GPT-6 Sol vs Claude Opus 5.5: What Developers Should Compare
GPT-6 Sol and Claude Opus 5.5 launched on the same day with a striking price gap: Sol’s standard input and output rates are half of Opus 5.5’s. But token price alone cannot tell you which coding agent costs less per finished task. Here’s what official evidence shows, what it does not, and how Luna changes the budget equation.
GPT-6 Sol and Claude Opus 5.5 arrived on September 22, 2026, with different headline strengths for developers. Sol’s listed standard API rates are half of Opus 5.5’s per input and output token. Anthropic, meanwhile, presents Opus 5.5 as a more efficient frontier model, reporting strong agentic-coding results and lower cost than its own predecessor. The key buying question is not which launch sounded bigger. It is how much you pay for a coding agent to complete your actual work reliably.
The quick price comparison
These are Standard prices per million tokens; GPT-6 Sol’s short-context rates apply up to 272K input tokens.
| API token type | GPT-6 Sol | Claude Opus 5.5 |
|---|---|---|
| Uncached input | $2.00 | $4.00 |
| Cached input / cache read | $0.20 | $0.20 |
| Cache write | $2.50 | $5.00 |
| Output | $10.00 | $20.00 |
OpenAI’s GPT-6 release notes list Sol at $2 input, $0.20 cached input, and $10 output for prompts up to 272K input tokens. Anthropic’s Opus 5.5 announcement lists $4 input, $0.20 cache reads, $5 cache writes, and $20 output. So the simple uncached input/output comparison is clear: Sol costs half as much per token. Cached-input rates are equal; cache-write rates differ.
As a simple illustration, one million uncached input tokens plus one million output tokens costs about $12 with Sol and $24 with Opus 5.5, before tool fees, retries, or other processing charges. Real agent bills also reflect caching, reasoning tokens, repeated context, retries, and tools. The full OpenAI pricing table also raises GPT-6 Sol rates for prompts above 272K input tokens. Always estimate from the token mix and processing mode you expect to run.
A lower-cost option: GPT-6 Luna
Luna is not the direct competitor named in this comparison, but it matters to a developer choosing a cost strategy. Its Standard API price is $0.10 per million input tokens and $0.50 per million output tokens. That is one-twentieth of Sol’s listed rate and one-fortieth of Opus 5.5’s uncached input and output prices. Cached input is $0.01 per million tokens.
That price makes Luna a candidate for bounded, easy-to-check tasks: classifying issues, extracting fields, generating small test scaffolds, summarizing a known file, or transforming code under explicit rules. Use Sol or Opus 5.5 for tasks where the agent must interpret incomplete requirements, make interacting changes, or recover from failed tests. A hybrid setup can reserve the stronger model for planning and difficult decisions, while Luna handles routine subtasks. Validate the handoffs and total cost before rolling that policy out.
What the published coding evidence can tell us
Anthropic reports that Claude Opus 5.5 leads its tested models on agentic coding, computer use, and knowledge-work evaluations. Its results include 66.4% on Terminal-Bench 4.0 and 54.4% on FrontierCode v1.1. Those are vendor-published results, not an independent head-to-head test against GPT-6 Sol.
That distinction matters: Anthropic’s comparison table includes GPT-6 Astra and GPT-5.6 Sol, but does not include GPT-6 Sol. The table reports Opus 5.5 at 66.4% on Terminal-Bench 4.0 against Astra at 57.9% and GPT-5.6 Sol at 37.3%; on FrontierCode, Opus scores 54.4%, Astra 53.3%, and GPT-5.6 Sol 47.5%. These figures do not establish how GPT-6 Sol compares with Opus 5.5. The model generation differs, and benchmark conditions matter.
Anthropic notes that most Opus 5.5 results use adaptive thinking at max effort; Terminal-Bench reports Opus at xhigh and Astra at high. It also explains that production safeguards intervened on some tasks, and that those interventions may lower measured scores. Read the benchmark methodology and footnotes with the scores. OpenAI’s positioning for Sol is complex coding and agentic workflows, but its release notes do not provide a matching Sol-versus-Opus 5.5 benchmark. The honest conclusion is that the public evidence supports comparing list prices today, while the direct quality comparison remains open.
“40% lower cost” means lower than Opus 5
Anthropic says Opus 5.5 costs 40% less than Opus 5 on typical workloads at default settings, and generates output more than 30% faster. It attributes the saving to both lower token rates and fewer tokens per task. That is a comparison with Opus 5, not with GPT-6 Sol. It should not be presented as evidence that Opus 5.5 is 40% cheaper than Sol—or that Sol necessarily costs half as much per completed job.
The same distinction applies to any agent benchmark. A model that costs more per token may finish with fewer calls or less output; a lower-priced model may need more retries or developer supervision. Measure cost per accepted change, including model calls, tools, failed attempts, and review time. For coding, include test pass rate, regressions, time-to-completion, and how often a developer must intervene. Run both models through the same harness, repository, permissions, task prompt, and stopping criteria.
Launch attention is not a quality score
The two launches also drew visible community discussion. The public X post and Reddit threads cited for this comparison had substantial early engagement, and multiple r/codex discussions compared Sol, Luna, and Opus 5.5. These counts are snapshots: likes and votes change, platform search indexing may lag, and engagement measures attention rather than coding quality. The launch-day comments are useful for identifying questions developers care about—price, model availability, coding feel, and usage limits—but they are not a substitute for a controlled evaluation.
Which should you choose?
Choose GPT-6 Sol when your priority is lower API token cost for a general coding-agent workload and you can verify quality on your own tasks. Choose Claude Opus 5.5 when its published strengths, Claude tooling, or your own early tests justify the higher unit rate. Keep GPT-6 Luna for repetitive work with clear acceptance checks. For long tasks, compare both models on representative repositories and track total cost per successful result rather than relying on list price or launch benchmarks alone.
As of September 23, 2026, GPT-6 Sol’s and Claude Opus 5.5’s listed API prices make Sol the lower-cost choice per uncached token; the available official benchmark table does not settle a direct quality comparison between these two models. That is a reason to run a small, same-harness evaluation before committing a production workload.
