Verified October 2026

Home › Compare › GPT-5.6 Terra vs Claude Sonnet 5

GPT-5.6 Terra vs Claude Sonnet 5

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

$2GPT-5.6 Terra input / 1M
$2Claude Sonnet 5 input / 1M
10%Blended price gap
Claude Sonnet 5Cheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-5.6 TerraClaude Sonnet 5Winner
Input / 1M$2$2Tie
Cached input / 1M$0.20$0.20Tie
Cache write / 1M$2.50$2.50Tie
Output / 1M$12$10Claude Sonnet 5
Context window1,050,0001,000,000GPT-5.6 Terra
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 Terra$5+$0.50
Claude Sonnet 5$4.50Cheaper

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 TerraClaude Sonnet 5Winner
Classification
100K reqs · 500 in / 10 out
$112$110Claude Sonnet 5
Data extraction
10K reqs · 2K in / 200 out
$64$60Claude Sonnet 5
Support chat
1K reqs · 4K in / 600 out
$15.20$14Claude Sonnet 5
Code review
2K reqs · 8K in / 1.5K out
$68$62Claude Sonnet 5
Summarisation
1K reqs · 20K in / 1K out
$52$50Claude Sonnet 5
Agent loop
5K reqs · 12K in / 2K out
$240$220Claude Sonnet 5
Translation
500 reqs · 15K in / 15K out
$105$90Claude Sonnet 5
Long-document QA
200 reqs · 120K in / 800 out
$49.92$49.60Claude Sonnet 5
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$31.10$29.10Claude Sonnet 5
Nightly batch
10K reqs · 10K in / 2K out
$220$200Claude Sonnet 5

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

Where the two bills cross over

The two are priced identically on one side of the token mix ($2 input, $12 output), so the crossover is decided entirely by the other side — the cheaper model on that side wins at every ratio.

Cost at four scales

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

Requests / monthGPT-5.6 TerraClaude Sonnet 5Difference
100$1.52$1.40Claude Sonnet 5 cheaper by $0.12
1,000$15.20$14Claude Sonnet 5 cheaper by $1.20
10,000$152$140Claude Sonnet 5 cheaper by $12
100,000$1,520$1,400Claude Sonnet 5 cheaper by $120

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 TerraClaude Sonnet 5Winner
0%$15.20$14Claude Sonnet 5
50%$11.60$10.40Claude Sonnet 5
80%$9.45$8.25Claude Sonnet 5
95%$8.37$7.17Claude Sonnet 5

Both models publish a cached-read rate, so the discount is comparable: GPT-5.6 Terra reads cache at $0.20 per million, Claude Sonnet 5 at $0.20.

Which one should you pick?

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

Beyond price, the two differ on output rate ($12 vs $10); 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 Terra and Claude Sonnet 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 Terra cheaper than Claude Sonnet 5?

On a blended 1M-input-plus-250K-output workload, Claude Sonnet 5 is cheaper by 10% ($4.50 vs $5). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-5.6 Terra or Claude Sonnet 5?

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

Do both models support prompt caching?

GPT-5.6 Terra bills cached reads at $0.20 per 1M; Claude Sonnet 5 at $0.20.

Related comparisons

Run your own token volumes