Verified October 2026

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GPT-6.1 Sol vs Claude Fable 5.1

A straight cost comparison of GPT-6.1 Sol (OpenAI) and Claude Fable 5.1 (Anthropic), per 1,000,000 tokens, verified October 2026.

$2GPT-6.1 Sol input / 1M
$10Claude Fable 5.1 input / 1M
80%Blended price gap
GPT-6.1 SolCheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-6.1 SolClaude Fable 5.1Winner
Input / 1M$2$10GPT-6.1 Sol
Cached input / 1M$0.10$0.25GPT-6.1 Sol
Cache write / 1M$2.50$12.50GPT-6.1 Sol
Output / 1M$10$50GPT-6.1 Sol
Context window1,050,0001,000,000GPT-6.1 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-6.1 Sol$4.50Cheaper
Claude Fable 5.1$22.50+$18

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-6.1 SolClaude Fable 5.1Winner
Classification
100K reqs · 500 in / 10 out
$110$550GPT-6.1 Sol
Data extraction
10K reqs · 2K in / 200 out
$60$300GPT-6.1 Sol
Support chat
1K reqs · 4K in / 600 out
$14$70GPT-6.1 Sol
Code review
2K reqs · 8K in / 1.5K out
$62$310GPT-6.1 Sol
Summarisation
1K reqs · 20K in / 1K out
$50$250GPT-6.1 Sol
Agent loop
5K reqs · 12K in / 2K out
$220$1,100GPT-6.1 Sol
Translation
500 reqs · 15K in / 15K out
$90$450GPT-6.1 Sol
Long-document QA
200 reqs · 120K in / 800 out
$49.60$248GPT-6.1 Sol
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$24.61$112GPT-6.1 Sol
Nightly batch
10K reqs · 10K in / 2K out
$200$1,000GPT-6.1 Sol

GPT-6.1 Sol wins 10 of 10, Claude Fable 5.1 wins 0 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

GPT-6.1 Sol is cheaper on both sides of the mix: $2 vs $10 per million input tokens, and $10 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-6.1 SolClaude Fable 5.1Difference
100$1.40$7GPT-6.1 Sol cheaper by $5.60
1,000$14$70GPT-6.1 Sol cheaper by $56
10,000$140$700GPT-6.1 Sol cheaper by $560
100,000$1,400$7,000GPT-6.1 Sol cheaper by $5,600

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-6.1 SolClaude Fable 5.1Winner
0%$14$70GPT-6.1 Sol
50%$10.20$50.52GPT-6.1 Sol
80%$7.93$38.84GPT-6.1 Sol
95%$6.79$33GPT-6.1 Sol

Both models publish a cached-read rate, so the discount is comparable: GPT-6.1 Sol reads cache at $0.10 per million, Claude Fable 5.1 at $0.25.

Which one should you pick?

GPT-6.1 Sol wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch, while Claude Fable 5.1 wins on none of the ten workloads above.

Beyond price, the two differ on input rate ($2 vs $10); output rate ($10 vs $50); cached-input rate ($0.10 vs $0.25); cache-write rate ($2.50 vs $12.50); context window (1,050,000 vs 1,000,000); long-context tier (yes, above 272,000 vs none).

Batch mode is available on GPT-6.1 Sol and Claude Fable 5.1, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-6.1 Sol cheaper than Claude Fable 5.1?

On a blended 1M-input-plus-250K-output workload, GPT-6.1 Sol is cheaper by 80% ($4.50 vs $22.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-6.1 Sol or Claude Fable 5.1?

GPT-6.1 Sol has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-6.1 Sol: rates rise once input passes it.

Do both models support prompt caching?

GPT-6.1 Sol bills cached reads at $0.10 per 1M; Claude Fable 5.1 at $0.25.

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