{"id":497,"date":"2026-08-25T06:34:31","date_gmt":"2026-08-25T06:34:31","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=497"},"modified":"2026-08-25T06:34:42","modified_gmt":"2026-08-25T06:34:42","slug":"claude-opus-5-vs-gpt-5-6-sol","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/claude-opus-5-vs-gpt-5-6-sol","title":{"rendered":"Claude Opus 5 vs GPT-5.6 Sol: Benchmarks, Price & Which to Pick (2026)"},"content":{"rendered":"\n
\n\n

Comparison \u00b7 August 2026<\/p>\n\n

\nClaude Opus 5 (Anthropic API) and GPT-5.6 Sol (OpenAI API) are the two expensive-feeling flagships people still argue about in 2026, except the price gap is no longer close. This is a decision guide: published API rates, three workload cost scenarios, live writing and coding snippets, and a routing matrix. Keep both if the jobs split. Start in a\nmulti-model AI workspace<\/a>\nor on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Claude Opus 5 if:<\/strong> you want warmer narrative email, deeper bug writeups (cause, crash path, optional ValueError), and richer geology-style refusals when a premise is false.<\/p>\n

Pick GPT-5.6 Sol if:<\/strong> you want a much cheaper flagship ($2\/$10 vs $5\/$25), ~1.05M context, cheaper cache, and tighter send-ready prose that still keeps every fact.<\/p>\n

Best default for many teams:<\/strong> Sol as the volume flagship. Opus as the review\/critique model. Do not pay Opus rates for every chat turn.<\/p>\n

Data checked: 2026-08-24. Prices and model cards change. Verify live API test and vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1M<\/strong><\/p><\/td>\n

Claude Opus 5 context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

GPT-5.6 Sol context (API)<\/p><\/td>\n<\/tr>\n

$5 \/ $25<\/strong><\/p><\/td>\n

Claude Opus 5 input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n

$2 \/ $10<\/strong><\/p><\/td>\n

GPT-5.6 Sol input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, API cost efficiency, writing warmth vs tightness, coding explanation depth, cache-read price). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ marketer<\/p><\/td>\n

GPT-5.6 Sol for send-ready. Opus for warm narrative.<\/p><\/td>\n

Sol kept every fact in two tight paragraphs. Opus added \u201cI hope you are doing well\u201d and flexible meeting language. Our Opus capture also truncated before Acme pricing.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

Sol default on cost. Opus for deep review.<\/p><\/td>\n

Micro bug-fix was a tie on the guard. Opus wrote a fuller crash path. Sol is 2.5\u00d7 cheaper on output and 2.5\u00d7 cheaper on input.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Either on window. Sol on bill.<\/p><\/td>\n

1M vs 1.05M is a hair. Same text\/image\/file input set. Cost is not a hair.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume<\/p><\/td>\n

GPT-5.6 Sol<\/p><\/td>\n

Chat $0.007 vs $0.0175. Repo $0.20 vs $0.50. Cached agent $0.42 vs $1.05.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

Multi-model AI means using more than one LLM in your stack. This page compares Claude Opus 5 (Anthropic API) with GPT-5.6 Sol (OpenAI API), not Claude vs ChatGPT as consumer apps, and not the older GPT-5.5 ID. For that older pair see\nClaude Opus 5 vs GPT-5.5<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

Anthropic<\/p><\/td>\n

OpenAI<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

Claude Opus 5 (Anthropic API)<\/code><\/p><\/td>\n

GPT-5.6 Sol (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

Listed card name<\/p><\/td>\n

Claude Opus 5<\/p><\/td>\n

OpenAI: GPT-5.6 Sol<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Anthropic Opus flagship<\/p><\/td>\n

GPT-5.6 series flagship<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Also in Claude.ai \/ Anthropic apps. This article uses the API model.<\/p><\/td>\n

Also in ChatGPT-family apps. This article uses the API model.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If a page still prices \u201cOpus vs GPT\u201d from a 2025 screenshot, throw it out. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-08-24)<\/h2>\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,000,000 tokens<\/p><\/td>\n

1,050,000 tokens<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image, file<\/p><\/td>\n

text, image, file<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

No<\/p><\/td>\n

No<\/p><\/td>\n<\/tr>\n

Vendor positioning (card)<\/p><\/td>\n

Flagship for demanding reasoning, coding, long-horizon agents. Strong at end-to-end software, code review, bug finding, visual analysis.<\/p><\/td>\n

GPT-5.6 flagship for complex reasoning, coding, and agentic workflows. Particularly strong at command-line and multi-step coding.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, reasoning-effort, or realtime-search rows. Those fields were not on the cards we pulled. Confirm tool and effort switches on the live vendor pages if your agent depends on them.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

This pair is not a close cost race. Numbers use published per-million rates as of 2026-08-24<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$5.00<\/p><\/td>\n

$2.00<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$25.00<\/p><\/td>\n

$10.00<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.50<\/p><\/td>\n

$0.20<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Sol is cheaper on every sticker: input, output, and cache. Opus is the premium review model in this pairing, not the default meter.<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. Claude Opus 5<\/p><\/th>\n

Est. GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.0175<\/p><\/td>\n

$0.007<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.500<\/p><\/td>\n

$0.200<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$1.050<\/p><\/td>\n

$0.420<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-08-24). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

Opus costs about 2.5\u00d7 Sol on each stylized run. That is the whole plot for budget owners. You can still justify Opus on a hard review, a long-horizon agent that actually uses the extra depth, or a brand-voice pass. You cannot justify it as the only model in a high-volume loop unless quality on your eval set pays for itself. For subscription math across ChatGPT \/ Claude \/ Gemini plans, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

We are not inventing SWE-bench or intelligence-index numbers we do not have for GPT-5.6 Sol. Treat vendor cards as positioning. Treat our snippets as a small live pack. Re-run on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Anthropic\u2019s card pushes Opus on end-to-end software, code review, and bug finding. OpenAI\u2019s card pushes Sol on command-line and multi-step coding. In our micro-test, both named ZeroDivisionError<\/code> and guarded empty input. Opus explained the 0\/0 crash path and floated a ValueError alternative. Sol shipped a cleaner runnable snippet plus a print smoke test. Style split inside a tie, not a reason to pay 2.5\u00d7 on every call.<\/p>\n\n

Writing and tone<\/h3>\n

Opus sounded like a careful account manager: greeting energy, flexible Wednesday, \u201chappy to adjust.\u201d The capture cut off at \u201cthe competitive slide,\u201d so Acme pricing is not visible. Sol sounded like a tight PM: name slot, two paragraphs, Friday miss, Wednesday move, Acme, \u201cThanks!\u201d Warmth vs completeness.<\/p>\n\n

Research, math, reasoning<\/h3>\n

We did not run a science-QA pack, so we will not fake one. On the false-premise prompt, both refused the green-cheese Moon. Opus added Apollo and Luna sample geology (basalt maria, anorthosite highlands, dusty regolith, no biological organics). The capture then trails into \u201cBut if the\u2026\u201d Sol refused, then listed a practical lunar-resource plan (polar ice, split water, habitat protein, recycle). Opus wins depth on the refuse. Sol wins a usable redirect. For publishable work, still add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Both cards list text, image, and file in, text out. Context is a hair: 1M vs 1.05M. Neither card in this pull lists audio or video. If you need those, see Gemini 3.1 Pro:\nClaude Opus 5 vs Gemini 3.1 Pro<\/a>\nand\nGPT-5.6 Sol vs Gemini 3.1 Pro<\/a>.<\/p>\n\n

Speed<\/h3>\n

No latency numbers in this pack. Measure p50 from your region. Do not ship on a third-party tokens-per-second screenshot.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ agents<\/p><\/td>\n

Split (Opus depth, Sol default)<\/p><\/td>\n

Micro-test tie. Opus fuller review writeup. Sol cheaper to loop.<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

Split (taste)<\/p><\/td>\n

Opus warmer. Sol tighter and complete in our capture.<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

Near tie<\/p><\/td>\n

1M vs 1.05M. Same modality set on the cards.<\/p><\/td>\n<\/tr>\n

Realtime \/ conversational<\/p><\/td>\n

Product-dependent<\/p><\/td>\n

Claude.ai vs ChatGPT apps wrap different tools. Not measured here.<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

GPT-5.6 Sol<\/p><\/td>\n

Not close. ~2.5\u00d7 cheaper on our three recipes.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read benchmarks<\/strong>\n

When we lack a primary public table for an ID, we do not invent one. Card positioning plus a live pack is the honest floor. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-08-24)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, false premise only. No invented fourth and fifth scores.<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

Claude Opus 5 (excerpt):<\/strong> \u201cI hope you are doing well.\u201d Q3 deck from last Tuesday. Finance promised Friday, nothing arrived. Ask to move stakeholders to next week, Wednesday preferred, happy to adjust. Then \u201cOne more thing: the competitive slide\u2026\u201d Capture truncated there, so Acme pricing is not visible in the snippet we scored.<\/p>\n

GPT-5.6 Sol (excerpt, sanitized):<\/strong> Name slot. Two paragraphs. Finance numbers still out after Friday. Move to next Wednesday if it works. Update competitive slide with new Acme pricing. \u201cThanks!\u201d<\/p>\n

Edge:<\/strong> Sol on completeness and tightness in the capture. Opus on warmth and scheduling flexibility. Deduct for the truncated Acme line on Opus. If your live Opus run includes that line, the writing gap shrinks.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Both named ZeroDivisionError<\/code>. Both guard with an empty check before dividing. Opus wrote a longer \u201cwhy it crashes\u201d note and mentioned raising ValueError as an alternative to returning 0. Sol wrote a shorter explanation and a runnable snippet with print(average([]))<\/code>. Tie<\/strong> on correctness. Opus for the review comment. Sol for the paste-and-run patch.<\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

Both refused first. Opus (sanitized): folk joke, not a fact. Apollo and Luna samples. Basalt, anorthosite, impact-shattered regolith. No cheese, no protein, essentially no biological organics. Capture cuts at a \u201cBut if the\u2026\u201d redirect. Sol: silicate rock and metal, no mineable protein, then four practical resource steps. Both pass.<\/strong> Opus more geological. Sol more operational after the refuse.<\/p>\n\n\n\n\n\n\n\n\n

Prompt type<\/p><\/th>\n

Claude Opus 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Client email rewrite<\/p><\/td>\n

22\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Sol complete. Opus warmer, truncated before Acme.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

24\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Both catch ZeroDivisionError. Tie.<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

24\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Both refuse. Opus more geology. Sol more operational redirect.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

70\/75<\/strong><\/p><\/td>\n

71\/75<\/strong><\/p><\/td>\n

Essentially a tie on quality. Not a tie on cost.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A one-point editorial gap does not buy a 2.5\u00d7 invoice. Use Opus where the extra review depth shows up in your evals. Use Sol as the default meter.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n