30) and matched cache ($0.50/M) at current API list rates.
Routing / version-aware defaults	GPT-5.5 for the meta-prompt	In our product-decision test, GPT named both models and built a matrix; Opus deferred on exact “Opus 5 / GPT-5.5” labels.

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What we are comparing (exact versions)

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not vague “Claude vs ChatGPT” brand names and not multimodal-as-in-one-model marketing language.

Field	Claude Opus 5	GPT-5.5
Provider	Anthropic	OpenAI
API model	Claude Opus 5	GPT-5.5
Listed API name	Anthropic: Claude Opus 5	OpenAI: GPT-5.5
App vs API note	Also in Claude.ai / Anthropic API; this article uses the API model above	Also in ChatGPT / OpenAI API; this article uses the API model above
Sibling line	Opus 4.x / 4.8-era coverage still appears in older SERPs; treat this page as the Opus 5 ID	GPT-5.x family; do not confuse with older GPT-4o or GPT-5.0 writeups

If you still see posts comparing Claude Opus 4.x to GPT-5.0 without the Opus 5 / GPT-5.5 labels, treat them as older. For routing across many models, see AI model routing.

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Spec sheet (API pricing, 2026-08-21)

Spec	Claude Opus 5	GPT-5.5
Context window	1,000,000 tokens	1,050,000 tokens
Input modalities (card)	text, image, file	file, image, text
Output	text	text
Cache read / 1M	$0.50	$0.50
Open weights	No	No
Vendor positioning (short)	Frontier Anthropic Opus-class reasoning, coding, long-context agents	Frontier OpenAI flagship for reasoning, tools, and general chat

Structurally these two are close cousins on paper: both million-token class, both text/image/file, both closed weights. The meaningful gaps show up in output price, public coding vs terminal-tool signals, and the live voice tests below.

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Pricing and real workload cost

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-21.

Price	Claude Opus 5	GPT-5.5
Input / 1M tokens	$5.00	$5.00
Output / 1M tokens	$25.00	$30.00
Cache read / 1M	$0.50	$0.50

Same input sticker and same cache read. Opus costs less on output ($25 vs $30). That gap compounds on chatty agents and long answers.

Scenario	Assumed tokens	Est. Claude Opus 5	Est. GPT-5.5
Chat turn	1k in + 0.5k out	$0.0175	$0.020
Repo / doc review	80k in + 4k out	$0.500	$0.520
Agent loop	200k in (50% cached) + 20k out	$1.050	$1.150

Opus is cheaper on every stylized workload above, mostly from the output delta. For subscription stacks (Claude Pro / ChatGPT Plus style plans), see AI subscription stack cost. Always verify live vendor pages before budgeting.

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Performance by job (public signals)

Public benches disagree by harness, effort mode, and date. Treat them as signals. Confirm with your prompts. Language below is verify-live: third-party pages move weekly.

Signal	Claude Opus 5	GPT-5.5	Reading
SWE-bench Pro / deep coding depth	Often leads in 2026 Opus 4.8-era and Opus 5 launch writeups	Competitive, not always first in those writeups	Opus strong for hard repo work; verify your harness
Terminal-Bench / tool orchestration	Strong, but earlier GPT-5.5 vs Opus 4.x coverage often favored GPT	Often leads in those earlier Terminal-Bench style boards	GPT edge on shell/tool loops in cited coverage
Context	1,000,000	1,050,000	Near tie; GPT slight edge on the API model card
Output price	$25 / 1M	$30 / 1M	Opus cheaper at matched $5 input
Modalities (card)	text / image / file	file / image / text	Parity for typical SaaS stacks

Coding and agents

Opus 5 launch coverage and late Opus 4.8-era writeups often put Anthropic ahead on deep software-engineering benches (SWE-bench Pro style). Earlier GPT-5.5 vs Opus 4.x coverage more often gave OpenAI the edge on Terminal-Bench and multi-step tool orchestration. Our empty-list micro-test was a clean tie on correctness, with Opus slightly more thorough in naming an alternate ValueError path. Use that as a caution: one snippet is not a coding crown.

Writing and tone

Benchmarks barely measure voice. That is why we ran the email rewrite below. Expect Opus to sound warmer and more conversational. Expect GPT-5.5 to sound tighter, closer to a clean subject-line / ops-email register.

Long context and files

Both are ~1M class with text/image/file inputs in the API. GPT’s listed 1.05M is a small structural edge. For most diligence packs that fit under 1M, the difference is noise; pick on quality and cost instead.

Job	Edge	Why
Hard / deep coding	Claude Opus 5 (public signals)	SWE-bench Pro / deep-coding writeups often favor Opus
Tool / terminal agents	GPT-5.5 (public signals)	Earlier Terminal-Bench / orchestration coverage often favors GPT
Everyday writing	Split (test tone)	GPT tighter in our rewrite; Opus warmer
Long docs / files	Near tie / slight GPT	1.05M vs 1M; same modality set
Cost at output-heavy volume	Claude Opus 5	$25 vs $30 output / 1M at current API list rates

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Side-by-side test (live API test, 2026-08-21)

We ran the same prompts on Claude Opus 5 and GPT-5.5 side by side in a multi-model workspace (temperature 0.2-0.3). Scores are editorial 1-5 across instruction following, depth, factual caution, style, and usefulness (max 25 per prompt).

Test 1: Client email rewrite

Task: Keep every fact. Warmer. Under 120 words.

Claude Opus 5 (excerpt): Opened with a friendly greeting, then hit the Q3 deck follow-up, missing finance numbers, stakeholder reschedule, and Acme pricing slide. Warm, complete, slightly more conversational padding.

GPT-5.5 (excerpt): Same facts in a cleaner, subject-line style register. Less greeting energy, tighter sentences, still under the word budget.

Edge: GPT for fidelity-plus-tightness (~23/25). Opus for warm human tone (~22/25). If your brand voice hates filler greetings, prefer GPT. If managers want “human warmth,” prefer Opus.

Test 2: Empty-list average bug

Both models correctly named ZeroDivisionError on empty input and proposed a minimal guard. Opus also mentioned a ValueError alternative path, which is more thorough documentation. Correctness: tie 24/24.

Test 3: False premise (Moon cheese)

Both refused the premise first. Opus wrote a longer educational redirect. GPT gave a tighter practical plan for what to research instead. Both pass; style preference only.

Test 4: Invented geography inflation

Both refused Atlantis inflation and asked for a real statistical office / country. Both pass the “do not invent numbers” bar. For trust workflows, still add a second-model check: multi-model hallucination checks.

Test 5: Self-knowledge / version awareness (important)

Asked which model should be the SaaS team default for emails, long PDFs, Python, and structured automation, comparing “Claude Opus 5” vs “GPT-5.5” by name:

• Claude Opus 5: Refused to compare those exact product names. Identity framing sounded like Opus 4.5-era self-description. It would not produce a named matrix for Opus 5 vs GPT-5.5.
• GPT-5.5: Produced a full matrix: Claude for emails / PDF narrative; GPT for Python; switch for structure / automation.

This is a real self-knowledge / version awareness finding, not a scandal. Models often lag their own marketing names in system identity. If your agent asks a model to route work by current flagship labels, GPT-5.5 was usable out of the box in this run; Opus needed a different prompt style (capabilities, not brand strings).

Prompt	Claude Opus 5	GPT-5.5	Note
Email rewrite	22/25	23/25	GPT tighter; Opus warmer
Bug fix	24/25	24/25	Tie; Opus more thorough on ValueError
False premise	24/25	24/25	Both refuse; Opus longer, GPT tighter
Refuse invented stat	24/25	24/25	Both refuse Atlantis rate
Routing / self-knowledge	16/25	23/25	Opus deferred on exact names; GPT built a matrix
Total	110/125	118/125	Gap is mostly the version-awareness prompt; jobs still split

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Ecosystem and where you run them

• Claude Opus 5: Anthropic API / Claude.ai / API access. Strength: Projects, Artifacts-style workflows, and long coding sessions in Anthropic’s product surface.
• GPT-5.5: OpenAI API / ChatGPT / API access. Strength: Assistants/tools ecosystem, Custom GPTs, and broad third-party connector coverage.
• Both in one place: Multi-model workspaces like i10X let you compare the same prompt without two browser profiles. See also the multi-model AI guide.

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Pros, cons, and failure modes

Claude Opus 5

• Pros: Strong deep-coding / SWE-bench Pro signals in 2026 writeups; cheaper output at matched $5 input; warm narrative writing; thorough coding explanations in our micro-test; 1M context.
• Cons: Slightly smaller listed context than GPT-5.5; higher output than mid-tier models (still $25/M); version self-description can lag brand strings in routing prompts.
• Fails when: you ask it to authoritatively rank “Opus 5 vs GPT-5.5” by exact marketing names, or you need the absolute cheapest high-volume generation outside the Opus price band.

GPT-5.5

• Pros: Strong Terminal-Bench / tool-orchestration signals in earlier coverage; tighter ops-email writing in our rewrite; ~1.05M context; clear named routing matrix in our live test; broad ChatGPT ecosystem.
• Cons: 20% higher output price vs Opus at current API list rates ($30 vs $25); deep SWE-bench Pro writeups often still lean Anthropic; can feel colder when you want conversational warmth.
• Fails when: you optimize purely for output-token burn at Opus-class quality, or you need the warmest customer-facing letter without an extra rewrite pass.

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Decision guide: pick one or route both

If you need…	Choose
Output-cheaper high volume at this tier	Claude Opus 5
Tight ops email / subject-line style	GPT-5.5 (A/B once)
Warm customer narrative	Claude Opus 5 (A/B once)
Deep repo / SWE-style coding (per recent writeups)	Start Opus 5; verify on your tools
Terminal / multi-tool agent loops (per earlier boards)	Start GPT-5.5; verify on your tools
Named model routing advice inside the prompt	GPT-5.5 in our 2026-08-21 run; or prompt Opus by capability, not brand
Mixed SaaS week	Both: route deep coding → Opus; tool loops / tight prose → GPT

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-5.5 when you want a short, subject-line clean rewrite without extra greeting padding. In our live test, GPT preserved every operational fact and stayed tighter. Opus produced a warmer letter with greeting energy that some brands love and some reject.

Switch to Opus if your brand voice is deliberately human and managers prefer warm scaffolding.

2) Long PDF / research pack

Near tie. Both sit in the ~1M class with file input. GPT’s 1.05M is a small edge on the API model card. Pick on which model summarizes cleaner for your domain, then use the other as a second-pass critic. Method: side-by-side AI comparison.

3) Everyday Python scripting

Often Claude Opus 5 as the interactive deep-coding partner, especially if you weigh SWE-bench Pro style writeups. Our micro bug-fix was a tie on correctness, with Opus slightly more thorough. For shell-heavy agent loops, bring GPT-5.5 in.

4) Tool orchestration / terminal agents

Often GPT-5.5 per earlier Terminal-Bench and tool-orchestration coverage versus Opus 4.x. Re-verify against Opus 5 on your exact tool schema; boards move.

5) Output-heavy generation at API scale

Better on cost: Claude Opus 5. Matched $5/M input, lower output rate ($25 vs $30), matched $0.50 cache. On our agent-loop estimate, Opus landed $1.050 vs GPT $1.150 per stylized run. That compounds.

6) “Which model should I use?” meta-prompts

Better in our live run: GPT-5.5. It accepted the Opus 5 / GPT-5.5 labels and returned a usable matrix. Opus deferred on exact names. If you still prefer Opus as the worker, hard-code routing rules in your app instead of asking the model to name its rivals.

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Consumer plans vs API (do not mix them up)

SERP pages often blur ChatGPT / Claude subscriptions with API model IDs. Keep them separate:

• API comparison (this article): Claude Opus 5 vs GPT-5.5 in the API.
• Consumer apps: Claude.ai / Anthropic plans vs ChatGPT / OpenAI plans may expose different tool defaults, rate limits, and bundled models (including cheaper mid-tiers).

If your question is “which subscription feels better on my phone,” run a week-long lived test in both apps. If your question is “which model ID should my agent call,” use this API page.

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What this means for routing

Opus 5 and GPT-5.5 are close enough on context and modalities that routing should be job-based, not brand-based. A practical default for many SaaS teams:

• Deep coding / long reasoning → Opus 5
• Tight prose / tool-heavy agents → GPT-5.5
• Publishable claims → second-model check either direction
• Cost-sensitive output volume at this tier → prefer Opus

For a fuller routing playbook, see AI model routing and the multi-model AI guide.

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Frequently asked questions

Which is better overall, Claude Opus 5 or GPT-5.5?
Neither permanently. Opus leads many deep-coding writeups and wins on output price. GPT often leads tool-orchestration signals and won our tighter-writing plus version-awareness prompts. Pick by job.

Which is better for coding?
Public SWE-bench Pro / deep-coding coverage often favors Opus 5. Terminal-Bench / tool loops in earlier GPT-5.5 vs Opus 4.x coverage often favor GPT. Our empty-list micro-test was a tie.

Which is better for writing?
Taste. In our rewrite, GPT stayed tighter; Opus sounded warmer. A/B on your brand voice.

Which is cheaper?
At published API rates (2026-08-21), input is $5/M for both; output is $25 (Opus) vs $30 (GPT-5.5). Cache reads match at $0.50/M. Opus is cheaper on output-heavy work.

Which has the larger context window?
GPT-5.5 (~1.05M) vs Claude Opus 5 (1M) in the API. Practically a near tie.

Do I need both?
If your week mixes deep repo work, tool agents, and customer email, yes. That is the multi-model thesis.

Are we comparing apps or API models?
This page uses API models Claude Opus 5 and GPT-5.5. Consumer apps may wrap different defaults or tools.

Why did Opus refuse the Opus 5 vs GPT-5.5 routing question?
In our 2026-08-21 run it did not treat those exact marketing names as known identities (self-description sounded Opus 4.5-era). GPT-5.5 produced a full matrix. Prompt by capabilities, or hard-code routes.

How often should I re-test?
After any major version bump. Monthly is sane for production teams.

Where can I run them side by side?
A multi-model workspace such as i10X workspace. Method guide: side-by-side AI comparison.

What about hallucinations and trust?
Both refused our false-premise and Atlantis traps. Still use source grounding and second-model checks for publishable claims. Multi-model hallucination checks.

Did model choice ever change outcomes in i10X research?
Yes, in hiring evals: up to a 42 percentage-point hire-rate gap for the same candidate depending on which AI wrote the resume ( ai-cv-bias). Different domain, same moral: which model you call is a product decision.

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