Claude Opus 5 vs GPT-5.6: pricing, long-context cost, and use cases (2026)
Claude Opus 5 and GPT-5.6 are the two frontier-tier models teams compare in August 2026 for the hardest reasoning and long-horizon agent work. This page previously compared their predecessors, Claude Opus 4.5 and GPT-5 — both have since been superseded, and GPT-5 is deprecated with a shutdown date of 11 December 2026. Below: list prices, the long-context surcharge that decides most real bills, and where each model wins.
Claude Opus 5 vs GPT-5.6 — at a glance
| Dimension | Claude Opus 5 | GPT-5.6 (sol) |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Input price (per 1M tok) | $5.00 | $5.00 |
| Output price (per 1M tok) | $25.00 | $30.00 |
| Long-context surcharge | None — 1M billed at the standard rate | 2x input, 1.5x output ($10 / $45) |
| Cached input (per 1M tok) | $0.50 (0.1x input) | $0.50 (0.1x input) |
| Max output tokens | 128K | Not published |
| Predecessor status | Opus 4.5 legacy, same $5 / $25 | GPT-5 deprecated, shuts down 2026-12-11 |
| Best for | Long-horizon agents, codebase reasoning, computer use | Math, deep reasoning, structured output |
Pick Claude Opus 5 or GPT-5.6?
When to choose Claude Opus 5
Choose Claude Opus 5 when the workload is a long-horizon agent that runs for hours, navigates large codebases, or depends on computer-use automation. Two pricing facts decide most real bills in its favour: the full 1M-token context is billed at the standard rate — a 900K-token request costs the same per token as a 9K one — and cache reads cost 0.1x input, so caching pays for itself after a single read. Its 128K max output also matters for generation-heavy work.
When to choose GPT-5.6
Choose GPT-5.6 when raw reasoning depth matters more than agent endurance — its extended thinking is the strongest on competition math and structured deep-reasoning work. Budget for the long-context tier: beyond the short-context threshold, sol moves from $5 / $30 to $10 / $45, so a long-document workload costs double on input and 1.5x on output. If your prompts are short, terra ($2 / $12) and luna ($0.20 / $1.20) are far cheaper members of the same generation — luna was repriced 80% down in the week to 2026-08-04.
Run Claude Opus 5 and GPT-5.6 side-by-side
VerticalAPI lets you switch between Claude Opus 5 and GPT-5.6 per-request through a single OpenAI-compatible endpoint. Same SDK, same API key, zero markup on tokens — you pay each provider directly under BYOK.
from openai import OpenAI client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...") # Claude Opus 5 resp_a = client.chat.completions.create( model="claude-opus-5", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "..."}, ) # GPT-5.6 — same SDK, different model + key resp_b = client.chat.completions.create( model="gpt-5.6-sol", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "..."}, )
VerticalAPI verdict
Use Claude Opus 5 for long-horizon coding agents, codebase-scale refactors, computer-use automation, and any workload with long prompts — the absence of a long-context surcharge is the single biggest cost difference between these two. Use GPT-5.6 for the hardest reasoning, math, and structured-output tasks, and consider terra or luna if your prompts are short. If you are still on GPT-5, plan the migration to GPT-5.6: it shuts down on 11 December 2026. Through VerticalAPI you can route between both via a single OpenAI-compatible endpoint and BYOK — switch with one model parameter.
Frequently asked questions
Is Claude Opus 5 or GPT-5.6 cheaper per token?
Input is identical at $5 per 1M. On output, Opus 5 is $25 against GPT-5.6 sol's $30, so Opus is about 17% cheaper. The gap widens sharply on long prompts: sol's long-context tier is $10 / $45 while Opus 5 bills its full 1M context at the standard rate, making Opus 50% cheaper on input and 44% cheaper on output for long-document work. Anthropic cache reads cost 0.1x input on both, so caching closes little of that gap. Figures verified on vendor pricing pages 2026-08-04.
Isn't Claude Opus $15 / $75?
No — that price belongs to Claude Opus 4.1, which was deprecated and retires on 2026-08-05. It is the only Claude ever priced at $15 / $75, and the figure is widely misattributed to current Opus models. Claude Opus 5, Opus 4.8, 4.7, 4.6 and 4.5 are all $5 / $25.
What happened to GPT-5?
GPT-5 (the gpt-5-2025-08-07 family, including mini, nano and pro) was deprecated on 2026-06-11 and shuts down on 11 December 2026. Its replacement is gpt-5.6-sol. GPT-5 remains purchasable and priced at $1.25 / $10 until shutdown, which makes it cheaper than its successor on paper — but it is on a countdown, so new integrations should target GPT-5.6.
Which is better for agentic coding?
Claude Opus 5 is the common default for long-horizon coding agents and multi-file refactors that span hours; GPT-5.6 is stronger on math-heavy and tightly-scoped reasoning. We deliberately publish no benchmark numbers for this pair: vendor-reported scores are not comparable across agent harnesses, and figures published for Opus 4.5 or GPT-5 do not transfer to their successors.
How do the context windows compare?
Claude Opus 5 ships a 1M-token context billed at the standard per-token rate — a 900K-token request costs the same per token as a 9K one. OpenAI does not state GPT-5.6's context window on its pricing page, and it prices long context at a higher tier ($10 / $45 for sol), so with OpenAI the question is not only whether the window fits but what the tokens cost once you use it.
Can I switch between Opus 5 and GPT-5.6 via one endpoint?
Yes. VerticalAPI exposes a single OpenAI-compatible endpoint at https://api.verticalapi.com/v1. Send <code>claude-opus-5</code> or <code>gpt-5.6-sol</code> in the model field with your own provider key — BYOK, zero markup on tokens, and you pay each vendor directly.
Limitations of this comparison
- Both are frontier-tier and overkill for most production traffic; mid-tier (Claude Sonnet 5, GPT-5.6 terra) is more cost-effective.
- We publish no latency figures for this pair. Our own measured latency data dates from April 2026 and covers models since superseded — see /benchmark/ for what is actually measured.
- We do not publish benchmark scores for this pair either: the vendors' own numbers are not comparable across harnesses, and figures for Opus 4.5 or GPT-5 do not transfer to their successors.
- OpenAI does not state the long-context threshold token count on its pricing page, so the exact point where sol doubles is unpublished.
- GPT-5.6 extended-thinking tokens are billed at the output rate; effective per-task cost can exceed the headline price.
- List prices exclude enterprise discounts and committed-use deals. Verified 2026-08-04; both vendors have repriced models in place within the last quarter.
What may change in 12-24 months
- Anthropic shipped the 1M-token context at standard pricing from Claude 4.6 onward — the thing frontier buyers were told to wait for is already here.
- OpenAI moved the other way, adding long-context tiers across the GPT-5.6 line (sol 2x input, 1.5x output), matching what Google and xAI already do.
- In-place repricing is now routine: gpt-5.6-luna dropped 80% and terra 20% in the week to 2026-08-04, under unchanged model names.
- Claude 4.7 onward uses a tokenizer that produces roughly 30% more tokens for the same text, so per-token prices understate real cost relative to earlier Claude generations.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- When is Claude Sonnet 5 a better choice than Opus 5?
- How do GPT-5.6 sol, terra and luna compare on cost-quality?
- What happened to GPT-5, and what should I migrate to before 11 December 2026?
- How much does the long-context tier actually add to a real GPT-5.6 bill?
- What is the cheapest way to A/B test Opus 5 and GPT-5.6 on the same traffic?
More head-to-head provider comparisons
Run a provider we have not measured? Any public endpoint is benchmarked and listed for free in the API directory. If you want yours measured now, on the record — time to first token, throughput under concurrency, cost per million tokens, function-calling reliability — see how a verified run works. The result is published exactly as it comes out.