Claude Sonnet 5.5 vs GPT-6.1 Sol: Which Is Actually Cheaper for Your Workload

Prices updated November 6, 2026.

The comparison most articles make is a one-liner: put the two per-million-token prices side by side and pick the lower one. For the current mid tier that one-liner does not even produce an answer, because GPT-6.1 Sol and Claude Sonnet 5.5 both list at $2/$10 per million tokens (input/output). Even when the list prices differ, the framing is wrong in practice because it assumes you are sending random tokens with no structure. Real workloads have a ratio of input to output tokens, a portion of stable context that can be cached, and sometimes a latency tolerance that unlocks batch pricing. Each of those dimensions can cut effective cost by half or more, and they interact.

The prices cited here are as of November 6, 2026. Verify current rates at OpenAI pricing and Anthropic pricing before making budget decisions. Both pages also list Batch rates.

This post originally compared GPT-4o with Claude Sonnet. GPT-4o is now a legacy model: OpenAI deprecated it on October 14, 2026 and it shuts down on January 14, 2027. It is still listed at $2.50/$10, and it appears in the table below as a reference point.

GPT-6.1 Sol pricing in practice

GPT-6.1 Sol is OpenAI's mid-tier model ("near-Astra performance", in OpenAI's words). It charges $2 per million input tokens and $10 per million output tokens, a 5:1 ratio. Prompt caching is on by default for the longest repeated prefix of your request, and cached input tokens on Sol cost $0.10 per million, a 95% discount. The batch API (async, up to 24-hour turnaround) cuts input and output prices by 50%.

The caching rules changed with GPT-5.6. Cache writes are no longer free: on GPT-5.6 and later, writing the cache costs 1.25x the input rate for the default 30-minute TTL ($2.50 per million on Sol) or 2.5x for the 12-hour TTL ($5.00 per million, Responses API only). The cached prefix must be at least 1,024 tokens, so short system prompts do not qualify. You can also switch GPT-5.6+ models to explicit-only cache breakpoints if you want control over what gets cached. Prompts over 272K tokens are billed at a long-context rate of $4 input, $0.20 cached and $15 output.

OpenAI is privately held with a deep partnership with Microsoft, which holds a significant equity stake and sells OpenAI models through Azure OpenAI. If your organization has policies around vendor concentration or cloud provider dependency, this is worth noting. GPT-6 Luna ($0.10/$0.50 per million tokens) is OpenAI's current small model if output quality is acceptable for the task.

Claude pricing in practice

Claude Sonnet 5.5 charges $2 per million input tokens and $10 per million output tokens, the same 5:1 ratio. Prompt caching works two ways: you can mark cache breakpoints in your requests using cache_control: {"type": "ephemeral"}, or use Anthropic's newer automatic caching through a single top-level cache_control field. Cache writes cost $2.50 per million tokens for the 5-minute TTL (a 25% premium on regular input) or $4 per million for the 1-hour TTL, and cache reads cost $0.10 per million tokens, a 95% discount versus the list rate.

That asymmetry is still the crux of the Claude cost model, but it no longer separates Claude from OpenAI. Writing the cache costs slightly more; reading it is dramatically cheaper. On Sonnet 5.5 the read price is the same $0.10 per million that OpenAI charges on Sol, so a long system prompt that is identical across thousands of daily requests costs the same to serve from cache on either model.

The limitation is operational: the default cache TTL is 5 minutes, against OpenAI's 30. If your system prompt changes frequently, or if your application does not hold enough volume to hit the cache again within the TTL, you keep paying the write premium and caching can slightly increase your bill rather than reduce it. The 1-hour TTL fixes this for bursty traffic at the price of a 2x write. Anthropic has Amazon and Google as significant investors; Claude is available through Amazon Bedrock, through Claude Platform on AWS and in Microsoft Foundry, the last two billed at the same token rates as the Claude API.

One more variable: Anthropic says the tokenizer in Claude 4.7 and later "produces approximately 30% more tokens for the same text" than earlier Claude models. Identical per-token prices do not guarantee identical per-request bills, so count tokens for your own prompts on both models before you compare.

The workload scenarios that change the math

Three representative workload shapes cover most production use cases. All numbers below use the November 2026 prices stated above, assume the same token count on both models, and assume a warm cache (reads only) for scenarios with caching applied.

Scenario 1: Output-heavy (1K input / 3K output per call) Typical for chatbots generating long responses, code generation, and long-form drafting. The 75% output share means the output price dominates.

Scenario 2: Cache-heavy (5K cached system prompt + 200 token query + 500 token output) Typical for agent loops with a large, stable instruction set, or any app where the same lengthy context appears in every request.

Scenario 3: Extraction with stable context (8K cached document + 2K query + 100 token output) Typical for document Q&A, classification pipelines, and structured data extraction where most of the input is reusable context.

How the cached cache-heavy figure works out on either model: 5,000 × $0.10/1M = $0.0005, plus 200 × $2/1M = $0.0004, plus 500 × $10/1M = $0.0050, for $0.0059 per call.

Reading the table: at equal token counts the two mid-tier models cost exactly the same in every scenario, cached or not. Output-heavy work gets no help from caching on either side, because the output share dominates. For cache-heavy and extraction workloads, caching cuts the per-call cost by 62% and 72% respectively on both models. The legacy GPT-4o column shows why the old comparison came out differently: its cache discount was only 50%.

There is no list-price crossover between these two models anymore. What decides the bill is how many tokens each model actually produces for your prompts, and which cache TTL fits your traffic: OpenAI's 30-minute default suits steady traffic with short gaps, while bursty traffic with gaps longer than 5 minutes needs Anthropic's 1-hour TTL (2x write) or OpenAI's 12-hour TTL (2.5x write).

Batch pricing applies on top of these figures: both providers offer 50% off for async batch jobs, and on both the discount also applies to cached input. If your workload tolerates a 24-hour SLA, run a batch and apply the caching discount on top of that. The combined effect brings the extraction scenario to about $0.0029 per call on either model.

The enforcement gap

Both providers offer dashboards showing spend after the fact. OpenAI's usage limits and Anthropic's API quotas are account-level controls set in advance, not per-user or per-project controls that adapt to real-time budget state. Neither provider can tell your application "this specific end user has spent $4.80 of their $5 monthly budget and the next call would exceed it" before the call fires.

This matters most in multi-tenant SaaS products, where cost attribution is per customer, not per account. If user A runs an unusually expensive session with a long context and high output volume, the cost lands on your API bill whether you intended to allow it or not. Post-hoc logging tells you what happened; it does not stop it.

The distinction is not about observability. Tools like Langfuse (now part of ClickHouse), LangSmith, and Helicone (in maintenance mode since Mintlify acquired it in March 2026) all log what was spent after the fact, which is useful for analysis. Pre-flight enforcement is a different mechanism: it estimates token cost client-side before the API call is made and blocks the request if the user or project is over budget.

FAQ

Does prompt caching work automatically with Claude? It can now. Anthropic supports automatic caching through a single top-level cache_control field, as well as explicit cache_control breakpoints in the messages or system prompt. OpenAI's caching is on by default for supported models. On Sonnet 5.5 and GPT-6.1 Sol the cache-read discount is the same 95%, and both charge 1.25x input for a default-TTL cache write.

Which model is better for RAG applications? On price alone, neither. Where retrieved context is large and reused across many queries, both models bill cached reads at $0.10 per million tokens, against $2 uncached. The practical difference is cache lifetime: OpenAI's default 30-minute TTL survives longer gaps between queries than Anthropic's 5-minute default. If your retrieved context changes with every query (no stable prefix), caching does nothing on either side and the identical $2/$10 list prices apply.

Does switching models mid-project make sense based on cost alone? Rarely, and between these two models almost never: at list price the scenarios above come out identical. Any real difference comes from token counts per request, which you have to measure. Switching models requires re-testing output quality, adjusting prompts (the models have different instruction-following behavior and formatting tendencies), and potentially rewriting integration code. The engineering cost of a migration usually exceeds the pricing delta for anything below $5,000/month in API spend.

How does batch pricing interact with context caching? On both providers, batch discounts and caching discounts stack. A batch request reading from a warm cache pays 50% off the cache-read price, bringing Claude Sonnet 5.5 cache reads down to $0.05 per million tokens. OpenAI's 50% batch discount applies to every Standard price including cached input, so GPT-6.1 Sol cached input in a batch is also $0.05 per million tokens.

Is GPT-4o mini or Claude Haiku a better drop-in for cost reduction? GPT-4o mini ($0.15/$0.60 per million) and Claude Haiku 4.5 ($1/$5 per million) sit in different price tiers. GPT-4o mini is substantially cheaper at list rate. The same caching logic applies: Claude Haiku's cache-read discount still gives it an edge for high-cache-ratio workloads, but at these price points the absolute dollar differences are small. The decision should be driven by output quality for your specific task.

Where noburn fits in this stack

Neither OpenAI nor Anthropic provides per-user budget enforcement before an API call fires. noburn runs a pre-flight check from your own code before the call to the model provider (it is not a proxy): it prices the call from its token estimate, checks it against the user's or project's remaining budget, and blocks the request if the limit would be exceeded, before any tokens reach the API. This works with both GPT-4o and Claude: the check goes in front of your existing OpenAI or Anthropic SDK call, whichever framework makes it. For multi-tenant SaaS products where you need to enforce per-customer spend limits and optionally pass usage costs through to Stripe billing, noburn handles both the enforcement and the billing side without requiring a separate metering service. The Free plan covers 10,000 recorded calls a month (current plans: pricing). Documentation and SDKs are at noburn.dev/docs.