Claude Opus 5 vs GPT-5.6 Sol: the price ranking flips
This is the clearest example on the site of why list-price comparisons mislead. GPT-5.6 Sol carries a lower published list price than Claude Opus 5 But the two are discounted differently, and once both are priced at current gateway rates the cheaper model changes. Work through the table before you decide — comparing these two on vendor list prices alone leads to the opposite conclusion to the one these rates support.
Claude Opus 5 vs GPT-5.6 Sol: the numbers side by side
| Claude Opus 5 | GPT-5.6 Sol | |
|---|---|---|
| Gateway rate — input | $2.5 | $2.5 |
| Gateway rate — output | $12.5 | $15 |
| Official list — input | $5 | $4 |
| Official list — output | $25 | $20 |
| Blended at 1/3 output | $5.83 | $6.67 |
| Output : input price ratio | 5.0× | 6.0× |
USD per 1M tokens. Gateway rates checked 2026-11-01; official list prices as published by each vendor. Gateway rates move with upstream promotions, re-check before committing a budget.
What each costs per month
| Monthly tokens | Claude Opus 5 | GPT-5.6 Sol | Cheaper |
|---|---|---|---|
| 10M | $58.33 | $66.67 | Claude Opus 5 — 13% less |
| 100M | $583 | $667 | Claude Opus 5 — 13% less |
| 1000M | $5,833 | $6,667 | Claude Opus 5 — 13% less |
Assumes 1/3 of tokens are output and 2/3 input, roughly a chat or coding-agent mix. At a 100M tokens/month workload the gap is $83.33 a month, or $1,000 a year. Change the split in the calculator (output-heavy workloads widen the gap).
How much tokens each budget buys
| Budget | Claude Opus 5 | GPT-5.6 Sol | Extra tokens |
|---|---|---|---|
| $100 | 17M | 15M | Claude Opus 5 — 2M more |
| $1,000 | 171M | 150M | Claude Opus 5 — 21M more |
Tokens purchasable at the blended rate above. Output tokens cost more than input, so reading these figures as "all input" would overstate them.
Which one to pick
If you are choosing between two frontier models, price is usually not the deciding factor — quality on your workload is. Use the price data here for the opposite purpose: to confirm that the model you actually want is affordable at your volume, and to find the volume at which it stops being affordable.
- Compare on the ratio, not the rate. The output:input ratio row tells you how much more you pay for generated tokens. A model with a lower ratio is more forgiving of verbose output, long reasoning traces and code generation, the parts of an agent workload that blow up.
- Check the 1B row. Frontier models at a billion tokens a month is a real budget line. If it is, the discount structure matters more than the sticker price, and that is exactly what the flip above is about.
- Model both, ship one. Run the same eval set through both and compare quality per dollar rather than price per token. The calculator will give you the dollar side.
Where the answer flips: cost as the output share moves
Both prices above are blended at one third output. That assumption is doing more work than it looks like, because it is the only thing standing between you and the opposite answer. The table below holds volume constant and moves only the output share.
| Output share | Claude Opus 5 | GPT-5.6 Sol | Cheaper | Gap |
|---|---|---|---|---|
| 0% | $2.5 | $2.5 | GPT-5.6 Sol | $0 |
| 10% | $3.5 | $3.75 | Claude Opus 5 | $0.25 |
| 25% | $5 | $5.62 | Claude Opus 5 | $0.625 |
| 33% | $5.8 | $6.62 | Claude Opus 5 | $0.825 |
| 50% | $7.5 | $8.75 | Claude Opus 5 | $1.25 |
| 75% | $10 | $11.88 | Claude Opus 5 | $1.88 |
| 100% | $12.5 | $15 | Claude Opus 5 | $2.5 |
The two cross at roughly -0% output. Below that share GPT-5.6 Sol is cheaper; above it the other one is. If your real output share sits anywhere near -0%, a headline rate comparison is not a safe basis for this decision, measure your own split first.
Read the table as the reason not to trust a single number. Most published comparisons quote one blended figure, usually at an assumed ratio, and that figure is only valid at that ratio. The gap column is the one to watch: where it narrows towards zero, the decision should be made on something other than price.
What this comparison does not tell you
A price comparison answers one question and quietly implies several others it has not addressed. Before acting on the numbers above, these are the differences that a rate table cannot show:
- Cost per task, not cost per token. A model that needs half as many turns to finish the same job can be cheaper at a higher rate. The only way to know is to run one representative task on each and compare tokens consumed, not rates charged.
- Context window. A shorter window does not produce an error It produces truncation, which looks like a quality problem and gets misdiagnosed as one. Where your workload depends on long context, this outranks price entirely.
- Latency and throughput. Two models at similar rates can differ enough in response time to change whether a feature is usable. Measure it on your own payloads.
- Availability and rate limits. What you can actually sustain under concurrency is a property of the account and the route, not of the price.
- Capability differences. These pages compare price only. Where a capability difference is decisive, the vendor's own documentation is the source, we do not guess at it, and a cheaper model that cannot do the job is not cheaper.
The honest ordering: if the gap column above is small at your output share, stop optimising price and optimise for whichever of the five items above your workload actually depends on.
Switching between them
Both models are called through the same endpoint, so this is a model-ID change, not a migration. Setup references: OpenAI & Anthropic SDK · Claude Code · Cursor · reducing API cost.
Related comparisons: Claude vs GPT (mid-tier) · Opus 5 vs Sonnet 5 · Lowest-cost LLM APIs
FAQ
Is Claude Opus 5 or GPT-5.6 Sol cheaper?
At current gateway rates Claude Opus 5 is cheaper for a mixed workload — $5.83 versus $6.67 per 1M tokens blended at one third output. The answer depends on your input/output split: output tokens are priced several times higher than input on both models (5x and 6x respectively), so an output-heavy workload widens the gap.
How much does Claude Opus 5 cost per month?
At $5.83 per 1M blended tokens: about $58.33 for 10M tokens a month, $583 for 100M, and $5,833 for 1B. That is pure token spend There is no subscription on either model.
How much does GPT-5.6 Sol cost per month?
At $6.67 per 1M blended tokens: about $66.67 for 10M tokens a month, $667 for 100M, and $6,667 for 1B.
Can I switch between them without rewriting my code?
Both are reached through the same gateway endpoint, so switching is a model-ID change in the request rather than a new integration. See the SDK guide for the one-line change, or the Claude Code setup if you are switching inside a coding agent.
Which one should I use for long-context work?
That is a capability question rather than a pricing one, and it is not something a price index should answer for you. Check each vendor's own documentation for context limits, then price the context volume you actually expect, long-context workloads are input-dominated, so the input rate and the blended column are the rows that matter most.
Which is cheaper for a coding agent, Claude Opus 5 or GPT-5.6 Sol?
Agent workloads are input-heavy. Roughly 500M input to 100M output a month, because every turn resends the whole context. At that shape Claude Opus 5 bills about $2,500.00 and GPT-5.6 Sol about $2,750.00, so Claude Opus 5 comes out ahead. The same model wins at both shapes, so this particular choice does not hinge on how output-heavy your workload turns out to be.
What is the price difference between Claude Opus 5 and GPT-5.6 Sol per 1M tokens?
On input it is $2.5 against $2.5 per 1M; on output, $12.5 against $15. Blended at one third output the gap is $0.8333 per 1M in favour of Claude Opus 5. Output carries a multiple of the input rate on both models, so the gap widens or narrows with your own token split rather than staying fixed — price it in the calculator with your real ratio before committing.