The rate cards side by side
| GPT-6.1 Sol | Claude Opus 5.5 | |
|---|---|---|
| Input / 1M | $2.00 | $4.00 |
| Output / 1M | $10.00 | $20.00 |
| Cached input / 1M | $0.10 | $0.20 |
| Batch / 1M | $1.00 / $5.00 | Batch advertised at 50% savings on Sonnet/Haiku pages; no Opus batch rate card |
| Fast mode / 1M | $4.00 / $20.00 | No fast tier listed in Anthropic's pricing documentation (checked Oct 28, 2026) |
| Long-context band | $4/$15 past 272K tokens (whole request) | Flat $4/$20 across the full 1M window — no band published |
| Released | September 2026 | September 22, 2026 |
Sol's rates are verified on OpenAI's pricing page; Opus 5.5's $4/$20 list price and $0.20 cache reads are verified on Anthropic's own product page. The headline: Sol costs exactly half on both input and output. Cache reads differ — Sol at $0.10/1M against Opus at $0.20/1M — so heavily cached workloads widen Sol's advantage instead of narrowing it.
Cost per task: the gap in dollars
GPT-6.1 Sol: 100 × $2 + 20 × $10 = $400
Claude Opus 5.5: 100 × $4 + 20 × $20 = $800
With 80% cache-hit rate on input:
GPT-6.1 Sol: 100 × (0.2 × $2 + 0.8 × $0.10) + $200 = $248
Claude Opus 5.5: 100 × (0.2 × $4 + 0.8 × $0.20) + $400 = $496
Opus 5.5 stays roughly 2× Sol at every caching level — and caching widens the gap slightly, because Sol's cache reads cost half of Opus's. The gap only closes if Opus's higher quality reduces the number of calls — fewer retries, less repair work, shorter outputs.
What we no longer claim: the eval battery
An earlier edition of this page reported an in-house 4-experiment evaluation battery (n=1 per experiment) in which Claude Opus 5.5 went 4–0. We have withdrawn that claim. When this page was re-verified in October 2026, the battery's run records could not be produced — and the surviving copies of the claim contradicted each other about which models were even in the battery. An unverifiable result does not ship. JetAI publishes no in-house measured quality comparison for these models: we compare them on verified price, and you should test both on your own workload.
Which to pick
- Pick GPT-6.1 Sol for cost-sensitive production at scale, batchable workloads ($1/$5 is the cheapest capable interactive-adjacent rate in the Western lineup), and when your tasks are routine enough that model quality differences are small.
- Pick Claude Opus 5.5 when quality per attempt matters more than price per token — agentic coding, complex reasoning, and anywhere first-attempt success and its token efficiency make the agent economics work.
- Pick both in a router: Sol for the bulk, Opus 5.5 for escalation. The 2× price gap makes the router architecture pay for itself quickly.
Two levers change the math: prompt caching discounts repeated input and the Batch API halves rates. Model either workload with the bill estimator.
Quick answers
Is GPT-6.1 Sol cheaper than Claude Opus 5.5?
Yes — exactly half on list rates. Verified October 2026: GPT-6.1 Sol is $2/$10 per million tokens against Claude Opus 5.5's $4/$20. Cache reads differ too — Sol $0.10/1M versus Opus $0.20/1M — so heavily cached workloads widen Sol's advantage instead of narrowing it.
Which is better, GPT-6.1 Sol or Claude Opus 5.5?
On verified price, Sol wins. On quality, we publish no in-house measured comparison: an earlier eval-battery claim was withdrawn because its run records could not be produced. Test both models on your own workload and compare cost per completed task, not cost per token — that is the honest decider.
Do GPT-6.1 Sol and Claude Opus 5.5 offer batch discounts?
Yes, both. GPT-6.1 Sol has a verified 50% Batch API tier ($1/$5 per million tokens). Anthropic likewise offers batch processing at 50% cost savings — stated on its Sonnet and Haiku product pages — so asynchronous workloads halve on both sides of this comparison, subject to each tier's eligibility.
When does Claude Opus 5.5 justify costing twice as much?
When its higher quality per attempt reduces total attempts: fewer retries, less human repair, shorter outputs. If Opus completes in one call what takes Sol two or three attempts, the 2x token price can still mean a lower total bill. That is why cost per completed task beats cost per token.