Start with Sol for budget. Evaluate Opus for harder work.
Sol’s lower token rates make it a useful baseline for repeatable work. Opus leads the independent composite at its highest setting in the snapshot below. The practical question is whether that extra quality saves enough failed attempts or review time to justify the bill.
Make acceptance measurable
For extraction, fixes or structured output, write a pass condition before choosing a model. Spend less when both models clear it reliably.
Count the review burden
A convincing answer can still be wrong. Track missed requirements and time spent reviewing, alongside API cost.
Compare configurations, not just model names.
| Metric | Sol · Max | Opus 5.5 · Max, Default Fallback |
|---|---|---|
| Intelligence Index ↑ | 52 | 58 |
| Terminal-Bench 4.0 ↑ | 56.1% | 59.6% |
| AutomationBench-AA ↑ | 64.9% | 69.5% |
| Weighted Index cost / task ↓ | $0.72 | $5.98 |
| Output speed after first chunk ↑ | 49 tokens/s | 96 tokens/s |
Independent measurements, not VSModels results. Max is not a matched compute budget across vendors. Opus includes its default fallback configuration. Output speed excludes waiting before generation; it is not end-to-end completion time.
The same release table gives both Low configurations an Index score of 42, with weighted task costs of $0.13 for Sol and $0.55 for Opus. That does not make their answers interchangeable. It shows why a “best model” claim without an effort setting is incomplete.
Token rates are the starting point.
| Token category | GPT‑6.1 Sol | Opus 5.5 |
|---|---|---|
| Uncached input | $2 | $4 |
| Cached reads | $0.10 | $0.20 |
| Cache writes | $2.50 | $5 |
| Billable output | $10 | $20 |
For an illustrative 10,000 uncached input tokens and 2,000 billable output tokens, one attempt costs $0.04 on Sol and $0.08 on Opus. If Sol needs two attempts and Opus one, that simple API advantage disappears. These are calculated examples, not measured success rates.
OpenAI applies higher rates beyond its long-input threshold; faster processing and tools can add costs. Compare your actual usage records, including reasoning, rather than counting only the answer visible on screen.
Budget for outputs you can accept.
Change each side’s tokens and estimated pass rate. Cost per accepted output is attempt cost divided by the acceptance fraction.
Standard uncached text rates, up to 272,000 input tokens per attempt. No cache, long-context premium, tools, images, faster processing, tax or gateway markup included. Attempts are assumed billed; acceptance does not imply free retries. No provider request is made.
Use a small test that resembles your work.
- Freeze a brief, repository snapshot, tools and pass conditions. Specify the exact model IDs and effort settings.
- Include a routine task, a multi-file fix and a task with missing information. Keep failed outputs.
- Score correctness before seeing the model label. Record review time, wall time, token usage and every retry.
- Keep the lower-cost configuration if it meets your threshold. Escalate the cases that fail rather than raising effort for every task.
For a broader frontier-model comparison, continue with Argon vs Opus. For the OpenAI-only tradeoff, read Sol vs Astra.
Common questions
Is GPT‑6.1 Sol the same as GPT‑6 Sol?
No. Use the explicit gpt-6.1-sol model ID when evaluating 6.1. Results for the older GPT‑6 Sol should not be silently reused.
Does a higher benchmark score mean Opus is always better?
No. The comparison depends on effort, fallback behavior, task mix, latency and what you count as an accepted result. Evaluate the setting you can afford to run.
Does half the token price mean half the task bill?
Only with equal billable usage at the same price schedule. Reasoning, retries, cache behavior and tool calls can change the relationship.
Sources, method and access
Prices and specifications are a dated documentation snapshot. Calculators run in your browser and estimate supplier API costs; they do not call a model or quote VSModels credits. Vendor claims and independent evaluations are labeled separately. VSModels has not run a paid head-to-head test for this guide and does not offer live calls to the models covered here.
- OpenAI · GPT‑6.1 Sol model and Standard prices
- Anthropic · Opus 5.5 release and API pricing
- Artificial Analysis · Sol and Opus release comparison
Found a changed rate or a reproducible result? Send the endpoint, settings, date and original source to support@vsmodels.app.