Comparison

Claude Sonnet 5 vs GPT 5.6 Sol: price, context, and the switching cost

Both bill at 0.1×. Sol costs 1.67× on input and 2× on output, and takes 372,000 tokens of context against Sonnet 5’s 1,000,000. They also speak two different request shapes.

Same multiplier, different sticker price

Both ids carry the 0.1× multiplier, so neither gets a discount the other does not. What differs is the official per-token rate the multiplier is applied to. Claude Sonnet 5 is $3 per million input tokens and $15 per million output; GPT 5.6 Sol is $5 and $30.

After the multiplier that works out to $0.30 and $1.50 per million on Sonnet 5, against $0.50 and $3.00 on Sol. Sol is 1.67× as expensive on input and 2× on output — the gap widens on the output side, which is the side that grows when a model explains itself at length.

Neither is a stripped-down id. Both list text, tools and thinking, and both cap output at 128,000 tokens per response. Whatever separates them, it is not a capability tier the catalog records.

The context gap is 2.7×

Sonnet 5 takes a 1,000,000-token context. Sol takes 372,000. That is roughly 2.7× more room on the Claude side, and it is the largest single difference between the two entries.

Room is not the same as value. Both are billed per token whether the window is nearly empty or nearly full, so the extra capacity only earns anything on work that genuinely does not fit in 372,000 tokens — a repository-wide read, a very long agent transcript, a document set you cannot chunk without losing the cross-references.

If your prompts sit comfortably under 372,000 tokens, the context column is not a reason to pick either one, and the price column is doing all the work. If they do not, the 372,000 ceiling is a hard stop rather than a slowdown, and no amount of price advantage on the other side changes that.

They are not drop-in replacements for each other

This is the part a price table hides. Sonnet 5 answers on /v1/messages with an x-api-key header and an anthropic-version header, and its body carries a messages array plus a required max_tokens. Sol answers on /v1/responses with an Authorization: Bearer header, and its body takes input.

So switching between them is not a one-field change the way switching between two Claude ids is. It is a different endpoint, a different auth header, a different request schema, and a different response schema for your parser to read. If your client already speaks one of these shapes, that is a real cost sitting on the other side of the comparison.

GPT 5.6 Terra is worth knowing about here: same /v1/responses shape, same 372,000-token context, same 0.1× multiplier, but $2 and $12 official — $0.20 and $1.20 after the multiplier. If your integration is already OpenAI-shaped, the cheaper option inside that shape may matter more than a cross-provider move.

The same question through both models — note that almost nothing about the two calls is shared.

# Claude Sonnet 5 — Anthropic shape
curl -sS https://token-share.app/v1/messages \
  -H "x-api-key: $TOKEN_SHARE_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{
    "model": "claude-sonnet-5",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Why does this test flake?"}]
  }'

# GPT 5.6 Sol — OpenAI Responses shape
curl -sS https://token-share.app/v1/responses \
  -H "Authorization: Bearer $TOKEN_SHARE_KEY" \
  -H "content-type: application/json" \
  -d '{
    "model": "gpt-5.6-sol",
    "input": "Why does this test flake?"
  }'

How to settle it on your own workload

Neither entry in the catalog contains the information you actually want, which is how each one behaves on your code. The numbers above bound the question — they tell you what a wrong guess costs — but they do not answer it.

The cheap version of the experiment: take twenty tasks you have already done and know the right answer to, run them through both ids, and compare the failures rather than the wins. Both cap at 128,000 output tokens, so a fair test means giving both the same max_tokens and the same prompt, not the same wall-clock budget.

Then price the result. Count the input and output tokens each run actually consumed and multiply by that model’s effective rate — $0.30/$1.50 for Sonnet 5, $0.50/$3.00 for Sol. A model that answers in half the output tokens can be the cheaper one to run even at 2× the output rate, and that only shows up when you measure it.

Common questions

Which one is cheaper per request?

Per token, Claude Sonnet 5 is: $0.30 and $1.50 per million input and output after the 0.1× multiplier, against $0.50 and $3.00 for GPT 5.6 Sol. Per request is a different question, because it depends on how many tokens each model spends answering you. Measure the token counts on your own prompts before assuming the per-token ordering carries over.

Can I send the same request body to both?

No. Sonnet 5 is served on /v1/messages, which expects a messages array and a required max_tokens, authenticated with x-api-key plus anthropic-version. Sol is served on /v1/responses, which expects input and authenticates with Authorization: Bearer. The bodies and the responses are both different shapes.

Does the 1M context mean Sonnet 5 remembers more?

It means it accepts more in one request — 1,000,000 tokens against Sol’s 372,000. Nothing carries between requests on either model, and both charge per token regardless of how full the window is, so filling the window is a cost decision, not a memory upgrade.