Verified October 2026

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GPT-6 Astra vs Claude Opus 5

A straight cost comparison of GPT-6 Astra (OpenAI) and Claude Opus 5 (Anthropic), per 1,000,000 tokens, verified October 2026.

$10GPT-6 Astra input / 1M
$5Claude Opus 5 input / 1M
50%Blended price gap
Claude Opus 5Cheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-6 AstraClaude Opus 5Winner
Input / 1M$10$5Claude Opus 5
Cached input / 1M$1$0.50Claude Opus 5
Cache write / 1M$12.50$6.25Claude Opus 5
Output / 1M$50$25Claude Opus 5
Context window1,050,0001,000,000GPT-6 Astra
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-6 Astra$22.50+$11.25
Claude Opus 5$11.25Cheaper

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-6 AstraClaude Opus 5Winner
Classification
100K reqs · 500 in / 10 out
$550$275Claude Opus 5
Data extraction
10K reqs · 2K in / 200 out
$300$150Claude Opus 5
Support chat
1K reqs · 4K in / 600 out
$70$35Claude Opus 5
Code review
2K reqs · 8K in / 1.5K out
$310$155Claude Opus 5
Summarisation
1K reqs · 20K in / 1K out
$250$125Claude Opus 5
Agent loop
5K reqs · 12K in / 2K out
$1,100$550Claude Opus 5
Translation
500 reqs · 15K in / 15K out
$450$225Claude Opus 5
Long-document QA
200 reqs · 120K in / 800 out
$248$124Claude Opus 5
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$146$72.76Claude Opus 5
Nightly batch
10K reqs · 10K in / 2K out
$1,000$500Claude Opus 5

GPT-6 Astra wins 0 of 10, Claude Opus 5 wins 10 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

Claude Opus 5 is cheaper on both sides of the mix: $5 vs $10 per million input tokens, and $25 vs $50 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-6 AstraClaude Opus 5Difference
100$7$3.50Claude Opus 5 cheaper by $3.50
1,000$70$35Claude Opus 5 cheaper by $35
10,000$700$350Claude Opus 5 cheaper by $350
100,000$7,000$3,500Claude Opus 5 cheaper by $3,500

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-6 AstraClaude Opus 5Winner
0%$70$35Claude Opus 5
50%$52.02$26.01Claude Opus 5
80%$41.24$20.62Claude Opus 5
95%$35.84$17.92Claude Opus 5

Both models publish a cached-read rate, so the discount is comparable: GPT-6 Astra reads cache at $1 per million, Claude Opus 5 at $0.50.

Which one should you pick?

GPT-6 Astra wins on none of the ten workloads above, while Claude Opus 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 input rate ($10 vs $5); output rate ($50 vs $25); cached-input rate ($1 vs $0.50); cache-write rate ($12.50 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-6 Astra 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-6 Astra cheaper than Claude Opus 5?

On a blended 1M-input-plus-250K-output workload, Claude Opus 5 is cheaper by 50% ($11.25 vs $22.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-6 Astra or Claude Opus 5?

GPT-6 Astra has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-6 Astra: rates rise once input passes it.

Do both models support prompt caching?

GPT-6 Astra bills cached reads at $1 per 1M; Claude Opus 5 at $0.50.

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