Verified October 2026

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GPT-5.6 Sol vs Claude Opus 5

A straight cost comparison of GPT-5.6 Sol (OpenAI) and Claude Opus 5 (Anthropic), per 1,000,000 tokens, verified October 2026.

$4GPT-5.6 Sol input / 1M
$5Claude Opus 5 input / 1M
20%Blended price gap
GPT-5.6 SolCheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-5.6 SolClaude Opus 5Winner
Input / 1M$4$5GPT-5.6 Sol
Cached input / 1M$0.40$0.50GPT-5.6 Sol
Cache write / 1M$5$6.25GPT-5.6 Sol
Output / 1M$20$25GPT-5.6 Sol
Context window1,050,0001,000,000GPT-5.6 Sol
Batch APIYesYesTie
Long-context tier> 272,000None—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
GPT-5.6 Sol$9Cheaper
Claude Opus 5$11.25+$2.25

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadGPT-5.6 SolClaude Opus 5Winner
Classification
100K reqs · 500 in / 10 out
$220$275GPT-5.6 Sol
Data extraction
10K reqs · 2K in / 200 out
$120$150GPT-5.6 Sol
Support chat
1K reqs · 4K in / 600 out
$28$35GPT-5.6 Sol
Code review
2K reqs · 8K in / 1.5K out
$124$155GPT-5.6 Sol
Summarisation
1K reqs · 20K in / 1K out
$100$125GPT-5.6 Sol
Agent loop
5K reqs · 12K in / 2K out
$440$550GPT-5.6 Sol
Translation
500 reqs · 15K in / 15K out
$180$225GPT-5.6 Sol
Long-document QA
200 reqs · 120K in / 800 out
$99.20$124GPT-5.6 Sol
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$58.21$72.76GPT-5.6 Sol
Nightly batch
10K reqs · 10K in / 2K out
$400$500GPT-5.6 Sol

GPT-5.6 Sol wins 10 of 10, Claude Opus 5 wins 0 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

GPT-5.6 Sol is cheaper on both sides of the mix: $4 vs $5 per million input tokens, and $20 vs $25 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthGPT-5.6 SolClaude Opus 5Difference
100$2.80$3.50GPT-5.6 Sol cheaper by $0.70
1,000$28$35GPT-5.6 Sol cheaper by $7
10,000$280$350GPT-5.6 Sol cheaper by $70
100,000$2,800$3,500GPT-5.6 Sol cheaper by $700

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptGPT-5.6 SolClaude Opus 5Winner
0%$28$35GPT-5.6 Sol
50%$20.81$26.01GPT-5.6 Sol
80%$16.49$20.62GPT-5.6 Sol
95%$14.34$17.92GPT-5.6 Sol

Both models publish a cached-read rate, so the discount is comparable: GPT-5.6 Sol reads cache at $0.40 per million, Claude Opus 5 at $0.50.

Which one should you pick?

GPT-5.6 Sol wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch, while Claude Opus 5 wins on none of the ten workloads above.

Beyond price, the two differ on input rate ($4 vs $5); output rate ($20 vs $25); cached-input rate ($0.40 vs $0.50); cache-write rate ($5 vs $6.25); context window (1,050,000 vs 1,000,000); long-context tier (yes, above 272,000 vs none).

Batch mode is available on GPT-5.6 Sol and Claude Opus 5, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-5.6 Sol cheaper than Claude Opus 5?

On a blended 1M-input-plus-250K-output workload, GPT-5.6 Sol is cheaper by 20% ($9 vs $11.25). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-5.6 Sol or Claude Opus 5?

GPT-5.6 Sol has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-5.6 Sol: rates rise once input passes it.

Do both models support prompt caching?

GPT-5.6 Sol bills cached reads at $0.40 per 1M; Claude Opus 5 at $0.50.

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